Varada recently announced the release of Varada 3.0, the latest version of the company’s flagship big data analytics offering. Varada’s latest merges cloud elasticity with the query power of indexing so users can scale analytics workloads with speed. Varada 3.0 also eliminates the need for organizations to keep SSD NVMe compute instances idling when the cluster is not in use. After raising $12 million in Series A funding last September, Varada officially unveiled its product on Amazon Web Services.
Varada enables data architects to hasten and optimize workloads via dynamic analysis and adaptive indexing. This technology ensures that queries will run at interactive response time. The platform then automatically analyzes and detects which datasets to accelerate, and then applies the optimal index. Varada transforms data into operational data in real-time, and enables queries to run on any column at its source granularity. Included machine learning optimization tools continuously track cluster performance as well.
Elastic scaling capabilities present in version 3 let users add and remove nodes and clusters quickly depending on current workload needs. When scaling in, indexes and data are not “lost” and continue to be available for other clusters and users. As new indexes are created by the platform, they are also stored in a designated folder on the customer’s data lake (“warm data”), in addition to the cluster’s SSDs in the “hot data” layer. When the cluster is scaled in or eliminated and some nodes are shut down, indexes remain available as warm data.
In a media statement on the news, Varada CEO Eran Vanounou said: “Varada was built on the premise that indexing can transform big data analytics, if done correctly. With version 3.0, the Varada platform is now the most powerful and cost-effective way to leverage the power of big data directly atop your data lake. V 3.0 introduces a new layer to Varada’s platform. We’ve separated the index and data from the SSD nodes, creating a “warm” tier in the data lake that allows us to preserve those indexes much faster and at a much lower cost. By doing so we’re bringing the power of cloud computing scaling to big data indexing.”
Read Elastic Indexing at Petabyte Scale in the company’s blog to learn more.
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