Meetups & Events

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

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

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

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 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

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.

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.

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.

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

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

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.

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.

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).

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

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

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

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

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.

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.

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.

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

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 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.

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.

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

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.

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.

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.

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

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.

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.

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!

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.

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!

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

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.

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

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

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.

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!

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

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.

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

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

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!

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.

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.

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.

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.

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.

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.

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.

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.

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?

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!

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 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

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 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.

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.

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.

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 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

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.

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.

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.

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.

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.

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."

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.

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.

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

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.

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!

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™.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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 Flink.

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.

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.

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

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.

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

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.

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

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.

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.

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

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 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

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 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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

This new release includes SQL DML operations support (INSERT, UPDATE, DELETE, MERGE) in Java, MapR distribution support in Hadoop, Entity Framework 2nd level cache and ASP.NET session state cache in .NET, DML and distributed joins in ODBC, stability and performance improvements, and more.

Download Ignite 1.8.0

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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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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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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This new release includes support for distributed SQL JOIN, decimal support in ODBC, custom affinity functions and ASP.NET Output Cache Provider in .NET, stability and performance improvements, and more.

Download Ignite 1.7.0

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 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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This new release includes support for deadlock detection in Ignite caches, ODBC driver, CacheStore implementation backed by Cassandra DB, AtomicSequence and AtomicReference data structures for .NET, transactions API for C++ client, stability and fault-tolerance improvements, and more.

Download Ignite 1.6.0

Add-ons has been added to the website for projects built on top of Ignite. These projects intend to make user experience with Ignite easier. Currently, there are two such projects available - Apache Ignite Extensions and GridGain Web Console.

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This final version of 1.5.0 includes support for .NET and C++, Streamer for MQTT, Twitter, Apache Flume, and Apache Camel, OSGi support, "deadlock-free" transactions, compact binary protocol, performance improvements for SQL queries, transactions, and more.

Download Ignite 1.5.0.final

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

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

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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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

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

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

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

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 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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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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