Category Archives: Impala

What’s Next for Impala: More Reliability, Usability, and Performance at Even Greater Scale

Categories: Impala

This year will close out with new features for reliability, usability, and nested types, and in 2016, performance-related enhancements promise >20x gains.

It’s been roughly a year since we provided an update about the Impala roadmap. During that time, a number of milestones have been reached:

  • Most Cloudera customers have deployed Impala to production across industries including financial services, retail, healthcare, gaming, government, advertising, and telecom.

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Impala Needs Your Contributions

Categories: Community Impala

Your contributions, and a vibrant developer community, are important for Impala’s users. Read below to learn how to get involved.

From the moment that Cloudera announced it at Strata New York in 2012, Impala has been an 100% Apache-licensed open source project. All of Impala’s source code is available on GitHub—where nearly 500 users have forked the project for their own use—and we follow the same model as every other platform project at Cloudera: code changes are committed “upstream”

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How-to: Read FIX Messages Using Apache Hive and Impala

Categories: Hadoop Hive How-to Impala

Learn how to read FIX message files directly with Hive, create a view to simplify user queries, and use a flattened Apache Parquet table to enable fast user queries with Impala.

The Financial Information eXchange (FIX) protocol is used widely by the financial services industry to communicate various trading-related activities. Each FIX message is a record that represents an action by a financial party, such as a new order or an execution report.

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Text Mining with Impala

Categories: Guest Impala Use Case

Thanks to Torsten Kilias and Alexander Löser of the Beuth University of Applied Sciences in Berlin for the following guest post about their INDREX project and its integration with Impala for integrated management of textual and relational data.

Textual data is a core source of information in the enterprise. Example demands arise from sales departments (monitor and identify leads), human resources (identify professionals with capabilities in ‘xyz’), market research (campaign monitoring from the social web),

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Using Apache Parquet at AppNexus

Categories: Guest Impala Parquet Performance

Thanks to Chen Song, Data Team Lead at AppNexus, for allowing us to republish the following post about his company’s use case for Apache Parquet (incubating at this writing), the open standard for columnar storage across the Apache Hadoop ecosystem.

At AppNexus, over 2MM log events are ingested into our data pipeline every second. Log records are sent from upstream systems in the form of Protobuf messages. Raw logs are compressed in Snappy when stored on HDFS.

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