Category Archives: Impala

New in Cloudera Enterprise 5.5: Support for Complex Types in Impala

Categories: Impala Parquet

The new support for complex types in Impala makes running analytic workloads considerably simpler.

Impala 2.3 (shipping starting in Cloudera Enterprise 5.5) contains support for querying complex types in Apache Parquet tables, specifically ARRAY, MAP, and STRUCTs. This capability enables users to query against naturally nested data sets without having to perform ETL to flatten them. This feature provides a few major benefits, including:

  • It removes additional ETL and data modeling work to flatten data sets.

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Introducing Cloudera Navigator Optimizer: For Optimal SQL Workload Efficiency on Apache Hadoop

Categories: Impala Performance

Cloudera Navigator Optimizer, a new (beta) component of Cloudera Enterprise, helps optimize inefficient query workloads for best results on Apache Hadoop.

With the proliferation of Apache Hadoop deployments, more and more customers are looking to reduce operational overheads in their enterprise data warehouse (EDW) installations by exploiting low-cost, highly scalable, open source SQL-on-Hadoop frameworks such as Impala and Apache Hive. Processing portions of SQL workloads better suited to Hadoop on these frameworks,

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Impala’s Next Step: Proposal to Join the Apache Software Foundation

Categories: Impala Kudu

The Impala project has already passed several important milestones on the way to its status as the leader and open standard for BI and SQL analytics on modern big data architecture. Today’s milestone is the submission of proposals for Impala and Kudu to join the Apache Software Foundation (ASF) Incubator.

[Update: Read the text of the Impala and Kudu proposals here and here, respectively.]

Since its initial release nearly five years ago,

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How-to: Ingest and Query “Fast Data” with Impala (Without Kudu)

Categories: Hadoop How-to Impala Kudu

Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running queries.

In this blog post, you will learn an approach for continuous loading of data into Impala via HDFS,

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How-to: Use Impala with Kudu

Categories: How-to Impala Kudu

Learn the details about using Impala alongside Kudu.

Kudu (currently in beta), the new storage layer for the Apache Hadoop ecosystem, is tightly integrated with Impala, allowing you to insert, query, update, and delete data from Kudu tablets using Impala’s SQL syntax, as an alternative to using the Kudu APIs to build a custom Kudu application. In addition, you can use JDBC or ODBC to connect existing or new applications written in any language,

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