Architects Use GridGain to Accelerate and Scale-Out Applications
Enterprise architects need to speed up and scale out new or existing enterprise applications to drive new digital initiatives. Digital transformation requires a new generation of business applications that ingest, process and analyze data in real-time to drive optimal user experiences. With GridGain, you can create modern, flexible applications built on an in-memory computing platform that can scale with your business needs.
GridGain In-Memory Computing Solutions
GridGain offers enterprise architects the in-memory computing platform software, support, and professional services they need to better achieve real-time digital business goals. The GridGain in-memory computing platform easily integrates with 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 features and abilities that span a wide variety of use cases and industries.
How GridGain Helps Enterprise Architects
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 existing applications and databases. GridGain can be deployed anywhere including on-premises, on a public or private cloud, or in a hybrid environment.
Learn About GridGain In-Memory Computing Solutions for Enterprise Architects
The white papers, webinars, application notes, product comparisons, and videos below discuss use case considerations from an architectural standpoint.
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This white paper discusses how to accelerate Apache® Cassandra™ and improve Cassandra performance. Apache Cassandra is a popular NoSQL database that does certain things incredibly well. It can be always available, with multi-datacenter replication. It is also scalable and lets users keep their data anywhere. However, Cassandra is lacking in a few key areas – particularly speed. Because it stores data on disk, Cassandra is not fast enough for some of today’s extreme OLTP workloads.
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, Ivan Rakov, Apache Ignite Committer, 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:
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:
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:
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.
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. Inserted between existing application and data layers, in-memory computing platforms can improve application performance up to 100x and enable massive application scalability.
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:
This product comparison describes the advantages and benefits of migrating from DataSynapse to GridGain as an in-memory computing solution to power mission-critical and data-intensive applications.
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.
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.