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Slides: How Columnar Databases Support Modern Analytics

By   /  August 9, 2018  /  No Comments

To view the On Demand recording from this presentation, click HERE>>

This webinar is sponsored by:

About the Webinar

The increased requirements of modern analytical workloads – querying billions of rows on demand, in real time and in unforeseen ways – is a challenge for traditional databases because they’re optimized for transactional workloads (e.g., point and range queries with indexes).

A transactional query may return every column in a single row whereas an analytical query may aggregate a single column in every row. Thus, it is far more efficient to store data by column rather than by row. In addition, the use of distributed data and massively parallel processing enables columnar databases to support scalable, high-performance analytics.

In this webinar, we will use the architecture of MariaDB AX to explain how columnar storage and massively parallel processing work, and how they enable columnar databases to query billions of rows in real time, and with the full power of SQL – a challenge for Apache Hadoop/Hive.

About the Speaker

Shane Johnson

Senior Director of Product Marketing

Shane Johnson.pngPrior to MariaDB, Shane led product and technical marketing at Couchbase. Before that, he performed technical roles in development, architecture and evangelism at Red Hat – specializing in Java and distributed systems.

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