Developers Use In-Memory Computing to Improve Application Performance
Developers must find ways to accelerate new and existing applications, achieve massive database scalability, enable real-time data access across datastores, and use technologies like machine and deep learning to power real-time decision making. Developers 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. We also offer the free online GridGain Control Center for managing, monitoring and developing on GridGain or Ignite. 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, with ANSI-99 SQL and key-value APIs among many others, 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.
Resources
Capital markets applications often require high performance, massive scalability, and high-performance data access across the enterprise to meet the demands of modern, digital business activities. In addition, digital transformations may require capital markets companies to design architectures that enable multiple business applications to access data from multiple, disparate data sources in real time.
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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.
In this webinar, Denis Magda will introduce several architectural techniques that can help you keep your cluster operational and your applications running even if memory becomes a scarce resource. During the webinar demo you will learn how to use those techniques in practice. Topics covered include:
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.
In this webinar, Denis Magda will discuss architectural patterns and design considerations for deploying Apache Ignite in a serverless computing environment. In particular, you will learn the following:
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. Tracing makes it easier to follow the execution path, analyze timings, and align API calls with logs and stack traces. Apache Ignite 2.9 introduces new tracing instrumentation that is based on the OpenCensus/OpenTelemetry standard.
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. This is a result of having the ML processing run on a different system from the operational transactions system in order to avoid a performance degradation.
In this webinar, Glenn Wiebe, GridGain Solution Architect, will introduce developers to Apache Ignite as an in-memory database (IMDB). He will review how an IMDB differs from a cache or an in-memory data grid (IMDG). He will discuss the key characteristics of an IMDB and also highlight core Ignite features and facilities. Finally, Glenn will demonstrate a repeatable and extensible process to:
Managing distributed systems can be complex and time consuming because there are numerous moving parts. Having a consistent set of tools can give a clear picture of how clusters are performing, when to take action and avoid potential problems, and how to optimize configurations.
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.
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.
In this webinar, Denis Magda will discuss architectural patterns and design considerations for deploying Apache Ignite in a serverless computing environment. In particular, you will learn the following:
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.
This webinar will introduce you to the role of the networking layer in distributed systems with Apache Ignite as an example. You will gain practical insights and learn how to maximize the performance and reliability of your applications running on distributed systems. In particular, you will learn:
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. Tracing makes it easier to follow the execution path, analyze timings, and align API calls with logs and stack traces. Apache Ignite 2.9 introduces new tracing instrumentation that is based on the OpenCensus/OpenTelemetry standard.
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.
In this webinar, Glenn Wiebe, GridGain Solution Architect, will introduce developers to Apache Ignite as an in-memory database (IMDB). He will review how an IMDB differs from a cache or an in-memory data grid (IMDG). He will discuss the key characteristics of an IMDB and also highlight core Ignite features and facilities. Finally, Glenn will demonstrate a repeatable and extensible process to:
Join Denis Magda, Apache Ignite veteran and GridGain Head of Developer Relations, as he demonstrates various Apache Ignite capabilities you might have heard of or read about. Topics covered will include clustering, compute and SQL capabilities, data loading and streaming. Denis will break things, emulate bottlenecks and explore unknowns, and discuss how to effectively handle those situations through coding examples. You are encouraged to try these examples on your own following the session.
Managing distributed systems can be complex and time consuming because there are numerous moving parts. Having a consistent set of tools can give a clear picture of how clusters are performing, when to take action and avoid potential problems, and how to optimize configurations.
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.
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.