The rise of cloud data lake storage (e.g., S3, ADLS) as the default bit bucket in the cloud, combined with the infinite supply and elasticity of cloud compute (e.g., EC2, Azure VMs), has ushered in a new era in data analytics architectures. In this new world, data can be stored and managed in open source file and table formats, such as Apache Parquet and Apache Iceberg, and accessed by best-of-breed elastic compute engines such as Dremio, Databricks and EMR. As a result, companies can now avoid becoming locked into monolithic systems such as cloud data warehouses and Hadoop distributions, and instead enjoy the flexibility of using the best of breed technologies of today and tomorrow. In this session we explore these secular trends and the building blocks that have come together to enable this new open architect.
Tomer Shiran
Tomer Shiran is the CPO and founder of Dremio. Prior to Dremio, he was VP Product and employee #5 at MapR, where he was responsible for product strategy, roadmap, and new feature development. As a member of the executive team, Tomer helped grow the company from five employees to over 300 employees and 700 enterprise customers. Prior to MapR, Tomer held numerous product management and engineering positions at Microsoft and IBM Research. He holds a master’s degree in electrical and computer engineering from Carnegie Mellon University and a bachelor’s in computer science from Technion – Israel Institute of Technology, as well as five U.S. patents.
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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