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 results.

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:

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

It included examples of configurations for:

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.

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 use cases. Topics covered include:

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.

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. In this webinar, we will consider real-world examples and discuss pros and cons of each approach, especially:

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. Throughout the webinar we also discussed service grid basic principles and internal implementation details that helped attendees better understand the product capabilities and build successful applications on top of Ignite.

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?