I'm excited to announce a new GridGain Systems white paper, Choosing the Right In-Memory Computing Solution, that explains the reasons to use in-memory computing and then walks through how to choose the right architecture and feature set to meet your needs.
As in-memory computing (IMC) gains momentum across a wide range of applications, from fintech and e-commerce to telecommunications and IoT, many companies are looking to IMC solutions to help them process and analyze large amounts of data in real time. However, the variety of IMC product categories and solutions can be confusing, making it difficult to determine which solution is best for a particular application.
There are many different terminologies for types of in-memory computing. It’s important to understand where they came from, their capabilities, and how they can be unified into a complete in-memory computing platform. It's also important to understand the strengths and weaknesses of each type and how it can be best applied to solve your business problems.
In-memory computing products generally fit into the following four categories:
• In-memory database options that are in-memory extensions to existing disk-based databases.
• In-memory databases that are databases that store data in memory instead of on disk.
• In-memory data grids that are distributed in-memory key-value stores.
• In-memory computing platforms that are complete in-memory solutions including a data grid and multiple fully integrated in-memory components providing a feature-rich in-memory experience.
The white paper features a table that summarizes the different types of solution, its advantages and disadvantages, and what it is best suited for.
In-Memory Computing Solution Options
With companies grappling with the challenges resulting from increasing fast-data and big-data workloads, demand for the GridGain in-memory computing platform is growing dramatically. This comprehensive platform contains a complete feature set that surpasses the capabilities of in-memory database point solutions, making it well suited for OLAP, OLTP and HTAP use cases.
As a complete in-memory computing platform, GridGain helps users consolidate onto a single high performance and highly scalable big-data solution for transactions and analytics, resulting in lowered TCO. Advanced SQL functionality and API-based support for common programming languages enable rapid deployment. This, coupled with the rapidly decreasing cost of memory, boosts ROI for in-memory computing initiatives, enabling businesses to build less expensive systems that perform thousands of times better.
The key modules of the GridGain in-memory computing platform are:
- Data grid – Essentially an in-memory key value store that can be queried
- SQL grid - provides the ability to interact with data in-memory using ANSI SQL-99 via JDBC or ODBC APIs
- Compute grid - A stateless grid that provides high-performance computation in memory using clusters of computers and parallel processing
- Service grid - A service grid in which grid service instances are deployed across the distributed data and compute grids
- Streaming – The ability to consume an endless stream of information and process it in real-time
- Advanced clustering – The ability to automatically discover nodes, eliminating the need to restart the entire cluster when adding new nodes
Choosing the Right In-Memory Computing Solution
If your organization is evaluating different types of in-memory technologies, please download Choosing the Right In-Memory Computing Solution, a new GridGain Systems white paper that takes a detailed look at how in-memory computing can deliver the performance and scale business need today and tomorrow.