It increasingly seems that every business wants to become a data-driven software company. The success of Airbnb, Alibaba, Netflix and many others has CEOs, CIOs, and CDOs jumping on the digital transformation bandwagon and imagining all the possible ways they can leverage their intellectual property and unique data to deliver a service instead of just shipping products. A builder of engine parts can deliver real-time monitoring of the health of installed parts. A manufacturer of computer printers can monitor ink levels and automatically ship refills. A maker of sprinkler system timers can monitor weather and soil conditions in order to optimize water usage.
No matter how great the idea, however, executing it often involves significant challenges. This can especially be the case when customer growth leads to ever-growing amounts of data that needs to be collected and analyzed in real time. The failure to plan ahead for cost-effectively scalability can be a critical threat to the business model, leading inevitably to customer frustration and churn. The best way to avoid this dilemma is to deploy a scalable, next generation architecture which can grow seamlessly to meet expanding need. And a smart way to cost-effectively deploy a scalable architecture is by building an infrastructure that uses open source software and commodity hardware.
The strategy that many companies have found to achieve this may surprise you. It’s in-memory computing. Many system designers still believe that in-memory computing is too expensive for most use cases, but this is no longer true. With the steady decline in costs, memory is now only slightly more expensive than disk-based storage. And next generation, memory-centric platforms can future proof today’s solutions against tomorrow’ challenges. By eliminating latency and dramatically improving application performance, today’s leading open source in-memory computing platforms offer an exceptional value proposition and can be considered for almost any type of digital transformation initiative. And industry leading, tiered-memory solutions can ensure that the scale as well as the performance of the system can be easily controlled far into the future while allowing users to take advantage of any of a number of storage technologies including spinning disks, solid state drives (SSDs), Flash, or 3D XPoint.
Inserted between the application and data layers, in-memory computing platforms support massive parallel processing across a highly available, distributed computing cluster with ACID transaction support. This enables simultaneously transacting and analyzing huge amounts of data in real-time - a key requirement of most digital transformation projects.
In a typical application deployment, the underlying RDBMS, NoSQL or Apache Hadoop database is kept in the RAM of the distributed cluster built with commodity hardware. Keeping the data in RAM provides a significant performance boost. Further, leading in-memory computing platforms make it easy to scale - another requirement of digital transformation projects - by automatically utilizing the RAM of new nodes added to the cluster and rebalancing the dataset across the nodes, which also ensures high availability.
The latest generation of open source in-memory computing platforms has introduced new memory-centric architectures that provide the optional ability to leverage additional data management capabilities. In new persistent storage architectures, the full dataset can be maintained on disk while a subset of the data is kept in various tiered memory layers which trade off cost and performance. Transaction processing and analytics can be performed across the entire dataset, no matter whether the relevant data is in-memory or only on disk. This new strategy enables organizations to establish the exact balance they want between performance and cost while obtaining all the benefits of a distributed, transactional SQL database that can be scaled out across thousands of servers.
In-memory computing platforms can also support hybrid transactional/analytical processing (HTAP) use cases. HTAP can be especially relevant for Internet of Things (IoT) applications requiring real-time analysis of sensor and other external data sources. HTAP provides the ability to perform analytics in real-time on the operational dataset in a single unified OLTP and OLAP environment without impacting system performance.
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