Cloudera Engineering Blog · Impala Posts
In its relatively short lifetime (co-founded by Twitter and Cloudera in July 2013), Parquet has already become the de facto standard for columnar storage of Apache Hadoop data — with native support in Impala, Apache Hive, Apache Pig, Apache Spark, MapReduce, Apache Tajo, Apache Drill, Apache Crunch, and Cascading (and forthcoming in Presto and Shark). Parquet adoption is also broad-based, with employees of the following companies (partial list) actively contributing:
Learn how HiveServer, Apache Sentry, and Impala help make Hadoop play nicely with BI tools when Kerberos is involved.
In 2010, I wrote a simple pair of blog entries outlining the general considerations behind using Apache Hadoop with BI tools. The Cloudera partner ecosystem has positively exploded since then, and the technology has matured as well. Today, if JDBC is involved, all the pieces needed to expose Hadoop data through familiar BI tools are available:
Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working as a Principal Big Data Consultant at Allstate Insurance, for the guest post below about his experiences with Impala.
It started with a simple request from one of the managers in my group at Allstate to put together a demo of Tableau connecting to Cloudera Impala. I had previously worked on Impala with a large dataset about a year ago while it was still in beta, and was curious to see how Impala had improved since then in features and stability.
The new Python client for Impala will bring smiles to Pythonistas!
As a data scientist, I love using the Python data stack. I also love using Impala to work with very large data sets. But things that take me out of my Python workflow are generally considered hassles; so it’s annoying that my main options for working with Impala are to write shell scripts, use the Impala shell, and/or transfer query results by reading/writing local files to disk.
Thanks to Alexander Rubin of Percona for allowing us to re-publish the post below!
Apache Hadoop is commonly used for data analysis. It is fast for data loads and scalable. In a previous post I showed how to integrate MySQL with Hadoop. In this post I will show how to export a table from MySQL to Hadoop, load the data to Cloudera Impala (columnar format), and run reporting on top of that. For the examples below, I will use the “ontime flight performance” data from my previous post.
The following post, by Sarah Cannon of Digital Reasoning, was originally published in that company’s blog. Digital Reasoning has graciously permitted us to re-publish here for your convenience.
At the beginning of each release cycle, engineers at Digital Reasoning are given time to explore the latest in Big Data technologies, examining how the frequently changing landscape might be best adapted to serve our mission. As we sat down in the early stages of planning for Synthesys 3.8 one of the biggest issues we faced involved reconciling the tradeoff between flexibility and performance. How can users quickly and easily retrieve knowledge from Synthesys without being tied to one strict data model?
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:
This quick demo illustrates how easy it is to implement role-based access and control in Impala using Sentry.
Apache Sentry (incubating) is the Apache Hadoop ecosystem tool for role-based access control (RBAC). In this how-to, I will demonstrate how to implement Sentry for RBAC in Impala. I feel this introduction is best motivated by a use case.
Cloudera’s own enterprise data hub is yielding great results for providing world-class customer support.
Here at Cloudera, we are constantly pushing the envelope to give our customers world-class support. One of the cornerstones of this effort is the Cloudera Support Interface (CSI), which we’ve described in prior blog posts (here and here). Through CSI, our support team is able to quickly reason about a customer’s environment, search for information related to a case currently being worked, and much more.
Hadoop 2.3.0 includes hundreds of new fixes and features, but none more important than HDFS caching.
The Apache Hadoop community has voted to release Hadoop 2.3.0, which includes (among many other things):