Cloudera Developer Blog · Impala Posts
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):
Bringing Parquet support to Hive was a community effort that deserves congratulations!
Previously, this blog introduced Parquet, an efficient ecosystem-wide columnar storage format for Apache Hadoop. As discussed in that blog post, Parquet encodes data extremely efficiently and as described in Google’s original Dremel paper. (For more technical details on the Parquet format read Dremel made simple with Parquet, or go directly to the open and community-driven Parquet Format specification.)
Thanks to Xavier Clements of Wajam for allowing us to re-publish his blog post about Wajam’s Hadoop experiences below!
Wajam is a social search engine that gives you access to the knowledge of your friends. We gather your friends’ recommendations from Facebook, Twitter, and other social platforms and serve these back to you on supported sites like Google, eBay, TripAdvisor, and Wikipedia.
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.
Impala’s speed now beats the fastest SQL-on-Hadoop alternatives. Test for yourself!
Since the initial beta release of Cloudera Impala more than one year ago (October 2012), we’ve been committed to regularly updating you about its evolution into the standard for running interactive SQL queries across data in Apache Hadoop and Hadoop-based enterprise data hubs. To briefly recap where we are today:
With the close of 2013, we also thought it appropriate to include some high points from across the year (not listed in any particular order):
Developers, rejoice: Impala is now available on EMR for testing and evaluation.
Very recently, Amazon Web Services announced support for running Cloudera Impala queries on its Elastic MapReduce (EMR) service. This is very good news for EMR users — as well as for users of other platforms interested in kicking Impala’s tires in a friction-free way. It’s also yet another sign that Impala is rapidly being adopted across the ecosystem as the gold standard for interactive SQL and BI queries on Apache Hadoop.
The new RImpala package brings the speed and interactivity of Impala to queries from R.
Our thanks to Austin Chungath, Sachin Sudarshana, and Vikas Raguttahalli of Mu Sigma, a Decision Sciences and Big Data analytics company, for the guest post below.
A quick on-ramp (and demo) for using the new Sentry module for RBAC in conjunction with Hive
One attribute of the Enterprise Data Hub is fine-grained access to data by users and apps. This post about supporting infrastructure for that goal was originally published at blogs.apache.org. We republish it here for your convenience.
Thanks to Victor Bittorf, a visiting graduate computer science student at Stanford University, for the guest post below about how to use the new prebuilt analytic functions for Cloudera Impala.
Cloudera Impala is an exciting project that unlocks interactive queries and SQL analytics on big data. Over the past few months I have been working with the Impala team to extend Impala’s analytic capabilities. Today I am happy to announce the availability of pre-built mathematical and statistical algorithms for the Impala community under a free open-source license. These pre-built algorithms combine recent theoretical techniques for shared nothing parallelization for analytics and the new user-defined aggregations (UDA) framework in Impala 1.2 in order to achieve big data scalability. This initial release has support for logistic regression, support vector machines (SVMs), and linear regression.