On Wednesday, Snowflake Computing announced enhanced automation for its cloud data warehouse that specializes in storing and analyzing structured and semi-structured data. Snowflake’s Elastic Data Warehouse now boasts improved capabilities for automated scaling to accommodate dramatic increases in concurrency, thereby preserving the speed and optimization of queries in scenarios marked by spikes in storage capacity, user activity and writes to the database more generally. The platform’s augmented automated scaling functionality extends to the management of distributed data and metadata by allowing customers to ensure optimal query performance without manual re-distribution of data fields and taxonomies as the database scales. In addition, Snowflake’s data warehouse optimizes dashboard and reporting capabilities by streamlining the results of frequently performed queries and accelerating the query results that they deliver. Moreover, the company’s Elastic Data Warehouse allows customers to travel back in time to customer-defined milestone points in the history of the database, thereby empowering customers to expeditiously recover data or retrieve data history as a point of comparison. Snowflake Computing’s ability to “milestone” the database enables it to deliver high availability and disaster recovery and thereby ensure business continuity for applications that leverage mission-critical data stored within its infrastructure.
Taken together, the July 20 product-related enhancements from Snowflake Computing usher in increased automation and performance optimization for its cloud-based data warehouse that boasts the ability to store and query big data, including JSON documents, using SQL. As enterprises increasingly seek cloud-based data warehouses as a solution to manage petabytes of siloed data across a bewildering multitude of hardware and software platforms, expect Snowflake Computing’s Elastic Data Warehouse to expand its footprint in the enterprise data warehouse space given the nexus of its simplicity, richness of functionality and fully managed quality that empower customers to focus on analyzing data instead of managing the operational process that undergirds the storage and retrieval of big data.
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