Author Archives: Marcel Kornacker

Analytics and BI on Amazon S3 with Apache Impala (Incubating)

Categories: Cloud Impala Ops and DevOps Performance

Thanks to new optimizations for running Impala on Amazon S3, doubling cluster size on AWS doubles multi-user performance while keeping total workload cost roughly the same.

With public-cloud deployments becoming increasingly popular, Cloudera is continuing to build out the capabilities of its platform to best take advantage of the cost-effective and flexible nature of the cloud. The current release of Cloudera’s platform (5.8) includes a major step forward in that area with Impala 2.6 able to store and query data directly from the Amazon S3 object store.

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BI and SQL Analytics with Apache Impala (Incubating) in CDH 5.8: 3x Faster on Secure Clusters

Categories: CDH Impala

Released with CDH 5.8, Impala 2.6 brings solid performance improvements, particularly for clusters secured by Kerberos running BI workloads on Apache Hadoop.

Just a few months back, we showed you how Impala 2.5 delivered a 4x performance boost compared to Impala 2.3 for BI workloads on Hadoop via the introduction of several features like runtime filters. Here’s an update: Compared to two releases ago, Impala 2.6 delivers 12x better performance on secure workloads and continues this drumbeat of consistent performance improvement.

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Announcing hs2client, A Fast New C++ / Python Thrift Client for Impala and Hive

Categories: Data Science Hive Impala Tools

This new (alpha) C++ client library for Apache Impala (incubating) and Apache Hive provides high-performance data access from Python.

Earlier this year, members of the Python data tools and Impala teams at Cloudera began collaborating to create a new C++ library to eventually become a faster, more memory-efficient replacement for impyla, PyHive, and other (largely pure Python) client libraries for talking to Hive and Impala.

We are excited to release this effort,

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Apache Impala (incubating) in CDH 5.7: 4x Faster for BI Workloads on Apache Hadoop

Categories: CDH Impala Performance

Impala 2.5, now shipping in CDH 5.7, brings significant performance improvements and some highly requested features.

Impala has proven to be a high-performance analytics query engine since the beginning. Even as an initial production release in 2013, it demonstrated performance 2x faster than a traditional DBMS, and each subsequent release has continued to demonstrate the wide performance gap between Impala’s analytic-database architecture and SQL-on-Apache Hadoop alternatives.

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Introducing Apache Arrow: A Fast, Interoperable In-Memory Columnar Data Structure Standard

Categories: Data Science General HDFS Impala Kudu Performance

Engineers from across the Apache Hadoop community are collaborating to establish Arrow as a de-facto standard for columnar in-memory processing and interchange. Here’s how it works.

Apache Arrow is an in-memory data structure specification for use by engineers building data systems. It has several key benefits:

  • A columnar memory-layout permitting O(1) random access. The layout is highly cache-efficient in analytics workloads and permits SIMD optimizations with modern processors. Developers can create very fast algorithms which process Arrow data structures.

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