Upcoming Apache Ignite Events

Data Streaming Using Apache Flink and Apache Ignite

Join Saikat Maitra to learn how to build a simple data streaming application using Apache Flink and Apache Ignite. This stream processing topology will allow data streaming in a distributed, scalable, and fault-tolerant manner, which can process data sets consisting of virtually unlimited streams of events.

In-Memory Computing Essentials for Software Engineers

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that are proven to boost application performance and solve scalability problems by storing and processing massive data sets in RAM and on disk.

How and why Apache Ignite^® is changing from an In-Memory Data Grid into an In-Memory Database

Our upcoming event “Open Source Database Best Practices” focuses on the latest developments in open source database technology. You will hear about ProxySQL, ClickHouse and Apache Ignite from speakers who are actively engaged in creating these technologies. The speakers work for ProxySQL, Altinity, and GridGain Systems and are all active committers to their respective projects.

How to Migrate Your Data Schema to Apache Ignite

In this webinar, we will discuss the pros and cons of SQL and JCache APIs approaches to work with your data in Apache Ignite. Talk includes Ignite SQL capabilities, consistency guarantees, complications of distributed SQL and how affinity co-location can minimize them.

Apache Ignite Moscow Meetup

Andrei Gora, Apache Ignite PMC, will talk about typical problems that arise when moving to a distributed model of data storage and processing, namely, the organization of consistent data placement in a distributed system. Denis Garus (Sbertech) will present Ignite Sandbox and tell about data protection in a distributed Java environment. After the talks, we plan to discuss what does community expect from Ignite 3.

Ignite Pearls - Insight and Creativity in Distributed Programming

This presentation is a live Scala coding talk full of small case studies, real examples, and interesting exercises for learning about how to do a modern distributed programming with Apache Ignite.

Apache Ignite Pumpkin Meetup

We are going to have a cozy Halloween talk about “how to use” (first speaker) and “how does it work” (second speaker). As usual, there will be tea and coffee, a snack and a raffle of useful books. Evgeny Zhuravlev will present talk “Architecture and optimization of working with memory for processing big data”. Apache Ignite Committer Maxim Muzafarov will tell about his experience of working with Rebalance feature.

In-Memory Computing Essentials for Software Engineers

Free books+swag, caviar, liquors, cheeses & other eclectic delicacies! Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that are proven to boost application performance and solve scalability problems by storing and processing massive data sets in RAM and on disk.

Ensembles of ML algorithms and Distributed Online Machine Learning with Apache Ignite

Alexey Zinovyev, Apache^® Ignite® Committer, will tell about Apache Ignite ML module and how can it speedup your ML training. Also He will speak about using Ignite as a backend for distributed TensorFlow calculations.

Updated Apache Ignite Apache® Ignite® Web Console live demo

GridGain Web Console is now 100% free for Apache® Ignite™ and GridGain® users. During the session we will cover the basics of installation and discuss new features and capabilities added to the most recent release as well as architectural changes.

Why did we developed another one Kafka connector

In his talk Alexey will tell, when the connectors are more convenient than developing from scratch, briefly will go through the existing connectors. After that Alexey will tell how he wrote his version based on Apache Ignite and certified by Confluent.

Data Distribution in Apache Ignite

Andrei Gora, committer and PMC of the Apache Ignite project, will talk about typical problems that arise when moving to a distributed model of data storage and processing, namely, the organization of a consistent data placement in a distributed system. Special attention will be paid to the rendezvous hashing algorithm, the features of its use in Apache Ignite (Rendezvous affinity function) and possible alternatives.

Moving Apache® Ignite® into Production: Best Practices For Disaster Recovery and High Availability

Learn some of the best practices and the different options for maximizing availability and preventing data loss. This session explains in detail the various challenges including cluster and data center failures, and the best practices for implementing disaster recovery (DR) for distributed in-memory computing based on real-world deployments.

Continuous Machine and Deep Learning at Scale with Apache Ignite

During this session Denis, Apache Ignite PMC chair, will tell, how Apache Ignite and GridGain help to address these limitations with model training and execution, and help achieve near-real-time, continuous learning. It will be explained how ML/DL work with Apache Ignite, and how to get started.

Moving Apache® Ignite® into Production: Best Practices for Deploying Apache Ignite in the Cloud

This webinar discusses deploying Apache Ignite into production in public and private clouds. Companies have faced many challenges when deploying in-memory computing platforms such as Apache Ignite in the cloud, but they have also discovered many best practices that have made success possible.

Cloud deployment best practices

Cloud deployments offer the potential for almost infinite resources and flexible scalability. But there are so many options! It can be overwhelming to know which services are best for your use case. Building distributed systems only adds to the complexity. Come learn some best practices on how to best structure and deploy IMDB/IMDG applications in a cloud environment.

The Insiders Checklist for Hardening an In-Memory Computing Cluster

In this talk Denis Magda, GridGain's VP of product management and Apache® Ignite™ PMC Chair, will walk through the various components of Apache Ignite and GridGain -- including memory storage, networking layer, compute grid -- to help explain many of best practices and the reasoning behind them.

HTAP via integration of Apache Ignite and Apache Spark

In this talk Roman will explain advantages of compute and storage disaggregation in general, and using Apache Ignite as a storage engine for Apache Spark in particular to handle HTAP workloads.

Moving Apache Ignite into Production: An Initial Checklist

Guaranteeing that your in-memory computing solution stays up and running is the most important goal for a rolling out a new production environment. The trick is making sure that you have all the bases covered and have thought through all your requirements, needs, and potential roadblocks. In this webinar, Apache® Ignite™ PMC Chair Denis Magda shares a checklist to consider for your Apache Ignite production deployments.

This webinar is the first in a series that will guide you through the best development, monitoring, and troubleshooting practices for deploying Apache Ignite across different topologies and use cases.

Distributed ML/DL with Ignite ML Module Using Apache Spark as Database

The current implementation of ML algorithms in Spark has several disadvantages associated with the transition from standard Spark SQL types to ML-specific types, a low level of algorithms' adaptation to distributed computing, a relatively slow speed of adding new algorithms to the current library. Apache Ignite could work closely with Apache Spark due to exellent Ignite RDD/Ignite DataFrame implementation (see https://ignite.apache.org/use-cases/spark/shared-memory-layer.html). Also Apache Ignite has Ignite ML module that includes a lot of distributed ML algorithms, NLP package (will be available in next release, 2.8), the bunch of approximate ML algorithms, simple integration with TensorFlow via TensorFlow Ignite Dataset (currently, this is a part of TF.contrib package) and also each algorithm supports the model updating that gives us ability to make online-learning not only for KMeans and LinReg.

Apache Ignite for Node.js Developers

The benefits of Ignite are now available to Node.js developers with the addition of the Node.js Thin Client for Ignite. In this webinar, using examples, Denis will cover the specifics of how to use Node.js with Ignite, including:

• Instantiating an Ignite Client
• Creating an Ignite Client Configuration
• Connecting a Client to an Ignite Cluster Node
• Obtaining an Ignite Storage Instance
• Configuring an Ignite Storage Instance
• Performing Key-Value Queries
• Performing SQL, SQL Fields and Scan Queries

Using TensorFlow with Apache Ignite

Yuri Babak will explain how to use TensorFlow with Apache Ignite. Topics covered in this presentation:

• Apache Ignite as a distributed data source for TensorFlow
• Distributed training of the model on the TensorFlow cluster over Apache Ignite
• Inference TensorFlow models on Apache Ignite cluster

What’s New in Apache Ignite 2.7

Learn what's new with Apache Ignite 2.7. This session, given by Denis Magda, Apache Ignite PMC Chair, is for all Apache Ignite users. You will learn how the new capabilities of Apache Ignite work. You will also understand more about some of the other changes made to Apache Ignite, and the reasoning behind them. Come with your questions, and learn from the questions of your peers.

Turbocharge your MySQL queries in-memory with In-Memory Computing

Learn how to boost performance 1000x and scale to over 1 billion transactions per second with in-memory storage of hundreds of TBs of data for your MySQL-based applications. Attendees will learn how Apache Ignite handles auto-loading of a MySQL schema and data from PostgreSQL, supports MySQL indexes, supports compound indexes, and various forms of MySQL queries including distributed MySQL joins.

GridGain Cloud: Deploy Apache Ignite in Minutes

If you're interested in in-memory computing and are trying Apache Ignite or are interested in-memory computing as a Service, this session about GridGain Cloud is for you. Learn how to deploy Ignite as a service with just a few clicks, and how to use Ignite as a distributed cache or in-memory database (IMDB) as a service.

In-Memory Computing Best Practices: Developing New Apps, Channels and APIs

Digital transformation is arguably the most important initiative in IT today, in large part because of its ability to improve the customer experience and business operations, and to make a business more agile. But delivering a responsive digital business is not possible at scale without in-memory computing. This session, the third in the In-Memory Computing Best Practices Series, dives into how in-memory computing acts as a foundation for digital business.

Workshop: Machine Learning 101 with In-Memory Computing

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, risk analysis and fraud detection tasks by storing and processing massive amounts of data in memory and on disk across a cluster of machines. By the end of the workshop the attendees will learn how to:

• Deploy a distributed Apache Ignite cluster.
• Preload data into the cluster deciding how much RAM is to be available for storage needs.
• Create custom calculations and algorithms and utilize cluster’s resources for their execution.
• Solve optimizations problems by simulating the process of biological evolution.
• Apply machine learning algorithms supported by Apache Ignite.

Workshop: In-Memory Computing Essentials for Data Scientists

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, risk analysis and fraud detection tasks by storing and processing massive amounts of data in memory and on disk across a cluster of machines. By the end of the workshop the attendees will learn how to:

• Deploy a distributed Apache Ignite cluster.
• Preload data into the cluster deciding how much RAM is to be available for storage needs.
• Create custom calculations and algorithms and utilize cluster’s resources for their execution.
• Solve optimizations problems by simulating the process of biological evolution.
• Apply machine learning algorithms supported by Apache Ignite.

Relational DBMSs: Faster Transactions and Analytics with In-Memory Computing

Combining Apache Ignite with a Relational DBMS can offer enterprises the best of both open-source worlds: a highly-scalable high-velocity grid-based in-memory SQL database, with a robust fully-featured SQL persistent datastore for advanced analytics and data-warehouse capabilities.

Topics to be covered:

• How to complement a Relational DBMS for Hybrid Transactional/Analytical Processing (HTAP) by leveraging the massive parallel processing and SQL capabilities of Apache Ignite.
• How to use Apache Ignite as an In-Memory Data Grid that stores data in memory and boosts applications performance by offloading reads from a Relational DBMS.
• The strategic benefits of using Apache Ignite instead of Memcache, Redis, GigaSpaces, or Oracle Coherence.

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Distributed databases can make so many things easier for a developer, but not always for DevOps. Kubernetes has come to the rescue with an easy application orchestration! It is straightforward to do the orchestration leaning on relational databases as a data layer. However, it is more difficult to do the same when a distributed SQL database or other kind of distributed storage is used instead. In this presentation, attendees will learn how Kubernetes can orchestrate a distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes
• Database Resilience - automated horizontal scalability
• Database Availability - what’s the role of Kubernetes and the database
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with the durability of disk

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Distributed databases can make so many things easier for a developer, but not always for DevOps. Kubernetes has come to the rescue with an easy application orchestration! It is straightforward to do the orchestration leaning on relational databases as a data layer. However, it is more difficult to do the same when a distributed SQL database or other kind of distributed storage is used instead. In this presentation, attendees will learn how Kubernetes can orchestrate a distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes
• Database Resilience - automated horizontal scalability
• Database Availability - what’s the role of Kubernetes and the database
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with the durability of disk

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Distributed databases can make so many things easier for a developer, but not always for DevOps. Kubernetes has come to the rescue with an easy application orchestration! It is straightforward to do the orchestration leaning on relational databases as a data layer. However, it is more difficult to do the same when a distributed SQL database or other kind of distributed storage is used instead. In this presentation, attendees will learn how Kubernetes can orchestrate a distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes
• Database Resilience - automated horizontal scalability
• Database Availability - what’s the role of Kubernetes and the database
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with the durability of disk

Memory-Centric Architecture - A New Approach to Distributed Systems

In this presentation, attendees will learn how to achieve the best performance and scale with the new memory-centric approach to distributed architectures. The presentation will review traditional in-memory and disk-based systems, compare their strengths and weaknesses, cover features such as ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance and more. Additionally, the presentation will cover some of the most common use cases for distributed computing and analyze some large Apache Ignite and GridGain deployments. Finally, attendees will learn how to build the most effective and scalable systems by borrowing the best of two worlds - disk-first and memory-first approaches.

Stream Processing Best Practices - In the Cloud and with Apache Ignite

Learn some of the best practices companies have used for making Apache Kafka and Apache Ignite scale. Making stream processing scale requires making all the components --including messaging, processing, storage -- scale together. During this talk, Akmal will explain:

• The integration between Apache Ignite and Kafka
• Examples of how Ignite and Kafka are used together
• Recommended approaches for deployment, monitoring and management
• Tips and tricks for performance and scalability tuning

Best Practices for Stream Processing with Kafka and Apache Ignite

Learn some of the best practices companies have used for making Apache Kafka and Apache Ignite scale. Making stream processing scale requires making all the components --including messaging, processing, storage -- scale together. During this talk, Akmal will explain:

• The integration between Apache Ignite and Kafka
• Examples of how Ignite and Kafka are used together
• Recommended approaches for deployment, monitoring and management
• Tips and tricks for performance and scalability tuning

Apache Ignite performance measurement. How we do benchmarks

We invite you to the next meeting of Apache Ignite Community in Moscow. Let's talk about benchmarks, what to do with unstable tests and how major open source features are created using the example of Transparent Data Encryption in Apache Ignite.

Best Practices for Stream Processing with Kafka and Apache Ignite

Learn some of the best practices companies have used for making Apache Kafka and Apache Ignite scale. Making stream processing scale requires making all the components --including messaging, processing, storage -- scale together. During this talk, Denis will explain:

• The integration between Apache Ignite and Kafka and the commercially supported versions, GridGain and Confluent
• Examples of how Ignite and Kafka are used together
• Recommended approaches for deployment, monitoring and management
• Tips and tricks for performance and scalability tuning

In-Memory Computing Essentials for Data Scientist

Lucas will talk about the fundamental capabilities of in-memory computing platforms that specifically boost high-load applications and services. These cost-effective capabilities bring existing IT architecture to the next level by storing and processing massive quantities of data both in RAM and, optionally, on disk.

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Distributed databases can make so many things easier for a developer, but not always for DevOps. Kubernetes has come to the rescue with an easy application orchestration! It is straightforward to do the orchestration leaning on relational databases as a data layer. However, it is more difficult to do the same when a distributed SQL database or other kinds of distributed storage is used instead. In this presentation, attendees will learn how Kubernetes can orchestrate a distributed database.

Powering up banks and financial institutions with distributed systems

In this talk, attendees will learn about the key capabilities and features of in-memory computing platforms that are important for financial applications -- including ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance, fraud detection and more.

Best practices for stream ingestion, processing and analytics using in-memory computing

In this talk, Akmal Chaudhri will share the best practices used for real-time stream ingestion, processing and analytics using Apache Ignite, Apache Kafka, Apache Spark and other technologies. Akmal, will explain how to:

• Optimize stream ingestion from Kafka and other popular messaging and streaming technologies
• Architect pre-processing and analytics for performance and scalability
• Implement and tune Apache Ignite or GridGain and Spark together
• Design to ensure performance for real-time reports

Apache Ignite: The In-Memory Hammer In Your Data Science Toolkit

In this presentation we will look at some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Scale Out and Conquer: Architectural Decisions Behind Distributed In-Memory Systems

In this session, attendees will learn about the compromises and pitfalls architects face when designing distributed systems. They will also learn the advantages and disadvantages of different data-sharding algorithms, effective data models for distributed environments, synchronization and coordination in distributed systems, and local scalability issues of speeding up local processing on cluster nodes.

Best Practices for Stream Ingestion, Processing and Analytics Using In-Memory Computing

Learn the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite®, GridGain®, Apache Kafka™, Apache Spark™ and other technologies. In this session Valentin will explain how to:

• Optimize stream ingestion from Kafka and other popular messaging and streaming technologies
• Architect pre-processing and analytics for performance and scalability
• Implement and tune Apache Ignite or GridGain and Spark together
• Design to ensure performance for real-time reports

Apache Ignite: From In-Memory Data Grid to Memory-Centric Distributed Database

In this session Roman will explain how Apache Ignite evolved from in-memory data grid to memory-centric database, and you will learn about its peculiarities and strengths for fast data storing and processing.

How to become a Big Data Rockstar in 15 minutes!

The secret? Apache Ignite! Apache Ignite is a memory-centric distributed database, caching, and processing platform. It is designed for transactional, analytical, and streaming workloads, delivering in-memory performance at scale.
In this presentation attendees will learn about some of the key capabilities of Ignite, such as:

• Turbocharging SQL queries when working with existing Relational database systems
• Sharing data and state across multiple Spark jobs using RDDs and DataFrames
• Using Ignite’s Machine Learning library for Data Science
• Easing DevOps dilemmas by using Ignite with Kubernetes

Attendees will also learn how to download and install Ignite and start to be productive in under 5 minutes.

In-Memory Computing Essentials for Data Scientists

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, risk analysis and fraud detection tasks by storing and processing massive amounts of data in memory and on disk across a cluster of machines. By the end of the workshop the attendees will learn how to:

• Deploy a distributed Apache Ignite cluster.
• Preload data into the cluster deciding how much RAM is to be available for storage needs.
• TCreate custom calculations and algorithms and utilize cluster’s resources for their execution.
• Solve optimizations problems by simulating the process of biological evolution.
• Apply machine learning algorithms supported by Apache Ignite.

This two-hour workshop is a must-attend event for all data scientists!

PostgreSQL with Apache Ignite: Faster Transactions and Analytics

Combining Apache Ignite with PostgreSQL can offer enterprises the best of both open-source worlds: a highly-scalable high-velocity grid-based in-memory SQL database, with a robust fully-featured SQL persistent datastore for advanced analytics and data-warehouse capabilities. Topics to be covered:

• How to complement PostgreSQL for Hybrid Transactional/Analytical Processing (HTAP) by leveraging the massive parallel processing and SQL capabilities of Apache Ignite.
• How to use Apache Ignite as an In-Memory Data Grid that stores data in memory and boosts applications performance by offloading reads from PostgreSQL.
• The strategic benefits of using Apache Ignite instead of Memcache, Redis, GigaSpaces, or Oracle Coherence.

Machine and Deep Learning with an Apache Ignite

Apache Ignite Release 2.4 added built-in machine learning (ML) and deep learning (DL). It not only eliminates any delays caused by transferring data to a different database or store. It delivers near real-time performance by running a variety of ML and DL algorithms in place, in memory, that are optimized for collocated processing.

Learn more about these new capabilities and how to use them in Apache Ignite 2.4. In his talk, Akmal will provide:

• An overview of the ML and DL algorithms and how they work
• Examples of how to implement each ML and DL algorithm
• Tips and tricks for getting the most performance out of ML and DL

Improving Apache Spark™ In-Memory Computing with Apache Ignite^®

Learn how Apache Ignite simplifies development and improves performance for Apache Spark. In his talk, Akmal Chaudhri will explain how Apache Spark and Ignite are integrated, and how they are used together for analytics, stream processing and machine learning.

By the end of this talk you will understand:

• How Apache Ignite's native RDD and new native DataFrame APIs work
• How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark
• How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames
• How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance

Speeding-up the IoT: Best practices for stream ingestion, processing and analytics using in-memory computing

In this talk, Akmal Chaudhri will share the best practices used for real-time stream ingestion, processing and analytics using Apache Ignite, Apache Kafka, Apache Spark and other technologies. Akmal will explain how to:

• Optimize stream ingestion from Kafka and other popular messaging and streaming technologies
• Architect pre-processing and analytics for performance and scalability
• Implement and tune Apache Ignite and Spark together
• Design to ensure performance for real-time reports

Best Practices for Deploying Distributed Databases and In-Memory Computing Platforms with Kubernetes

Denis will explain how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache Ignite as an example. He'll demonstrate how to:

• Deploy, provision and manage an IMDG or IMDB when you wish to keep data in RAM
• Set up and manage persistence for the in-memory technologies above or configure a disk-based distributed database
• Set up auto-discovery and automated horizontal scalability, and use other tricks for high availability

In-Memory Computing Essentials for Data Scientists

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, risk analysis and fraud detection tasks by storing and processing massive amounts of data in memory and on disk across a cluster of machines. These capabilities and benefits will be demonstrated with the usage of Apache Ignite which is the in-memory computing platform that is durable, strongly consistent, and highly available with powerful SQL, key-value and processing APIs.

Apache Ignite + Apache Spark RDDs and DataFrames integration (ENG)

This session will explain how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning. By the end of this session attendees will understand:

• How Apache Ignite’s native RDD and new native DataFrame APIs work
• How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark
• How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames
• How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance

Adding Speed and Scale to Existing Applications with No Rip and Replace Using Apache Ignite

Learn how companies are not only adding speed and scale without ripping out, rewriting or replacing their existing applications and databases, but also how they're setting themselves up for future projects to improve the customer experience. In this talk, Denis will explain how to:

• Get started with Apache Ignite as In-Memory Data Grid (IMDG) deployed on top of RDBMS or NoSQL database
• Keep data in sync across RAM (Apache Ignite) and disk (RDBMS/NoSQL database)
• Leverage from Apache Ignite distributed SQL and ACID transaction for IMDG scenarios
• Move further and start to build HTAP applications, real-time analytics, and machine learning, on the same IMDG

Machine and Deep Learning with in-memory computing

Apache Ignite is an open-source distributed database, caching and processing platform designed to store and compute on large volumes of data across a cluster of nodes. Using this free, open-source software, Akmal will give:

• An overview of the ML and DL algorithms and how they work
• Examples of how to implement each ML and DL algorithm
• Tips and tricks for getting the most performance out of ML and DL

Best Practices for Deploying Distributed Databases and In-Memory Computing Platforms with Kubernetes

In-memory computing technologies such as in-memory data grids (IMDG) and databases (IMDB), NoSQL and NewSQL databases can make so many things easier for a developer. But implementing DevOps for these distributed technologies and the related storage can be difficult. Luckily Kubernetes has come to the rescue!

In this webinar, learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache Ignite as an example.

In-Memory Computing Essentials for Architects and Developers: Part 1

In this webinar, Akmal Chaudhri will introduce the fundamental capabilities and components of an in-memory computing platform with a focus on Apache Ignite, and demonstrate how to apply the theory in practice. With increasingly advanced coding examples, architects and developers will learn about:

• Cluster configuration and deployment
• Data processing with key-value APIs
• Data processing with SQL

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Distributed databases can make so many things easier for a developer, but not always for DevOps. Kubernetes has come to the rescue with an easy application orchestration! It is straightforward to do the orchestration leaning on relational databases as a data layer. However, it is more difficult to do the same when a distributed SQL database or other kind of distributed storage is used instead. In this presentation, attendees will learn how Kubernetes can orchestrate a distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes
• Database Resilience - automated horizontal scalability
• Database Availability - what’s the role of Kubernetes and the database
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with the durability of disk

Scale Out and Conquer: Architectural Decisions Behind Distributed In-Memory Systems

In this talk attendees will learn about the compromises and pitfalls architects face when designing distributed systems:

• Advantages and disadvantages of different data-sharding algorithms
• Effective data models for distributed environments
• Synchronization and coordination in distributed systems
• Local scalability issues of speeding up local processing on cluster nodes

Apache Cassandra vs Apache Ignite for HTAP

Join Denis Magda to learn the current best-practices for HTAP along with the differences between Apache Cassandra and Apache Ignite, two of the most-common technologies utilized for Hybrid Transactional/Analytical Processing. This session will cover:

• A detailed comparison of Apache Ignite and Apache Cassandra for HTAP applications
• The requirements for real-time, high volume HTAP applications
• Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale

Building New Hybrid Transactional/Operational Processing (HTAP) Applications With Apache® Ignite®

Learn how companies build new HTAP applications with in-memory computing that leverage analytics within transactions to improve business outcomes. This is how many retail innovators like Amazon, Expedia/HomeAway or SaaS innovators like Workday have succeeded. This webinar will explain with examples on how to:

• Merge operational data and analytics together, so that analytics can work against the most recent data
• Improve processing and analytics scalability with massively parallel processing (MPP)
• Increase transaction throughput using a combination of distributed SQL, ACID transaction support and native persistence
• Synchronize data and transactions with existing systems

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Distributed databases can make so many things easier for a developer, but not always for DevOps. Kubernetes has come to the rescue with an easy application orchestration! It is straightforward to do the orchestration leaning on relational databases as a data layer. However, it is more difficult to do the same when a distributed SQL database or other kind of distributed storage is used instead. In this presentation, attendees will learn how Kubernetes can orchestrate a distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes
• Database Resilience - automated horizontal scalability
• Database Availability - what's the role of Kubernetes and the database
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with the durability of disk

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT

This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. In particular, attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.

In-memory computing hot topics & emerging trends: Panel discussion in Menlo Park

While the cost of memory is still slightly higher than disk-based storage, an in-memory computing solution offers a tremendous increase in performance and much greater flexibility to incorporate new capabilities in the future. The benefit? A far superior return on investment (ROI), especially when competitive advantage and customer experience is taken into account.

You are invited to attend our June 13 gathering at BootUp in Menlo Park for an insightful panel discussion on in-memory computing hot topics and emerging trends (and more). The panel of experts will also field your questions, suggestions and ideas.

Skyrocket Java applications with the open-source Apache Ignite

In her talk, Dani will introduce the many components of the open-source Apache Ignite. As Java professionals, you will learn how to solve some of the most demanding scalability and performance challenges. She’ll also cover a few typical use cases and work through some code examples.

Adding Speed and Scale to Existing Applications with No Rip and Replace Using Apache® Ignite®

Learn how companies are not only adding speed and scale without ripping out, rewriting or replacing their existing applications and databases, but also how they're setting themselves up for future projects to improve the customer experience. This webinar will explain, with examples, how to:

• Get started with Apache Ignite as In-Memory Data Grid (IMDG) deployed on top of RDBMS or NoSQL database
• Keep data in sync across RAM (Apache Ignite) and disk (RDBMS/NoSQL database)
• Leverage from Apache Ignite distributed SQL and ACID transaction for IMDG scenarios
• Move further and start to build HTAP applications, real-time analytics, and machine learning, on the same IMDG

Improving Apache Spark™ In-Memory Computing with Apache Ignite®

Val will explain how Apache Ignite® simplifies development and improves performance for Apache Spark™. He'll demonstrate how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning. By the end of his presentation you'll understand:

• How Apache Ignite’s native RDD and new native DataFrame APIs work
• How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark
• How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames
• How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT

This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. Attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.

Machine Learning and Deep Learning with Apache® Ignite®

Apache Ignite Release 2.4 added built-in machine learning (ML) and deep learning (DL). It not only eliminates any delays caused by transferring data to a different database or store. It delivers near real-time performance by running a variety of ML and DL algorithms in place, in memory, that are optimized for collocated processing. Learn more about these new capabilities and how to use them in Apache Ignite 2.4.

Comparing Apache Ignite and Cassandra for Hybrid Transactional/Analytical Processing (HTAP)

The 10x growth of transaction volumes, 50x growth in data volumes -- along with the drive for real-time visibility and responsiveness over the last decade -- have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional/analytical processing (HTAP).

Comparing Apache Ignite and Cassandra for Hybrid Transactional/Analytical Processing (HTAP)

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite®. This session will cover:

• The requirements for real-time, high volume HTAP applications
• Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
• A detailed comparison of Apache Ignite and GridGain® for HTAP applications

Improving Apache Spark™ In-Memory Computing with Apache Ignite®

Learn how Apache Ignite® simplifies development and improves performance for Apache Spark™. This session will explain how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning. By the end of this session you will understand:

• How Apache Ignite's native RDD and new native DataFrame APIs work
• How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark
• How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames
• How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance

Comparing Apache Ignite and Cassandra for Hybrid Transactional/Analytical Processing (HTAP)

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite®. This session will cover:

• The requirements for real-time, high volume HTAP applications
• Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
• A detailed comparison of Apache Ignite and GridGain® for HTAP applications

Apache Spark and Apache Ignite: Make streaming analytics real with in-memory computing

Learn how Apache Spark is integrated with Apache Ignite through standard Spark APIs, and how Spark benefits from processing data in-memory in Apache Ignite. In this session Akmal will demonstrate how to:

• Use Ignite as an in-memory database for Spark applications
• Perform streaming analytics by deploying Spark stream pipeline
• Process data stored in Ignite with Spark RDDs and DataFrames
• Speed up SQL queries by leveraging the Ignite SQL engine and indexing

Apache Ignite: the in-memory hammer in your data science toolkit.

In this presentation, Akmal will look at some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Faster data access and processing? Our experiment with Apache Ignite

Slow database performance is a common complaint for Java developers. Is Apache Ignite the solution? Akmal Chaudhri will cover a few typical use cases and work through some code examples using Apache Ignite.

The In-Memory Hammer In Your Data Science Toolkit

In this presentation, Akmal will explain some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Comparing Apache® Ignite® and Cassandra™ for Hybrid Transactional Applications (HTAP)

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite®. This session will cover:

• The requirements for real-time, high volume HTAP applications
• Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
• A detailed comparison of Apache Ignite and GridGain® for HTAP applications

Comparing Apache® Ignite® and Cassandra™ for Hybrid Transactional Applications (HTAP)

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite®. This session will cover:

• The requirements for real-time, high volume HTAP applications
• Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
• A detailed comparison of Apache Ignite and GridGain® for HTAP applications

In-Memory Computing Essentials for Data Scientists

In this hands on workshop, attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, risk analysis and fraud detection tasks by storing and processing massive amounts of data in memory and on disk across a cluster of machines.

Apache Ignite: the in-memory hammer in your data science toolkit

In this presentation, Akmal will explain some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

How to Share State Across Multiple Spark Jobs using Apache Ignite

This session will demonstrate how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite

Apache Ignite: The in-memory hammer in your data science toolkit

Join Big Bang Data Science as we learn from Dr. Akmal Chaudhri about some of the main components of Apache Ignite, such as the Compute Grid, Data Grid, and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Choosing the Right In-Memory Computing Technology

The need for real-time computing has resulted in the growth of many different in-memory computing technologies including caches, in-memory data grids, in-memory databases, streaming technologies and broader in-memory computing platforms. But what are the best technologies for each type of project? Learn about your options from one of the leading in-memory computing veterans.

All the Cool Kids are Doing it: The Whys and Hows of Architecting a Distributed Caching solution for your use case with Apache Ignite

Are you considering a distributed cache to help accelerate and scale your existing application? Or do you have a new project that you know the SLAs are going to require an act of magic to produce? Have no fear, Foti is here to walk your through some reference architectures on various use cases that have benefited from using Apache Ignite. He will cover the Why and How of each use case and the pros and cons of different technology choices. How to use Kafka, noSQL, RDBMS, Kubernetes and container deployment, Spark, etc will all be discussed in terms of various best practices in architecting the right solution with Apache Ignite.

In-Memory Computing Essentials for Architects and Developers - Part 1

Denis Magda will talk about the main features and components of In-Memory Computing solutions using the example of Apache Ignite. The webinar combines theory and practice, after which participants will be able to design and write code for similar systems. On specific examples of the code you will learn about:

• Configuration and launch of clusters
• Data processing using key-value API
• Optimal data processing with distributed SQL

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT

This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. In particular, attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.

In-Memory Computing Essentials for Java Developers

Akmal Chaudhri will introduce to the fundamental capabilities of in-memory computing platforms that boost high-load applications and services, and bring existing IT architecture to the next level by storing and processing a massive amount of data both in RAM and, optionally, on disk.

Catch an intro to the Java-powered Apache Ignite - memory-centric distributed platform

In his talk, Akmal will introduce the many components of the open-source Apache Ignite. You will learn how to solve some of the most demanding scalability and performance challenges. Akmal will also cover a few typical use cases and work through some code examples. Hope to see you there so you can leave ready to fire up your database deployments!

Distributed Database DevOps Dilemmas? Kubernetes to the rescue!

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost highly-loaded applications, research projects, risk analysis and fraud detection tasks by storing and processing massive amounts of data in memory and on disk across a cluster of machines. These capabilities and benefits will be demonstrated with the usage of Apache Ignite which is the in-memory computing platform that is durable, strongly consistent, and highly available with powerful SQL, key-value and processing APIs.

Skyrocket Java applications with the open-source Apache Ignite

In his talk, Akmal will introduce the many components of the open-source Apache Ignite. As Java professionals, you will learn how to solve some of the most demanding scalability and performance challenges. He’ll also cover a few typical use cases and work through some code examples. Attendees would leave ready to fire up their own database deployments!

Basics of In-Memory Computing for architects and developers: Part 1

Denis Magda will talk about the main features and components of In-Memory Computing solutions using the example of Apache Ignite. The webinar combines theory and practice, after which participants will be able to design and write code for similar systems. On specific examples of the code you will learn about:

• Configuration and launch of clusters
• Data processing using key-value API
• Optimal data processing with distributed SQL

The In-Memory Hammer In Your Data Science Toolkit

In this presentation, Akmal will explain some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Building consistent and highly available distributed systems with Apache Ignite and GridGain

In this session, meetup attendees will be given an overview of Apache® Ignite® and GridGain capabilities that allow the delivery of high availability, while not breaking data consistency. Specific guidelines will be presented on how to build such systems covering topics such as:

• In-memory backups
• Data persistence
• Data center replication
• Full and incremental snapshots

Kubernetes: Good, Bad, Ugly of GKE and Distributed Databases in Kubernetes

In this talk you will learn how Kubernetes can orchestrate distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes
• Database Resilience - automated horizontal scalability
• Database Availability - what’s the role of Kubernetes and the database
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with durability of disk

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT

It is not enough to build a mesh of sensors or embedded devices to obtain more insights about the surrounding environment and optimize your production systems. Usually, your IoT solution needs to be capable of transferring enormous amounts of data to storage or the cloud where the data have to be processed further. This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources.

Skyrocket Java applications with the open-source Apache Ignite

In his talk, Akmal will introduce the many components of the open-source Apache Ignite. Meetup members, as Java professionals, will learn how to solve some of the most demanding scalability and performance challenges. He’ll also cover a few typical use cases and work through some code examples. Attendees would leave ready to fire up their own database deployments!

GridGain Webinar: Redis Replaced: Why Companies Now Choose Apache® Ignite® to Improve Application Speed and Scale

Learn why businesses are choosing Apache Ignite to handle their in-memory computing needs, and moving away from traditional caches like Redis. In this session, Denis will explain how:

• In-memory technologies have evolved from caches to in-memory computing platform
• Apache Ignite slides in-between existing applications and SQL databases to improve performance and scale
• Apache Ignite native SQL and ACID transaction support works
• Apache Ignite in-memory storage and collocated computing scales out linearly to avoid scale limitations with traditional caches

Deploy like a Boss: Using Kubernetes and Apache Ignite!

If downtime is not an option for you and your application needs to be extremely low-latency; what cocktail of open source projects can facilitate this best? Both Kubernetes and Apache Ignite are Open Source Frameworks that work exceedingly well together to achieve said goals. By working with containerization Kubernetes helps enable developers to work seamlessly with new versions of their applications, running them where they want with a flexibly scalable experience. Apache Ignite is the perfect complement to this.

Getting Started with Apache® Ignite^® as a Distributed Database

Apache Ignite native persistence is a distributed ACID and SQL-compliant store that turns Apache Ignite into a full-fledged distributed SQL database. In this webinar, Valentin Kulichenko will:

• Explain what native persistence is, and how it works
• Show step-by-step how to set up Apache Ignite with native persistence
• Explain the best practices for configuration and tuning

Ignite your Cassandra Love Story: Caching Cassandra with Apache Ignite

In this session you will learn how Apache Ignite can turbocharge your Cassandra cluster without sacrificing availability guarantees. In this talk we’ll cover:

• An overview of the Apache Ignite architecture
• How to deploy Apache Ignite in minutes on top of Cassandra
• How companies use this powerful combination to handle extreme OLTP workloads

Java and In-Memory Computing: Apache Ignite

In his talk, Akmal will introduce the many components of the open-source Apache Ignite. Meetup members, as Java professionals, will learn how to solve some of the most demanding scalability and performance challenges. He’ll also cover a few typical use cases and work through some code examples. Attendees would leave ready to fire up their own database deployments!

Turbocharge your MySQL queries in-memory with Apache Ignite

Apache Ignite is a unique data management platform that is built on top of a distributed key-value storage and provides full-fledged MySQL support.Attendees will learn how Apache Ignite handles auto-loading of a MySQL schema and data from PostgreSQL, supports MySQL indexes, supports compound indexes, and various forms of MySQL queries including distributed MySQL joins.

Turbocharge your MySQL queries in-memory with Apache Ignite

Apache Ignite is a unique data management platform that is built on top of a distributed key-value storage and provides full-fledged MySQL support.Attendees will learn how Apache Ignite handles auto-loading of a MySQL schema and data from PostgreSQL, supports MySQL indexes, supports compound indexes, and various forms of MySQL queries including distributed MySQL joins.

Building consistent and highly available distributed systems with Apache Ignite and GridGain

It is well known that there is a tradeoff between data consistency and high availability. However, there are many applications that require very strong consistency guarantees. Making such applications highly available can be a significant challenge. Akmal will explain how to overcome these challenges.

Apache Ignite Service Grid: Foundation of Your Microservices-Based Solution

During this session, Denis will provide a step-by-step guide on how to build a fault-tolerant and scalable microservice-based solution using Apache Ignite's Service Grid and other components to resolve these aforementioned issues.

Meet Apache Ignite In-Memory Computing Platform

In this talk you will learn about Apache Ignite memory-centric distributed database, caching, and processing platform. Roman will explain how one can do distributed computing, and use SQL with horizontal scalability and high availability of NoSQL systems with Apache Ignite.

Deploy like a Boss: Using Kubernetes and Apache Ignite!

If downtime is not an option for you and your application needs to be extremely low-latency; what cocktail of open source projects can facilitate this best? Both Kubernetes and Apache Ignite are Open Source Frameworks that work exceedingly well together to achieve said goals. By working with containerization Kubernetes helps enable developers to work seamlessly with new versions of their applications, running them where they want with a flexibly scalable experience. Apache Ignite is the perfect complement to this.

Ignite The Fire In Your SQL App

Apache Ignite is (an in-memory computing platform OR an in-memory distributed data store and compute grid) with full-fledged SQL, key-value and processing APIs. Many companies have added it as a cache in-between existing SQL databases and their applications to speed up response times and scale. In other projects they've used it as its own SQL database. This session will dive into some of the best practices for both types of projects using Apache Ignite.

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT

This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources.

Building consistent and highly available distributed systems with Apache Ignite and GridGain

It is well known that there is a tradeoff between data consistency and high availability. However, there are many applications that require very strong consistency guarantees, and making such applications highly available can be a significant challenge. In this session, attendees will be given an overview of Apache Ignite and GridGain capabilities that allow the delivery of high availability, while not breaking data consistency.

Scale Out and Conquer: Architectural Decisions Behind Distributed In-Memory Systems

Distributed platforms like Apache® Ignite® rely on a horizontal “scale-out” architecture where you dynamically add more machines to achieve near-linear, elastic scalability. But how does it really work? What are its limits? And how can you optimize performance and scalability?

Getting Started With Apache Ignite

Apache Ignite provides a caching layer between applications and the system of record, but additionally, it provides a peer to peer architecture for transacting data, performing computations, microservices, streaming, and much more. During this session, we will do a deep-dive into the Apache Ignite architecture and discuss how it is being deployed around the globe. You will walk away knowing why and when to use Apache Ignite in your next data intensive application!

Want extreme performance at scale? Do distributed the RIGHT way!

During this meetup, Valentin Kulichenko will talk about challenges and pitfalls one may face when architecting and developing a distributed system. Valentin will show how to take advantage of the affinity collocation concept that is one of the most powerful and usually undervalued technique provided by distributed systems. He will take Apache Ignite as a database for his experiments covering these moments in particular: What is data affinity and why is it important for distributed systems? What is affinity colocation and how does it help to improve performance? How does affinity colocation affects execution of distributed computations and distributed SQL queries? And more...

In-Memory Computing Essentials for Architects and Developers: Part 2

In this webinar, Denis Magda will introduce the fundamental capabilities and components of a distributed, in-memory computing platform. With increasingly advanced coding examples, you’ll learn about:

• Collocated processing
• Collocated processing for distributed computations
• Collocated processing for SQL (distributed joins and more)
• Distributed persistence usage

Apache Ignite Use Cases for Banks and Telecoms

Welcome to the inaugural gathering of the Moscow Apache® Ignite® Meetup! Our guest experts - Mikhail Kuznetzov, Mikhail Khasin, and Victor Khoodyakov will talk about their experiences implementing solutions for a large bank as well as a telecom company, based on Apache Ignite.

Distributed Database DevOps Dilemmas? Kubernetes to the Rescue

Distributed databases can make so many things easier for a developer... but not always for DevOps. OK, almost never for DevOps. Kubernetes has come to the rescue with an easy application orchestration! In this talk you will learn how Kubernetes can orchestrate distributed database like Apache Ignite, in particular:

• Cluster Assembling - database nodes auto-discovery in Kubernetes.
• Database Resilience - automated horizontal scalability.
• Database Availability - what’s the role of Kubernetes and the database.
• Utilizing both RAM and disk - set up Apache Ignite in a way to get in-memory performance with durability of disk.

Apache Ignite: the in-memory hammer in your data science toolkit

Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers are able to find hidden insights without the help of explicit programming. These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats. The availability of very powerful in-memory computing platforms, such as the open-source Apache Ignite (https://ignite.apache.org/), means that more organizations can benefit from machine learning today. In this presentation, Denis will look at some of the main components of Apache Ignite, such as a distributed database, distributed computations, and machine learning toolkit. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Meeting the Challenges of Fast Data in Healthcare with In-Memory Technologies

In this webinar, Akmal Chaudhri will discuss the requirements for fast data in healthcare and specifically how Apache Ignite, a distributed in-memory computing platform, is used by drug discovery companies to identify potential therapies for complex diseases.

Implementing Durable Memory-Centric Architectures in Large Financial
Institutions

In this 1-hour webinar, GridGain Systems Chief Product Officer Dmitriy Setrakyan will present how distributed memory-centric architectures can be applied to various financial systems. Dmitriy will first go over some Apache® Ignite® features important for financial use cases, including ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance and more. He will next analyze one of the largest Apache Ignite deployments in the world at Sberbank, a Russian and eastern European bank. He’ll walk through its overall architecture and demonstrate the 5 major challenges that Sberbank ran into when integrating distributed, horizontally scalable memory-centric database at their bank.

In-Memory Computing Essentials for Architects and Developers: Part 1

In this webinar, Denis Magda will introduce the fundamental capabilities and components of an in-memory computing platform, and demonstrate how to apply the theory in practice. With increasingly advanced coding examples, you’ll learn about:

• Cluster configuration and deployment
• Data processing with key-value APIs
• Data processing with SQL

Hands-on Workshop: In-Memory Computing Essentials for Java Developers

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms that boost high-load applications and services, and bring existing IT architecture to the next level by storing and processing a massive amount of data both in RAM and, optionally, on disk. The capabilities and benefits of such platforms will be demonstrated with the usage of Apache Ignite, which is the in-memory computing platform that is durable, strongly consistent, and highly available with powerful SQL, key-value and processing APIs.

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT

It is not enough to build a mesh of sensors or embedded devices to obtain more insights about the surrounding environment and optimize your production systems. Usually, your IoT solution needs to be capable of transferring enormous amounts of data to storage or the cloud where the data have to be processed further. Quite often, the processing of the endless streams of data has to be done in real-time so that you can react on the IoT subsystem's state accordingly. This session will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. In particular, attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.

How to build an event-driven, dynamically re-configurable micro-services platform

Why only look at Apache Kafka to build event-driven microservices when there is also Apache Ignite, which brings far more to the table?

In this presentation, Sven will show you how to combine Apache Ignite with Docker to not only build an event-driven microservice platform but also to make this dynamically re-configurable without any downtime at all.

Apache Ignite: The In-Memory Hammer In Your Data Science Toolkit 

Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers are able to find hidden insights without the help of explicit programming. These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats. The availability of very powerful in-memory computing platforms, such as Apache Ignite, means that more organizations can benefit from machine learning today. In this presentation Denis will look at some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Fast Data meets Big Data in the IoT- Using Apache Ignite 

So, you've built yourself a killer IoT application. You have connected all the things and they are all happily sending over their packets of data faster than you can say "Big Blue". Now what? How do you architect a server architecture that can support all the data flowing in and also be able to grow for the future? In this talk, Rachel will cover some of the architectural decisions you need to consider when choosing a data platform and discuss how Apache Ignite can meet those requirements. Rachel will also cover other design options like NoSQL and Spark and how to deploy in the IBM cloud.

Achieving High Availability and Consistency With Distributed Systems 

Tech talk No. 1 - Val kicks off the learning with a session titled, "Building Consistent and Highly Available Distributed Systems with Apache Ignite and GridGain."

Tech talk No. 2 - Denis continues the knowledge sharing with a session titled, "Harnessing the 21st Century with a Distributed Memory-Centric SQL."

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT 

In this session, Akmal will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. In particular, attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.

How to Share State Across Multiple Spark Jobs using Apache® Ignite® 

Attend this session to learn how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite. During the talk, attendees will learn in detail how IgniteRDD – an implementation of native Spark RDD and DataFrame APIs – shares the state of the RDD across other Spark jobs, applications and workers. Examples will show how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames.

The In-Memory Computing Summit 2017 – North America 

There will be several highly technical talks about Apache Ignite at the 3rd-annual In-Memory Computing Summit Oct. 24-25 at the South San Francisco Conference Center. The IMC Summit is the only industry-wide event that focuses on the full range of in-memory computing-related technologies and solutions held in North America.

Check out the agenda here: https://www.imcsummit.org/us/agenda/schedule/day-1

Better Machine Learning with Apache® Ignite® 

The availability of very powerful in-memory computing platforms, such as Apache® Ignite®, means that more organizations can benefit from machine learning today. In this presentation, Akmal will discuss how the Compute Grid, Data Grid, and Machine Learning Grid components of Apache Ignite work together to enable your business to start reaping the benefits of machine learning.

Through examples, attendees will learn how Apache Ignite can be used for data analysis and be the in-memory hammer in your machine learning toolkit.

Catch an intro to Apache Ignite and skyrocket Java applications 

Join Valentin (Val) Kulichenko as he introduces the many components of the open-source Apache Ignite. You, as a Java professional, will learn how to solve some of the most demanding scalability and performance challenges. He will also cover a few typical use cases and work through some code examples. Hope to see you there so you can leave ready to fire up your database deployments!

Apache® Spark™ and Apache® Ignite®: Where Fast Data Meets the IoT 

During this 1-hour webinar, Denis Magda will discuss a Fast Data solution that can receive endless streams from the Internet of Things (IoT) and be capable of processing the streams in real-time using Apache Ignite’s cluster resources. You will also learn about data streaming to an Apache Ignite® cluster from embedded devices and real-time data processing with Apache® Spark™.

Apache Ignite®: The In-Memory Hammer in Your Data Science Toolkit 

In this presentation, Akmal will show some of the main components of Apache Ignite®, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for data analysis.

Powering up banks and financial institutions with distributed systems 

In this presentation, attendees will learn about important Apache Ignite features for financial applications, such as ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance and more.

A customer case study will also be presented. We will analyze one of the largest Apache Ignite deployments in the world at Sberbank, a Russian and Eastern European Bank, by walking through the overall architecture and demonstrating various implementation and deployment challenges.

Real-time Data Analysis with Apache Ignite High-Performance In-memory Platform 

With the advances in IoT technology, the volume and the diversity of data to be analyzed has enormously increased, and processing it with traditional disk-based technology has become more challenging. Therefore the number of data analysis solutions using in-memory technology, such as Apache Ignite high-performance in-memory platform, has increased.

In this session, Roman will introduce Apache Ignite, and explain how it can be used for real-time analysis of large volumes of IoT data.

Postgres with Apache® Ignite®: Faster Transactions and Analytics 

Join Fotios Filacouris as he discusses how you can supplement PostgreSQL with Apache Ignite. You'll learn:

• The strategic benefits of using Apache Ignite instead of Memcache, Redis®, GigaSpaces®, or Oracle Coherence™
• How to overcome the limitations of the PostgreSQL architecture for big data analytics by leveraging the parallel distributed computing and ANSI SQL-99 capabilities of Apache Ignite
• How to use Apache Ignite as an advanced high-performance cache platform for hot data

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT 

In this session, Akmal will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources. In particular, attendees will learn about data streaming to an Apache Ignite cluster from embedded devices and real-time data processing with Apache Spark.

Giving a boost to the Hadoop and Spark ecosystems with in-memory technologies 

In this talk, Akmal present how to speed up existing Hadoop and Spark deployments by making Apache Ignite responsible for RAM utilization. No code modifications, no new architecture from scratch! Specifically, this presentation will cover : Hadoop Accelerator, HDFS compliant In-Memory File System, MapReduce Accelerator, Spark Shared RDDs, Spark SQL boost.

Stateful Apps in Production and Distributed Database Orchestration 

This talk will focus on a DevOps perspective on the orchestration of distributed databases, Apache Ignite. Denis will speak on node auto-discovery, automated horizontal scalability, availability and utilization of RAM and disk with Apache Ignite.

Better Machine Learning with Apache® Ignite® 

In this presentation, Akmal will discuss how the Compute Grid, Data Grid, and Machine Learning Grid components of Apache Ignite work together to enable your business to start reaping the benefits of machine learning. Through examples, attendees will learn how Apache Ignite can be used for data analysis and be the in-memory hammer in your machine learning toolkit.

Powering up banks and financial institutions with distributed systems 

In this talk, Akmal will start with a brief high-level overview of distributed computing fundamentals and in-memory computing use cases. Then he'll introduce the open-source Apache Ignite, an in-memory computing platform that enables high-performance transactions, real-time streaming, and fast analytics in a single, comprehensive data access and processing layer.

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT 

Quite often, the processing of the endless streams of data has to be done in real-time so that you can react on the IoT subsystem's state accordingly. In this session, Akmal will show attendees how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using Apache Ignite's cluster resources.

Business Intelligence and Apache Ignite for .NET Users 

This presentation will provide a deep dive into .NET features of the top level Apache projects: Apache Ignite. Apache Ignite is the memory-centric platform that combines distributed SQL database and key-value data grid, that is ACID compliant, horizontally scalable and highly available, and empowered with compute and machine learning capabilities.

Apache Ignite: The in-memory hammer in your data science toolkit 

In this talk, Denis will go through some of the main components of Apache Ignite, such as the Compute Grid, Data Grid and the Machine Learning Grid. Through examples, attendees will learn how Apache Ignite can be used for big data analysis.

Implementing In-Memory Computing for Financial Services Use Cases with Apache® Ignite® 

In this presentation, Denis will explain features of the Apache Ignite distributed computing platform that are important for financial use cases, including:

• ACID transaction guarantees
• Distributed ANSI-99 SQL support
• Replication
• Security
• Fault tolerance
• Persistence

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT 

In his talk, Denis will will show attendees how to build a sast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time using the cluster resources of Apache Ignite.

Deploy like a Boss: Using Kubernetes® and Apache® Ignite® 

If downtime is not an option for you, and your application needs to be extremely low-latency, Kubernetes® and Apache® Ignite® are open source frameworks that work exceedingly well together to achieve these goals.

In this webinar, Dani Traphagen will walk through the basics of a Kubernetes and Apache Ignite deployment, including:

• Setting up a Apache Ignite cluster
• Using the Kubernetes IP Finder and the Kubernetes Ignite Lookup Service
• Sharing the Ignite Cluster Configuration
• Deploying your Ignite Pods
• Adjusting the Ignite Cluster Size when you need to scale

Introduction to Apache Ignite, a memory-centric distributed platform 

Apache Ignite is an open source memory-centric platform that combines a distributed SQL database with a Key-Value Data Grid that is ACID-compliant and horizontally scalable. It enables high-performance transactions, real-time streaming, and fast analytics in a single, comprehensive data access and processing layer.

In this webinar, attendees will learn about the many components of Apache Ignite, including the Data Grid, Compute Grid, distributed SQL database and the Machine Learning Grid. We will also cover a few typical use cases and work through some Java code examples.

Building Consistent and Highly Available Distributed Systems with Apache® Ignite® 

In this session, Valentin Kulichenko, Apache Ignite Committer and PMC, will give an overview of some of Apache® Ignite® capabilities that allow the delivery as much availability as possible, while not breaking data consistency. Valentin will give specific guidelines on how to build such systems, and will do a deep dive into topics like:

• In-memory backups
• Data persistence
• Data center replication
• Full and incremental snapshots

Diving into the internals of Apache Ignite's memory architecture 

Apache Ignite is one of the fastest growing Apache projects. The presentation will take the audience on a roadmap discovery of Ignite moving to a memory-centric storage model, supporting both, fast in-memory and durable on-disk data, and blending a distributed SQL database with an in-memory key-value data grid.

An Intro to Apache Ignite, the Memory-centric Distributed Platform 

Join Akmal Chaudhri as he introduces the many components of the open-source Apache Ignite. You, as a Java professional, will learn how to solve some of the most demanding scalability and performance challenges. He will also cover a few typical use cases and work through some code examples.

Distributed ACID Transactions in Apache Ignite 

During this session, Akmal Chaudhri will do a deep-dive on the architecture of Apache Ignite's ACID-compliant transactional subsystem, elaborating on the following:

• Data consistency: one-phase and two-phase commit implementations.
• Fault-tolerance: recovery protocol for running transactions.
• Optimistic and Pessimistic transactions.
• Deadlock-free transactions
• Deadlock detection mechanism

Turbocharge your SQL queries in-memory with Apache® Ignite® 

During this session, Denis will explain how Apache Ignite handles auto-loading of SQL schema and data from MySQL, supports SQL indexes, compound indexes support, and various forms of SQL queries including distributed SQL joins. He will demostrate how to:

• Import SQL schema from MySQL and preserve the data sets stored in MySQL and Apache Ignite in sync.
• Connect to Apache Ignite from your favourite tool or application language using ODBC or JDBC driver and start talking to a clustered data using familiar statements like SELECT, UPDATE, DELETE or INSERT.
• Boost application performance 1,000x and scale to over 1 billion transactions per second with in-memory storage of hundreds of TB's of data for your SQL-based applications.

Apache Ignite Community Meetup - An Overview of Donated Ignite Persistent Store Feature 

Apache Ignite Community decided to gather and dive into the details of Ignite Persistent Store donation to the main code base. It’s planned to give a general overview of the store learning more about its main capabilities and features as well as go over implementation details referring to the source code. To join use the details below. Please join my meeting from your computer, tablet or smartphone.
https://global.gotomeeting.com/join/818661157 You can also dial in using your phone.
United States: +1 (571) 317-3112 Access Code: 818-661-157

Apache Ignite and Apache Spark: This is Where Fast Data Meets the IoT 

During this session, Denis will explain and demonstrate how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time. In particular, you will learn the following:

• Data streaming to an Apache Ignite cluster from embedded devices powered by Apache Mynewt.
• Real-time data processing with Apache Spark and Apache Ignite.

Apache® Ignite® 2.0: Prelude to a Distributed SQL Database 

Apache Ignite 2.0 is a turnkey release which blends a distributed in-memory SQL database (IMDB) and an in-memory key-value data grid (IMDG) under one data management platform. It is also a necessary stepping stone ahead of Ignite 2.1 release which will be focused around the native disk persistence, allowing Ignite operate equally well in-memory and on-disk. You will learn how the off-heap memory architecture in Ignite has been re-engineered to better support SSD or Flash-based persistence. The new off-heap design uses a page-based approach with slab memory allocation, which may be optionally mapped to a persistent storage as is, without having to serialize or deserialize the data. The new architecture automatically handles memory fragmentation, significantly accelerates SQL, and almost completely removes costly garbage collection pauses. You will also learn how to create and alter SQL indexes at runtime, as well as utilize DDL to update distributed data sets using standard SQL syntax. We will also cover B+Tree data structures used to store SQL indexes off-heap.

Apache Ignite and Apache Spark: This is Where Fast Data Meets the IoT 

During this session, Denis will explain and demonstrate how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time. In particular, you will learn the following:

• Data streaming to an Apache Ignite cluster from embedded devices powered by Apache Mynewt.
• Real-time data processing with Apache Spark and Apache Ignite.

Apache Ignite SQL Grid: Hot Blend of Traditional SQL and Swift Data Grid 

In-memory data grids bring exceptional performance and scalability gains to applications built on top of them. The applications truly achieve 10x more performance improvement and become easily scalable and fault-tolerant thanks to the unique data grids architecture. However, because of this particular architecture, a majority of data grids have to sacrifice traditional SQL support requiring application developers to completely rewrite their SQL-based code to support data grid specific APIs.

Apache Ignite and Apache Spark: Where Fast Data Meets the IoT 

During this session, Denis will explain and demonstrate how to build a Fast Data solution that will receive endless streams from the IoT side and will be capable of processing the streams in real-time. In particular, you will learn the following:

• Data streaming to an Apache Ignite cluster from embedded devices powered by Apache Mynewt.
• Real-time data processing with Apache Spark and Apache Ignite.

Benchmarking: Apache Ignite Still Keeps Ahead Of Hazelcast 

There's an ad saying that Hazelcast is up to 50% faster than Apache Ignite, but that may not be true anymore. Check out this benchmark to get the true story.

Apache Flink meets Apache Ignite 

Akmal B. Chaudhri will be giving a quick introduction of Apache Ignite, its main capabilities and how it can add value to your pipelines. Akmal is a Technical Evangelist, specializing in Big Data, NoSQL and NewSQL database technologies.

The next phase of distributed systems with Apache Ignite 

Is memory the new disk? If so, what does this mean for the future of database systems and persistence as we know it? Will all our data(bases) still belong to us? Dani Traphagen explores the key paradigm shifts currently impacting those Fortune 500 companies that view disk as a bottleneck. Dani explains how to optimize toward the cache, leveraging it for low-latency, highly available microservices architectures with the hot-and-fresh-out-of-the-kitchen open source project Apache Ignite.

Apache® Ignite®: Real-Time Processing of IoT-Generated Streaming Data 

During this 1-hour webinar, Denis will explain and demonstrate how to build a fast data solution that can receive endless IoT-generated streams and process them in real-time using Apache Ignite's distributed in-memory computing platform. In particular, you will learn the following:

• How to stream data to an Apache Ignite cluster from embedded devices
• How to conduct real-time data processing on this stream using Apache Ignite

Apache Ignite 2.0 Released 

This major release was under the development for a long time. The community spent almost a year incorporating tremendous changes to the legacy Apache Ignite 1.x architecture. Curious why are we so boastful about this? Some of the main features of Apache Ignite 2.0 are:

• Re-engineered Off-Heap Memory Architecture
• Data Definition Language
• Machine Learning Grid Beta - Distributed Algebra
• Integration with Spring Data, Rocket MQ, Hibernate 5
• Enchanced Inite.Net and Ignite C++ APIs

See release notes for a full list of the changes.

Accelerate MySQL® for Demanding OLAP and OLTP Use Cases with Apache® Ignite™ 

How to overcome the limitations of the MySQL architecture for big data analytics by leveraging the parallel distributed computing and ANSI SQL-99 capabilities of Apache Ignite. How to use Apache Ignite as an advanced high performance cache platform for hot data. The strategic benefits of using Apache Ignite® instead of memcache, Redis®, Elastic®, or Apache® Spark™. At the end of the session, you will understand how incorporating Apache Ignite into your architecture can empower dramatically faster analytics and transactions when augmenting your current MySQL infrastructure.

Apache® Ignite™: An In-Memory Backbone for Microservices-Based Architectures 

When systems that rely on microservices are used under high load or have to process rapidly growing volumes of data, they usually face the same issues and difficulties as applications that are not microservices-based. Disk-backed databases become a performance bottleneck as they can no longer keep up with growing volumes of data that have to be stored and processed in parallel. This degrades application performance and ultimately causes instability.
This webinar discusses how in-memory computing using Apache® Ignite® can overcome the performance limitations common to microservices architectures built using traditional database architectures.

Scalability in Distributed In-Memory Systems 

In-memory computing frameworks and products rely on a simple horizontal scalability property - the more machines we have in a cluster the better the performance. However, a reasonable question arises. If I add a second machine to the cluster will I get 2x improvement? If there are 10 machines in a cluster should I expect overall 10x performance increase? Is it true and if, yes, if the guarantee meets all the time? Join Yakov on his talk to get answers on these questions and learn more about scalability and concurrency concepts implemented in Apache Ignite In-Memory Data Fabric.

Scalability in Distributed In-Memory Systems 

In-memory computing frameworks and products rely on a simple horizontal scalability property - the more machines we have in a cluster the better the performance. However, a reasonable question arises. If I add a second machine to the cluster will I get 2x improvement? If there are 10 machines in a cluster should I expect overall 10x performance increase? Is it true and if, yes, if the guarantee meets all the time? Join Yakov on his talk to get answers on these questions and learn more about scalability and concurrency concepts implemented in Apache Ignite In-Memory Data Fabric.

Presenting Apache Ignite SQL Grid at PGConf US 2017 

Learn how to boost performance 1,000x and scale to over 1 billion transactions per second with in-memory storage of hundreds of TBs of data for your SQL-based applications. Apache Ignite is a unique NewSQL platform that is built on top of a distributed key-value storage and provides full-fledged SQL support. Denis will show how Apache Ignite handles auto-loading of SQL schema and data, SQL indexes, compound indexes support, and various forms of SQL queries including distributed SQL joins. It will be demonstrated how to connect to Apache Ignite from your favorite tool or application language using ODBC or JDBC driver and start talking to a clustered data using familiar statements like SELECT, UPDATE, DELETE or INSERT.

Presenting Apache Ignite SQL Grid at Big Data Bootcamp 

In this presentation, Denis will introduce Apache Ignite SQL Grid component that combines the best of two worlds - performance and scalability of data grids and traditional ANSI-99 SQL support of relational databases. Moreover, Denis will take an existing application that works with a relational database and will show how to run it on top of Apache Ignite with minimum efforts.

Presenting Apache Ignite at Codemotion Rome 2017 

Join and learn about Apache Ignite which is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies.

The Apache® Ignite® SQL Grid: A Hot Blend of Traditional SQL and In-Memory Data Grids 

During this webinar, Apache Ignite PMC chair Denis Magda will introduce the SQL Grid component of Apache® Ignite®. He will discuss:

• ANSI-99 SQL queries including distributed joins
• Creating and leveraging SQL indices
• Data modification with ANSI-99 DML (INSERT, UPDATE, DELETE, etc.)
• Using Apache Ignite's JDBC and ODBC drivers

Apache Ignite: Transform batch-based system into swift real-time solution 

IHS Markit will present first on how they have been using Apache Ignite on several major projects. The 2nd part of the meetup will be led by Mandhir Gidda who's been working with in-memory technologies for nearly 10 years.

The Apache® Ignite® Web Console: Automating RDBMS Integration 

During this webinar, Apache Ignite PMC chair Denis Magda will demonstrate how Apache® Ignite® Web Console enables automatic integration of Apache Ignite and your RDBMS. He will show you how to:

• Import a RBMS schema and map it to the Apache Ignite caches
• Setup indexes
• Create a Java POJO
• Download a ready-to-run Apache Ignite based project that will be fully integrated with the RDBMS

Deploying Apache® Ignite® – Top 7 FAQs 

Christos Erotocritou and Rachel Pedreschi have helped numerous customers get started with Apache® Ignite® and GridGain. During this 1-hour webinar, they will share answers to the most common questions asked prior to deployment. They will also provide guidance that will save you time and make deploying Apache Ignite a more enjoyable experience.

Shared Memory Layer for Spark Applications 

Join Dmitriy Setrakyan, Apache Ignite Project Management Committee Chairman and co-founder and Chief Product Officer at GridGain, to learn more about the need to share state across different Spark jobs and applications and several technologies that make it possible, including Tachyon and Apache Ignite.

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Shared Memory Layer and Faster SQL for Spark Applications 

Learn the importance of In Memory File Systems, Shared In-Memory RDDs with Apache Ignite, as well as the need to index data in-memory for fast SQL execution.

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Apache Ignite - Path to Converged Data Platform 

The presentation will take the audience on a roadmap discovery of Ignite moving to a converged storage model, supporting both, analytical and transactional data sets.

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Apache Ignite London Meetup 

Join us for a technical session to look at Apache Ignite and hear from BlackRock on how they believe it will solve their application performance & scalability challenges.

Please RSVP on Meetup.com.

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Apache Ignite NYC Meetup 

Apache Ignite PMC member, Nikita Ivanov will be presenting a deep dive on Apache Ignite at our NYC meetup, Tuesday, June 28 at Work Market, 240 W 37th St, 9th Floor, New York, NY.

Please RSVP on Meetup.com. Space is limited and is filling up fast!

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Apache Ignite 1.5.0-b1 Early Access Released 

This early access version includes support for .NET and C++, Streamer for MQTT, Twitter, and Apache Flume, compact binary protocol, performance improvements for SQL queries, transactions, and more.

Download Ignite 1.5.0-b1

Apache Ignite 1.4.0 Released 

This is the first Apache Ignite release since the project graduated from incubation in August, 2015. This new release includes SSL support to communication and discovery, support for log4j2, significantly faster JDBC driver implementation, fixes for SQL queries group index logic, auto-retries for cache operations in recoverable cases and more.

Download Ignite 1.4.0

Apache Ignite Graduated to a Top-Level Project 

The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today that Apache® Ignite® has graduated from the Apache Incubator to become a Top-Level Project (TLP), signifying that the project's community and products have been well-governed under the ASF's meritocratic process and principles.

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Apache Ignite 1.3.0 Released 

This new release includes integration with Apache YARN for data center and resource management, fixes for JTA transactions, Hibernate L2 Cache improvements, and more.

Download Ignite 1.3.0

Apache Ignite 1.2.0 Released 

This new release includes shared RDD for Apache Spark (based on Ignite Data Grid), integration with Apache Mesos for data center management, client-mode for light-weight cluster discovery, memory-size eviction policy, and more.

Download Ignite 1.2.0

Apache Ignite 1.1.0 Released 

This new release includes Google Compute Engine and generic cloud TCP discovery IP finder, "Collocated" mode for SQL queries, support for (*) star notation in cache names, fix for SQL union support, and more.

Download Ignite 1.1.0

Apache Ignite 1.0.0 Released 

This new release includes dynamic caching functionality to start and stop caches during runtime, simplified Query API, automatic aggregation, grouping and sorting support for SQL Queries, Streaming examples, and more.

Download Ignite 1.0.0

Apache Ignite 1.0.0-RC3 Released 

This is the first release of Apache Ignite project. The source code in large part is based on the GridGain In-Memory Data Fabric, open source edition, v. 6.6.2, which was donated to Apache Software Foundation. The main feature set of Ignite In-Memory Data Fabric includes:

• Advanced Clustering
• Compute Grid
• Data Grid
• Service Grid
• IGFS - Ignite File System
• Distributed Data Structures
• Distributed Messaging
• Distributed Events
• Streaming & CEP

Download Ignite 1.0.0-RC3

InfoQ Interview with Nikita Ivanov on Apache Ignite Incubation 

InfoQ caught up with Nikita Ivanov, CTO and founder of GridGain, about the In-Memory Computing framework becoming an Apache project, motivation behind this decision, and upcoming features and enhancements of GridGain.

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Apache Ignite Enters Incubation 

GridGain recently announced that the GridGain In-Memory Data Fabric has been accepted into the Apache Incubator program under the name "Apache Ignite." Earlier in 2014, GridGain was transformed to an open source model through Apache 2.0 license. Now, the product will be available under the Apache Foundation project portfolio.

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