Dremio Shares Latest Innovations in Data Lakes on AWS and Azure at Strata Data Conference 2019
Dremio, the data lake engine company, today announced its executives will present at Strata Data Conference, held at Javits Center in New York, NY, September 23-26. Dremio will be in booth #1243 showcasing its Data Lake Engines for AWS, Azure, and on-premises. Dremio’s open source platform includes advanced columnar caching, predictive pipelining, and an execution engine kernel that delivers up to 70x increases in performance — operating directly against data lake storage.
Data lakes have become a key ingredient in the data architecture of most companies. In the cloud, object storage systems such as S3 and ADLS make it easier to operate a data lake. However, there are still a number of key challenges when it comes to building a cloud-based data lake.
Tomer Shiran, co-founder and CEO and Jacques Nadeau, co-founder and CTO, will jointly present a talk on, “Building a Best-in-Class Data Lake on AWS and Azure.” The presentation will describe how companies are building data lakes in the cloud using S3 and ADLS as storage layers, while leveraging multiple processing engines to address various needs including batch processing, ad-hoc data exploration, reporting and ML/AI.
In addition, the talk will explore best practices and provide several real-world examples from different industries and discuss the significance of Apache Arrow to the future of the heterogeneous data lake.
Conference Session Highlights Details:
When: Thursday, September 26 at 2:05 PM
Where: 1E 09
What: Tomer Shiran, co-founder and CEO and Jacques Nadeau, co-founder and CTO will present “Building a Best-in-Class Data Lake on AWS and Azure.” During this session, attendees will learn how to build a cloud data lake on AWS and Azure.
Tweet this: .@Dremio to present on #datalakes #azure and #AWS at @strataconf #stratadata
Dremio’s Data Lake Engine delivers fast query speed and a self-service semantic layer operating directly against data lake storage. Dremio eliminates the need to move data to proprietary data warehouses or create cubes, aggregation tables and BI extracts, providing flexibility and control for Data Architects, and self-service for Data Consumers. For more information, visit www.dremio.com.