Technical Presentations About GridGain and Apache Ignite

These technical presentations, developed and presented by GridGain in-memory computing experts, will help you understand, configure, and deploy the GridGain® and Apache Ignite® in-memory computing solutions. The presentations cover topics including in-memory computing, in-memory databases, stream processing, digital integration hubs, data lake acceleration, machine learning, deep learning, and how to apply these technologies and others to power digital transformation.


Apache Ignite’s ANSI-99 SQL support provides application developers a classical SQL database experience while enabling in-memory speeds at petabyte scale for a variety of workloads. Concise SQL syntax and availability of JDBC and ODBC drivers shields the complexity of Ignite’s distributed architecture from developers and allows them to easily manage and query…
When working with multiple data centers, it is important to ensure high availability of your GridGain cluster. The GridGain Enterprise and Ultimate Editions, built on Apache Ignite®, include a Data Center Replication feature that allows data transfer between caches in distinct topologies, even located in different geographic locations.
Learn how to get started with deploying an in-memory data grid so you can solve your immediate application performance challenges and prepare your business for the changing post-COVID-19 world.
Apache Ignite can function in a strong consistency mode which keeps application records in sync across all primary and backup replicas. It also supports distributed ACID transactions that allow you to update multiple entries stored on different cluster nodes and in various caches/tables.
Apache Ignite 2.8 includes over 1,900 upgrades and fixes that enhance almost all components of the platform. The release notes include hundreds of line items cataloging the improvements. In this webinar Ignite community members demonstrate and dissect new capabilities related to production maintenance, monitoring, and machine learning including:
Attendees were introduced to the fundamental capabilities of in-memory computing platforms (IMCPs). IMCPs boost application performance and solve scalability problems by storing and processing unlimited data sets distributed across a cluster of interconnected machines.
With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data and to train models. It can also be hard to scale with data sets that are increasingly frequently larger than the capacity of any single server. The size of the data can also make it hard to incrementally test and retrain models in near real-time to improve business…
Apache Ignite® and GridGain® allow users to perform fast calculations and run highly efficient queries over distributed data. Both Ignite and GridGain provide a flexible configuration that can help you make cluster operations more secure. This webinar covered the following security topics:
Deployment models for Apache Ignite® and applications connected to it vary depending on the target production environment. A bare metal environment provides the most flexibility and fewer restrictions on configuration options.
Change Data Capture (CDC) has become a very efficient way to automate and simplify the ETL process for data synchronization between disjointed databases. It is also a useful tool for efficient replication schemas. These webinar slides cover the fundamental principles and restrictions of CDC and reviews examples of how change data capture is implemented in real life…
To take full advantage of an in-memory platform, it’s often not enough to upload your data into a cluster and start querying it with key-value or SQL APIs. You need to distribute the data efficiently and tap into distributed computations that minimize data movement over the network.
Apache Ignite is a powerful in-memory computing platform. The Apache IgniteSink streaming connector enables users to inject Flink data into the Ignite cache. Join Saikat Maitra to learn how to build a simple data streaming application using Apache Flink and Apache Ignite.
If you experience limitations with the size, scale or performance of your relational database, it may be time to migrate to a distributed system. This webinar discussed how the distributed Apache Ignite platform can function as a database, providing both SQL and JCache APIs to work with your data.
Attendees of this webinar learned how to use the service grid capabilities of the Apache Ignite distributed in-memory computing platform. Simple code examples helped attendees review possible architectural solutions and learn how to build fault-tolerant, scalable and flexible systems.
Most enterprises have PostgreSQL deployments that they will be using for years to come for transactional, big data, mobile, and IoT use cases. How can Postgres continue to support the current and emerging use cases which demand ever higher performance and more scalability into the future?