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 a petabyte scale for a variety of workloads.
The latest release of the GridGain in-memory computing platform features enhanced support for the platform’s multi-tier database engine, that scales up and out across memory and disk.
Typically, operations exceed fifty percent of the cost of an IT system’s life cycle. By developing applications that can be easily managed, developers can significantly reduce the cost of ownership. Manageability is especially important for distributed applications because they are especially complex and often mission-critical.
Kafka with Debezium and GridGain connectors enables change data capture (CDC) based synchronization between third-party databases and GridGain clusters. Synchronization that is based CDC does not require coding; all it requires is to prepare configuration files for each of the points.
The GridGain Operator for Kubernetes enables you to deploy and manage Apache Ignite and GridGain clusters efficiently. The automation that Kubernetes and the Operator provide simplifies provisioning and minimizes the operational and management burden.
During this webinar, we review various approaches to storing and using authentication data in distributed applications. Moving from the simplest to the most complex models, we consider the pros and cons of each approach. We give special attention to one of the most popular approaches to distributed sessions—single sign-on.
Apache Ignite can scale horizontally to accommodate the data that your applications and services generate. However, in practice, most of us cannot scale out a cluster instantly.
Serverless computing allows you to design and build scalable cloud-native applications without thinking about infrastructure provisioning and orchestration. With Apache Ignite, you can bootstrap an in-memory cluster in the cloud and access data 100-1000x faster than with disk-based databases.
Networking is a core pillar of any distributed system and is responsible for cluster-node discovery procedures, peer-to-peer communication between nodes, and failure handling. Network architecture can greatly influence operational performance and efficiency.
Apache Ignite and GridGain can be used as a simple cache, an in-memory data grid (IMDG), and as an in-memory database (IMDB). These data management patterns can be combined with Ignite integration facilities to function as a Digital Integration Hub (DIH) for real-time data access across data sources and applications. Common uses for the DIH architecture include:
Debugging distributed-system applications can be more complex than debugging traditional monolithic applications. As API calls jump across nodes in the cluster, it can be tricky to follow the execution just by analyzing application logs. Tracing adds a useful tool to the root-cause-analysis toolbox.
Growing customer traffic, the launch of new services, seasonal traffic spikes or a host of other load-related issues can slow down customer-facing applications, resulting in poor customer experiences. In-memory computing offers a solution to overcome performance-related challenges and deliver outstanding customer interactions.
Apache Ignite is an excellent tool for external RDBMS, NoSQL or Hadoop database acceleration and offloading. The Ignite in-memory computing platform can power real-time applications that need to process terabytes of data with in-memory speed. Join us for this webinar to learn about the various Ignite deployment options for database acceleration including:
Join us for a special webinar presented by Branimir Angelov, Co-Founder and CTO of Kubo, Software Architecture Consultant in Obecto, and Member of the Comrade Cooperative.
Many machine learning (ML) and deep learning (DL) platforms are slow in production environments. It can sometimes take hours or days to update ML models.