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Deep Segment Reader.

Enable your data science team with easy access to all historical data on demand using our open-source Spark Druid Segment Reader.

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Enable your data science teams.

Let your data science team do what they do best using the tools they love.

This is exactly why we built the open-source tool, based on our team's own experience.

The Challenge: reducing unnecessary pressure on our Druid from ad-hoc data analyses by our data science team.

Our Solution: Since the majority of these jobs could be done in the more familiar environment of Spark, we transferred the jobs there and freed up our Druid cluster.
Apache Druid Segment Reader for Spark Architecture

Reduce storage & compute costs.

Don't settle for inefficient solutions.

The most typical solution is usually to copy the data as a DataFrame on HDFS, but this requires duplication of both the storage and indexing processes, quickly becoming an operational burden and a significant cost.

The Druid Segment Reader eliminates the need for data duplication.
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use Spark and Druid seamlessly!

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