Author Archives: Greg Rahn

A Technical Overview of Cloudera Altus Analytic DB

Categories: Altus Analytic Database Cloud

A few weeks back, we announced the upcoming beta of Cloudera Altus Analytic DB for cloud-based data warehousing. As promised, the beta is now available and we wanted to spend some time describing the unique architecture.

Architecture of Cloudera Altus Analytic DB

Altus Analytic DB is built on the Cloudera Altus platform-as-a-service foundation, which also supports the Altus Data Engineering service. The architecture of Cloudera Altus is based around a few simple but important premises —

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Faster Performance for Selective Queries

Categories: CDH Impala

One of the principal features used in analytic databases is table partitioning. This feature is so frequently used because of its ability to significantly reduce query latency by allowing the execution engine to skip reading data that is not necessary for the query. For example, consider a table of events partitioned on the event time using calendar day granularity. If the table contained 2 years of events and a user wanted to find the events for a given 7-day window,

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Apache Impala is now a Top-Level Apache Project

Categories: CDH Hadoop Impala

Five years ago, Cloudera shared with the world our plan to transfer the lessons from decades of relational database research to the Apache Hadoop platform via a new SQL engine — Apache Impala — the first and fastest open source MPP SQL engine for Hadoop.  Impala enabled SQL users to operate on vast amounts of data in open formats, stored on HDFS originally (with Apache Kudu, Amazon S3, and Microsoft ADLS now also native storage options),

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Apache Impala Leads Traditional Analytic Database

Categories: CDH Impala Performance

Unmodified TPC-DS-based performance benchmark show Impala’s leadership compared to a traditional analytic database (Greenplum), especially for multi-user concurrent workloads. Additionally, benchmark continues to demonstrate significant performance gap between analytic databases and SQL-on-Hadoop engines like Hive LLAP, Spark SQL, and Presto.

The past year has been one of the biggest for Apache Impala (incubating). Not only has the team continued to work on ever-growing scale and stability, but a number of key capabilities have been rolled out that further solidifies Impala as the open standard for high-performance BI and SQL analytics.

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Apache Kudu and Apache Impala (Incubating): The Integration Roadmap

Categories: Impala Kudu

Impala users can expect new performance and usability benefits via improved integration with Kudu.

It’s been nearly one year since the public beta announcement of Kudu (now a top-level Apache project) and a noteworthy milestone has been reached: its 1.0 release. This is particularly exciting as Kudu extends the use cases that can be supported on the Apache Hadoop platform, whether it be on-premises or in the cloud,

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