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Last updated Feb 9, 2024

Dremio revolutionizes self-service analytics for data teams. It empowers data analysts to explore and visualize data with rapid query response times. Concurrently, data engineers benefit from the ability to ingest and transform data directly in the data lake, fully supporting DML operations.

A standout feature is the ability for analysts to join data in the lake with external database data. This integration means there’s no need to move data to object storage for value extraction. Dremio’s open Lakehouse platform, grounded in community-driven standards like Apache Iceberg and Apache Arrow, empowers organizations to utilize top-tier processing engines while avoiding vendor lock-in.

Primarily, Dremio leverages In-Memory Formats for executing queries across diverse data landscapes. This is particularly beneficial for rapid querying and joining of varied data sources. Dremio fits within the Data Virtualization category.

# Older Illustrations

Comparison: traditional approach versus Dremio.

Efficiency in data processing with Apache Arrow:

Advantages of a unified data layer:

# Dremio 2.0

This major release brings a suite of new features, performance enhancements, and stability improvements. Below are the highlights. Join our product team for a live discussion on these features:

Discover more in the release notes: Dremio 2.0 Release Notes. Kelly

References: My Dremio Setup