Read the full post by Matthew Thomson Senior Product Manager, Azure Big Compute, on the Azure Blog.
In-memory computing has accelerated the big compute capabilities and enabled customers to extend its experience beyond just the monte carlo simulation and into the analytics. This is of note within Financial Services where business users wish to move away from pre-canned reports and instead directly interact with data. With Azure, banks can analyze in real-time and make the right decisions intraday and be more equipped to meet the regulatory standards. This blog will look to explore the new possibilities of a scale out architecture for in-memory analytics in Azure through ActivePivot. ActivePivot is part of the ActiveViam platform that brings big compute and big data closer together.
ActivePivot is an in-memory database that aggregates large amounts of fast-moving data through incremental, transactional, and analytical processing to enable customers to make the right decisions in a short amount of time. ActivePivot computes sophisticated metrics on data that is updated on the fly without the need for any pre-aggregation and allows the customer to explore metrics across hundreds of dimensions, analyze live data at its most granular level, and perform what-if simulations at unparalleled speed.
For customers to enable this on-premise, purchasing servers with enough memory can be expensive and is often saved for mission critical workloads. However, the public cloud opens this up to more workloads for research and experimentation, and taking this scenario to Azure is compelling. Utilizing Azure blob storage to collect and store the historical datasets generated over a period of time allows the customer to use the compute only when the user requires it. Starting from scratch to being fully deployed in less than 30 minutes drastically reduces the total cost of ownership and provides enormous business agility.
