Gnarly Data Waves

Episode 48

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March 26, 2024

Understanding the Dremio Data Lakehouse

Join this session and learn a comprehensive talk that delves into the evolution of data analytics, culminating in the data lakehouse model with Dremio at its core. The session will feature a live demonstration of Dremio's capabilities, showcasing how it streamlines the journey from data storage to insightful analysis, thereby facilitating a win-win scenario for data engineers and analysts alike.

We will embark on a journey that begins with a brief history of data analytics, tracing its development through the advent of the data lakehouse concept. This exploration sets the stage for a deeper understanding of the unique position Dremio occupies within this ecosystem, highlighting its innovative approach to bridging the gap between vast data lakes and the analysts striving to extract actionable insights.

The core of this presentation features a live demonstration, showcasing the end-to-end process of data connection and evaluation within the Dremio platform. Attendees will witness firsthand how Dremio facilitates a seamless flow of data from storage in data lakes to its transformation into a format ready for analysis, ultimately culminating in the delivery of valuable insights to analysts. This demonstration not only illustrates Dremio’s capabilities but also emphasizes its role in enabling a win-win scenario for both data engineers and analysts, by simplifying access to data and enhancing the efficiency of the analytics process.

In this session, we’ll cover:

  1. A short overview of the power of Dremio
  2. What is a semantic layer and why you need it
  3. Why Dremio is faster than anything else

Watch and gain a deeper understanding of the Dremio Data Lakehouse and discover how it can revolutionize your approach to data analytics, from enhancing data accessibility to streamlining the journey from raw data to actionable insights.

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What is Apache Polaris

Apache Polaris is an open-source catalog service purpose-built for managing Apache Iceberg tables in a distributed, multi-engine environment. At its core, Polaris implements the Iceberg REST Catalog API, providing a standardized, cloud-native method for connecting query engines with Iceberg metadata, without requiring tight coupling to storage systems.

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Accelerate AI and Analytics with Dremio

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Delivering Data Products with a Lakehouse Architecture: Insights from BARC & Dremio

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