Tag Archives: impala

Sustained Innovation in Apache Spark: DataFrames, Spark SQL, and MLlib

Categories: CDH Spark

Cloudera has announced support for Spark SQL/DataFrame API and MLlib. This post explains their benefits for app developers, data analysts, data engineers, and data scientists.

In July 2015, Cloudera re-affirmed its position since 2013: that Apache Spark is on course to replace MapReduce as the default general-purpose data processing engine for Apache Hadoop. Thanks to initiatives like the One Platform Initiative,

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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: Cloudera Navigator 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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Cloudera Enterprise 5.5 is Now Generally Available

Categories: CDH Cloudera Manager

Cloudera Enterprise 5.5 (comprising CDH 5.5, Cloudera Manager 5.5, and Cloudera Navigator 2.4) has been released.

Cloudera is excited to bring you news of Cloudera Enterprise 5.5. Our persistent emphasis on quality is especially pronounced in this release, with more than 500 issues identified and triaged during its development.

A highlight of this release is the inclusion of Cloudera Navigator Optimizer (available in limited beta for select Cloudera Enterprise customers;

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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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