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,
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),
New R package implyr enables R users to query Impala using dplyr.
Apache Impala (incubating) enables low-latency interactive SQL queries on data stored in HDFS, Amazon S3, Apache Kudu, and Apache HBase. With the availability of the R package implyr on CRAN and GitHub, it’s now possible to query Impala from R using the popular package dplyr.
dplyr provides a grammar of data manipulation,
Over the past year (and through several releases), Apache Impala (incubating) has added numerous new features and performance enhancements better enabling high-performance SQL analytics over big data. Thus, it is time again for an update to the Impala cookbook, which contains best practices for these new features, updated guidelines, and more detailed examples.
Note: This cookbook does not yet capture best practices for the major new advancements available with the recent GA of Kudu.
Cloudera Enterprise 5.10 includes the latest updates of Hue, the intelligent editor for SQL Developers and Analysts.
As part of Cloudera’s continuing investments in user experience and productivity, Cloudera Enterprise 5.10 includes an updated version of Hue. We provide a summary of the main enhancements in the following part of this blog post. (Hue from C5.10 is also available for a quick try in one click on demo.gethue.com.)
The Hue editor keeps getting better with these major improvements:
The number of rows returned is displayed so you can quickly see the size of the dataset.