Technical Presentations About GridGain and Apache Ignite

The technical presentations below 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, data lake acceleration, machine learning, deep learning, and how to apply these technologies and others to power your organizations’ digital transformation. These technical presentations have been developed and presented by GridGain in-memory computing experts.


Learn how companies have added speed and scale to PostgreSQL deployments for different use cases. This presentation will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.
In this presentation, Yuri Babak, the head of ML/DL framework development at GridGain and major contributor to Apache Ignite, will explain how ML and DL work with Apache Ignite, and how to get started. Topics include:
This presentation will show how to add speed and scale to your Oracle Database, Oracle-based applications, APIs and analytics for different use cases. It will discuss when each option makes sense, as well as how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.
In this presentation, we will cover the specifics of how to use Node.js with Ignite, including:
This presentation will help you understand best practices about how to configure Ignite and GridGain for deployment, management and monitoring; leverage log files for troubleshooting, and fix and identify top errors in your deployment.
This presentation discusses the new features and functionality of Apache Ignite. Topics covered include: Transactional SQL Deep learning with TensorFlow Thin client support for Node.js, Python, PHP Transparent encryption for Ignite persistence
This presentation covers best practices for delivering new applications and APIs with in-memory computing, and how it helps open up existing systems, become more agile, and deliver unlimited speed and scale.
This presentation explains the best practices for adding speed and scale to existing applications that offer the least disruption and help meet the long term goals of transforming the business.
Learn how companies have been using Apache® Ignite™ to add in-memory speed and unlimited horizontal scale to SQL with no rip-and-replace of the underlying database. This session will explain how to:
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This presentation covers some of the best practices companies have used for making Apache Ignite and Apache Kafka scale. Making stream processing scale requires making all the components—including messaging, processing, storage—scale together. 
With real-time streaming analytics there is no room for staging or disk.  Learn the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite™, GridGain®, Kafka™, Spark™ and other technologies. 
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This presentation will provide a better understanding about best practices for designing multi-cluster in-memory computing applications. It covers....
This 60-minute hands-on session will: Explain how GridGain Cloud works Walk through step-by-step how to create an account, start a cluster, load and query data using ANSI-99 SQL Demonstrate how to build applications that accesses data using a REST API, ODBC/JDBC or a binary thin client
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Learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache Ignite as an example.
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When deployed properly, it's hard to beat a horizontal distributed architecture's scalability, availability and reliability. The trick is deploying it properly to compensate for individual node, data or network failures. Learn some of the best practices for setting up your clusters properly to maximize availability, reliability and flexibility for future needs.
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