While InterSystems’ IRIS Data Platform has only been generally available for a little more than 18 months, it is based on highly stable and hardened technology. And the vendor continues to expand the platform with plans to add orchestration and management of analytical workflows as well as in-database machine learning tooling, for instance. All of this should give organizations confidence, particularly as they consider investing in mission-critical workloads. While InterSystems’ roots have traditionally been in the healthcare space, our ongoing coverage of the company reveals continued traction in other sectors such as financial services, logistics, logistics/supply chain and manufacturing. With organizations realizing the opportunities that exist with hybrid workloads (transactional and analytical), we expect a burgeoning market here, but InterSystems, which is looking to raise its profile in other segments, will certainly find stiff competition from relational
InterSystems is seeing strong adoption of its IRIS Data Platform, which was made available a year and a half ago and is capable of handling transactional and analytical workloads. But the company is not stopping there and plans to expand the platform with additional functionality, including introducing in-database embedded machine learning tooling.
In our previous coverage of InterSystems, we discussed the rollout of its IRIS Data Platform, first announced in September 2017 and then made generally available in January 2018. The company pitches the IRIS Data Platform as a highly flexible offering that can handle both transactional and analytical workloads for data-heavy applications.
When the IRIS Data Platform first came out, it was designed to combine two of InterSystems products into one platform: Caché, a NoSQL-like object database, and Ensemble, a rapid integration and development platform. The company reports strong adoption of the platform since its release, particularly among existing Caché and Ensemble customers.
Adoption of the IRIS Data Platform is being fueled by several factors. One factor is a tighter, deeper integration between Caché and Ensemble that drives certain system efficiencies while also enabling a simplified process for handling updates and upgrades. Cloud has also been a strong driver because the platform was architected with the cloud in mind and management reports several clients running it in production on AWS, Microsoft Azure and Google Cloud Platform (GCP), including some running hybrid (on-premises and cloud) deployments.