Online digital applications are the backbone of our lives. We take for granted that anything we want to do online -whether a simple search on the internet – or an online transaction such as booking a flight or transferring money to a friend – can be achieved within seconds. This real-time availability of online services relies on complex computing processing capabilities, among them in-memory computing, or in-memory analytical processing. In-memory processing is a technology developed to enable very fast computing of data stored in in-memory databases (IMDB). 

In-memory data grids and other caching solutions such as key/value stores are commonly used to accelerate application performance and achieve low latency access to data, which is critical when serving data to digital applications that support real-time transactions.

In order to optimize the response time for data processing, in memory databases (IMDB) store data in memory, rather than on hard disks or SSDs. In-memory databases, and in-memory computing, enable ultra-fast data processing for online digital applications.

In-Memory Computing provides super-fast performance (thousands of times faster) and scale of never-ending quantities of data, and simplifies access to increasing numbers of data sources. By storing data in RAM and processing it in parallel, it supplies real-time insights and provides data that enable businesses to carry out immediate actions and responses. That’s what makes it ideal for implementation in transactional and analytical applications sharing the same data infrastructure (point-of-decision HTAP) and transactional analytics guided by real-time analytics (in-process HTAP).

The Rise of In-Memory Computing

Adoption of In-memory Computing,  also known as IMC, is on the rise. This can be attributed to the growing demand for faster processing and analytics on big data, the need for simplifying architecture as the number of various data sources increases,  and technology enhancements that are optimizing TCO.

The market for in-memory computing was estimated at 11.4B USD in 2020, and is expected to grow at a CAGR of 16.5% by 2025.

The availability of SSD and persistent memory technologies are driving costs down. Consequently, IMC platforms that support these data storage tiers intelligently are helping organizations optimize their TCO while delivering in-memory performance.

Why In-Memory Computing?

To maintain a competitive edge and meet today’s demands for optimal customer experience, enterprises must deal with the constant upsurge of available data and the never-ending demands for better and faster performance.

In-Memory Computing has evolved because traditional solutions, typically based on disk storage and relational databases using SQL query language, are inadequate for today’s analytical and operational data needs which depend on super-fast computing and scaling of data in real-time.

In-Memory Computing: Basic Principles and Significance

In-Memory Computing is based on two main principles: the way data is stored and scalability – the ability of a system, network or process to handle constantly growing amounts of data, or its potential to be elastically enlarged to accommodate that growth. This is achieved by leveraging two key technologies: random-access memory (RAM) and parallelization.

High Speed and Scalability: To achieve high speed and performance, In-Memory Computing is based on RAM data storage and indexing. This results in data processing and querying at more than 100 times faster than any other solution, delivering optimal and uncompromised performance and scalability for any given task.

For scalability – which is essential for big data processing – In-Memory Computing is based on parallelized distributed processing. In contrast to a single, centralized server managing and providing processing capabilities to all connected systems, distributed data processing relies on a network of computers in which multiple endpoints across different locations share computer-processing capabilities.

Real-time Insights:  In-Memory Computing allows for the collocations of business logic, analytics and data that can be ingested from multiple sources (multi-model store). In this way, In-Memory Computing is much more than just producing an analysis much faster than before; it’s about becoming predictive in the analysis itself!

By simultaneously addressing massive amounts of streaming, hot and historical data (as in GigaSpaces’ solution), In-Memory Computing supports the running of real-time advanced analytics and machine learning for instant insights that are immediately leveraged by collocated business logic with the memory fabric. When something happens that can affect business operations, customers’ actions, regulatory compliance, and more, an immediate understanding of the impact and consequences are made available, enabling the provision of an appropriate, real-time response and decision-making.

Furthermore, continuous predictive analysis leveraging the ability to ingest millions of events per second and analyze the data, prevents undesired occurrences from equipment breakdowns, customer churn, cyber-attacks, and more.

Alongside its ability to enhance the performance of analytics, in-memory computing is also key to enabling operational and transactional workloads which rely on the ability to access fresh data in real time. 

Broad Use Cases: In-Memory Computing is applicable for companies dealing with large volumes of data across all segments, especially when online applications are involved. Typical examples include risk and transaction management in banks/financial institutions, fraud detection for payments and in insurance, trade promotion simulations in consumer product companies and real-time/personalized advertising. But In-Memory Computing is applicable for any industry or market where real-time analysis, insights and predictions based on streaming and historical data offer business value, such as geospatial data analysis, predictive maintenance and route optimization in transportation.

Enabling Technology: Many of today’s applications and technologies would not be possible without the integration of In-Memory Computing. Typical examples of this enabling technology include applications implementing blockchain technology (which allows digital information to be distributed but not copied), or applications involving geospatial/GIS processing for transportation (such as real-time directions on traffic congestion, recommended routes and traffic hazards).

The Hybrid Transactional and Analytical Processing (HTAP) Use Case

In contrast to the traditional computing paradigm of moving data to a separate database, processing it and then saving it back to the data store, with In-Memory Computing everything can be placed in an in-memory data grid and distributed across a horizontally scalable architecture. In-Memory Computing helps to eliminate bottlenecks and allows organizations to utilize their data more efficiently and deliver it rapidly to support business applications.