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On Demand Webinar

High Performance Cloud Data Warehouse Vendor Evaluation

This free 1-hour webinar from GigaOm Research brings together GigaOm analyst William McKnight and special guest, Actian’s SVP of Engineering, Emma McGrattan to discuss the intriguing results from an in-depth GigaOm Analytic Field Test derived from the industry-standard TPC Benchmark™ H (TPC-H) to compare five leading cloud data warehouse offerings: Actian Avalanche, Amazon Redshift, Azure Synapse, Snowflake, and Google BigQuery.

Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high-speed to derive timely insights. Data volumes within modern organization’s information ecosystems are rapidly expanding—placing significant performance demands on legacy architectures. Today, to fully harness their data to gain a competitive advantage, businesses need modern, scalable architectures and high levels of performance and reliability to provide timely analytical insights. In addition, many companies are attracted to fully-managed cloud services.

In this 1-hour webinar, you will discover:

  • How cloud databases are a way for enterprises to avoid large capital expenditures, provision quickly, and provide performance at scale for advanced analytic queries
  • How these leading cloud data warehouses compare on both performance and cost across a broad spectrum of real-world query use cases
  • How user concurrency can materially impact both performance and cost
  • Who consistently outperformed the competition in the test

Register To Watch Now


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Why Attend?

This webinar focuses on relational analytical databases in the cloud because deployments are at an all-time high and poised to expand dramatically. The cloud enables enterprises to differentiate and innovate with these database systems at a much more rapid pace than was ever possible before.

Register now to join GigaOm and Actian for this free expert webinar.

Who Should Attend:
• Data Architects
• Data Strategists
• Data Analysts
• IT Leadership
• Data Engineers
• Chief Data Officer & Team
• Application Owners