Digital transformation, data democratization, and cloud modernization are fueling the rise of cloud data platforms. By running SQL workloads directly on cloud-native object storage, the cloud data platform seeks to merge the best of data warehouse and data lake technologies. Enterprise BI analysts and data scientists alike consume data from these repositories to answer their growing analytics needs.That’s why enterprises adopt cloud data platforms. But how do their data teams actually achieve those goals? They face obstacles such as complex data warehouse structures, slow performance, and siloed datasets. Data engineers in particular struggle to simplify, accelerate, and consolidate analytics workloads on cloud data platforms. This webinar provides guiding principles to overcome the obstacles.Join Eckerson Group and Dremio to learn how your data team can:
Moonfare replaced a PostgreSQL-based data warehouse on Amazon Web Services (AWS) with a Dremio data lakehouse to offer data engineers, analysts and business users a high performance platform for business intelligence and predictive analytics empowering them to make better data-driven decisions.
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Deutsche Bahn Group (DB) is one of the world's leading mobility and logistics companies. The DB Cargo business unit manages DB's rail freight business.
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Amazon's Supply Chain Finance Analytics team developed a new analytics architecture with Dremio to simplify ETL processes, accelerate queries, and provide analytics on a unified view of the data.
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