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Dremio enables live, interactive queries directly against data lake storage, including Amazon S3, Azure Data Lake Storage (ADLS) and Hadoop (HDFS), and provides a semantic layer that makes the data consumable, consistent and secure. With Dremio, there’s no need to copy or move data to a data warehouse, or create cubes, extracts and aggregation tables to optimize query performance.
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Cloud data lake storage has become the destination of choice for storing high volumes of data. However, to analyze that data, it first needs to be moved and copied into proprietary data warehouses – a process that can become costly, complex, risky and inflexible.
Dremio is an engine that sits between data lake storage and end users who want to directly query that data for high-performing dashboards and interactive analytics, without the need for copying data into data warehouses.
1. Lightning-fast queries, directly on data lake storage. Dremio technologies like data reflections, columnar cloud cache (C3), and predictive pipelining work alongside Apache Arrow to execute queries on data lake storage at interactive speed.
2. A self-service semantic layer. This abstraction layer enables line of business owners and IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets.
3. Flexibility and openness. Dremio enables organizations to avoid vendor lock-in, query across clouds, and keep data in storage that they control.
4. Significant infrastructure cost savings. Dremio combines query acceleration and highly elastic compute resources to provide significant performance and cost benefits.
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