Author Archives: John Russell

How-to: Prepare Unstructured Data in Impala for Analysis

Categories: How-to Impala

Learn how to build an Impala table around data that comes from non-Impala, or even non-SQL, sources.

As data pipelines start to include more aspects such as NoSQL or loosely specified schemas, you might encounter situations where you have data files (particularly in Apache Parquet format) where you do not know the precise table definition. This tutorial shows how you can build an Impala table around data that comes from non-Impala or even non-SQL sources,

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New in CDH 5.2: More SQL Functionality and Compatibility for Impala 2.0

Categories: CDH Impala

Impala 2.0 is the most SQL-complete/SQL-compatible release yet.

As we reported in the most recent roadmap update (“What’s Next for Impala: Focus on Advanced SQL Functionality”), more complete SQL functionality (and better SQL compatibility with other vendor extensions) is a major theme in Impala 2.0.

In this post, we’ll describe the highlights (not a complete list), and provide links to the docs that drill-down on these functions.

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How-to: Use Parquet with Impala, Hive, Pig, and MapReduce

Categories: Hive How-to Impala MapReduce Parquet Pig

The CDH software stack lets you use your tool of choice with the Parquet file format – – offering the benefits of columnar storage at each phase of data processing. 

An open source project co-founded by Twitter and Cloudera, Parquet was designed from the ground up as a state-of-the-art, general-purpose, columnar file format for the Apache Hadoop ecosystem. In particular, Parquet has several features that make it highly suited to use with Cloudera Impala for data warehouse-style operations:

  • Columnar storage layout: A query can examine and perform calculations on all values for a column while reading only a small fraction of the data from a data file or table.

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How-to: Get Started Writing Impala UDFs

Categories: Hive How-to Impala

Cloudera provides docs and a sample build environment to help you get easily started writing your own Impala UDFs.

User-defined functions (UDFs) let you code your own application logic for processing column values during a Cloudera Impala query. For example, a UDF could perform calculations using an external math library, combine several column values into one, do geospatial calculations, or other kinds of tests and transformations that are outside the scope of the built-in SQL operators and functions.

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