Developers Use In-Memory Computing to Accelerate Application Performance
Developers must find ways to accelerate new and existing applications, achieve massive scalability, and use technologies like machine and deep learning to power real-time decision making. Developers must help their companies meet competitive demands to accelerate digital transformations, build out their applications quickly, and get it right the first time.
GridGain In-Memory Computing Solutions
GridGain offers developers the in-memory computing platform software, support, and professional services you need to better achieve your real-time digital transformation technical challenges. The GridGain in-memory computing platform easily integrates with your new or existing applications and provides real-time performance and massive scalability.
Built on the open source Apache Ignite project, GridGain is a cost-effective solution for accelerating and massively scaling out new or existing applications with users that span a wide variety of use cases and industries.
Developers Need the GridGain In-Memory Platform
For existing applications, GridGain is typically used as an in-memory data grid between the application and data layer, with no rip-and-replace of the underlying database. For new applications, GridGain is used as an in-memory data grid or an in-memory database. A Unified API, including ANSI-99 SQL and ACID transaction support, provides easy integration with your new or existing code, enabling you to create modern, flexible applications built on an in-memory computing platform which will grow with your business needs. Thin and thick clients are available which support a wide variety of protocols including SQL, Java, C++, .NET, PHP, Scala, Groovy and Node.js.
How GridGain In-Memory Computing Solutions Help Developers Accelerate Applications
The white papers, webinars, application notes, product comparisons, and videos below discuss provide developers with various technical in-memory computing development examples.
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Learn some of the best practices and the different options for maximizing availability and preventing data loss. This session explains in detail the various challenges including cluster and data center failures, and the best practices for implementing disaster recovery (DR) for distributed in-memory computing based on real-world deployments. Topics include:
This webinar discusses deploying Apache Ignite into production in public and private clouds. Companies have faced many challenges when deploying in-memory computing platforms such as Apache Ignite in the cloud, but they have also discovered many best practices that have made success possible.
In this webinar Alexey Zinoviev, Apache Ignite ML contributor for GridGain will talk about new 2.7 release of Apache Ignite and present the new features that are added to Ignite ML modules.
In the second phase of his presentation he will introduce what a Java programmer needs to do and understand in a typical Big Data and ML projects.
In this webinar you will learn:
- How to choose features
- How to encode features
- How to scale
- How to clear and fill in the missed values
- How to evaluate the quality of the model
In this presentation, attendees will learn about Apache Ignite and the GridGain in-memory computing platform, which is built on Apache Ignite, and about the key capabilities and features important for financial applications, including ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance, fraud detection and more.
If your company is one of the tens of thousands of organizations that use Apache® IgniteTM or GridGain® Community Edition in a production environment, GridGain Basic Support can provide you with peace of mind that you have a trusted partner to help keep your environment running flawlessly. The service includes....
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Redis Enterprise (and their respective open source projects where relevant) compare in 25 categories.
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Hazelcast (and their respective open source projects where relevant) compare in 25 different categories.
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Oracle Coherence (and their respective open source projects where relevant) compare in 25 different categories.
When you add a distributed in-memory computing cluster to support existing systems or new APIs, you introduce additional moving parts that can be hard to track and troubleshoot for performance issues or failures.
Learn how the veterans monitor various components of a distributed cluster for network, memory, or node-specific issues, and troubleshoot to resolve issues. By the end of this session you'll have a handy checklist and set of tools to consider using for your own deployments.
This session will cover:
This webinar discusses how an in-memory computing platform such as GridGain or Apache Ignite can modernize existing data lake architectures, enabling real-time analytics that spans operational, historical, and streaming data sets.
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.
Apache Ignite is (an in-memory computing platform OR an in-memory distributed data store and compute grid) with full-fledged SQL, key-value and processing APIs. Many companies have added it as a cache in-between existing SQL databases and their applications to speed up response times and scale. In other projects they've used it as its own SQL database.
This session will dive into some of the best practices for both types of projects using Apache Ignite.
Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers can find hidden insights without the help of explicit programming. These insights bring tremendous benefits to many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats.
If you are trusting a single datacenter to support your newest mission critical or cutting edge in-memory computing application, you may want to reconsider your strategy. No datacenter is 100% secure against natural disasters, hackers or just plain old human error. In order to maintain all the 9s of availability that you have promised, you need to hedge your bets on an active - active or active - passive set up. The GridGain Multi-Datacenter Replication feature makes doing this a snap.
In this webinar, GridGain System’s Solution Architect Dani Traphagen will walk through the basics of a Kubernetes and Apache Ignite deployment, including:
Over the last decade, the 10x growth of transaction volumes, 50x growth in data volumes, and drive for real-time response and analytics has pushed relational databases beyond their limits. Scaling an existing RDBMS vertically with hardware is expensive and limited. Moving to NoSQL requires new skills and major changes to applications. Ripping out the existing RDBMS and replacing it with another RDBMS with a lower TCO is still risky.