The Apache Ignite transactional engine can execute distributed ACID transactions which span multiple nodes, data partitions, and caches/tables. This key-value API differs slightly from traditional SQL-based transactions but its reliability and flexibility lets you achieve an optimal balance between consistency and performance at scale by following several guidelines.

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. In addition, consistency and transactional guarantees are put in effect for memory and disk tiers on every cluster node.

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 will be introduced to the fundamental capabilities of in-memory computing platforms (IMCPs) in this Apache Ignite tutorial. IMCPs boost application performance and solve scalability problems by storing and processing unlimited data sets distributed across a cluster of interconnected machines.

Apache Ignite^® and GridGain^® allow you 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. In this webinar, we will cover the following security topics:

• The secure connection between nodes (SSL/TLS)
• User authentication
• User authorization

Using live examples, we will go through the configurations for:

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. We will cover the fundamental principles and restrictions of CDC and review examples of how change data capture is implemented in real life use cases. By the end of this session you will understand:

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 results.

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. When using Docker and Kubernetes environments, you need to decide how Ignite and its associated applications will interact before writing the first line of code.

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.

In this webinar, you’ll see how to design and execute distributed computations considering all the pros and cons. In particular, the following will be covered:

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. 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.