Cloudera Developer Blog · Impala Posts

Download the New Impala e-Book from O’Reilly Media

As a delicious appetizer for the Strata Conference + Hadoop World next week (sold out!), O’Reilly Media has partnered with us to create and publish a new e-book specifically intended for technical end-users of Cloudera Impala, the open source distributed query engine for Apache Hadoop.

Authored by Cloudera’s own John Russell, the e-book provides a 30-page tour of Impala’s internals and architecture, as well as common usage patterns intended for mainstream (SQL) users.

Parquet at Salesforce.com

The following Parquet blog post was originally published by Salesforce.com Lead Engineer and Apache Pig Committer Prashant Kommireddi (@pRaShAnT1784). Prashant has kindly given us permission to re-publish below. Parquet is an open source columnar storage format co-founded by Twitter and Cloudera.

Parquet is a columnar storage format for Apache Hadoop that uses the concept of repetition/definition levels borrowed from Google Dremel. It provides efficient encoding and compression schemes, the efficiency being improved due to application of aforementioned on a per-column basis (compression is better as column values would all be the same type, encoding is better as values within a column could often be the same and repeated). Here is a nice blog post from Julien Le Dem of Twitter describing Parquet internals.

Explore the Impala App in Hue

The following post was originally published by the Hue Team at the Hue blog in a slightly different form.

Hue, the open source web GUI that makes Apache Hadoop easy to use, has supported Cloudera Impala since its inception to enable fast, interactive SQL queries from within your browser. In this post, you’ll see a demo of Hue’s Impala app in action and explore its impressive query speed for yourself.

Impala App Demo

Secrets of Cloudera Support: Impala and Search Make the Customer Experience Even Better

In December 2012, we described how an internal application built on CDH called Cloudera Support Interface (CSI), which drastically improves Cloudera’s ability to optimally support our customers, is a unique and instructive use case for Apache Hadoop. In this post, we’ll follow up by describing two new differentiating CSI capabilities that have made Cloudera Support yet more responsive for customers:

What’s Next for Impala After Release 1.1

In December 2012, while Cloudera Impala was still in its beta phase, we provided a roadmap for planned functionality in the production release. In the same spirit of keeping Impala users, customers, and enthusiasts well informed, this post provides an updated roadmap for upcoming releases later this year and in early 2014.

But first, a thank-you: Since the initial beta release, we’ve received a tremendous amount of feedback and validation about Impala — copious in its quality as well as quantity. At least one person in approximately 4,500 unique organizations around the world have downloaded the Impala binary, to date. And even after only a few months of GA, we’ve seen Cloudera Enterprise customers from multiple industries deploy Impala 1.x in business-critical environments with support via a Cloudera RTQ (Real-Time Query) subscription — including leading organizations in insurance, banking, retail, healthcare, gaming, government, telecom, and advertising.

Visualization on Impala: Big, Real-Time, and Raw

The guest post below is provided by Justin Langseth, Founder & CEO of Zoomdata, Inc. Thanks, Justin!

What if you could affordably manage billions of rows of raw Big Data and let typical business people analyze it at the speed of thought in beautiful, interactive visuals? What if you could do all the above without worrying about structuring that data in a data warehouse schema, moving it, and pre-defining reports and dashboards? With the approach I’ll describe below, you can.

Impala Meetup in San Francisco on Aug. 20

Cloudera Impala has made huge progress since its initial announcement – and there’s even more good news on the roadmap. To learn more, plan to attend an Impala meetup hosted by Cloudera in its San Francisco offices on the evening of Aug. 20:

Announcing Parquet 1.0: Columnar Storage for Hadoop

We’re very happy to re-publish the following post from Twitter analytics infrastructure engineering manager Dmitriy Ryaboy (@squarecog).

In March we announced the Parquet project, the result of a collaboration between Twitter and Cloudera intended to create an open-source columnar storage format library for Apache Hadoop.

Thanks for the Memories, #OSCON 2013

OSCON 2013 is already receding in the rear-view mirror, but we had a great time. Cloudera’s sessions were very well attended — with Tom Wheeler taking the prize (well over 200 attendees for his “Introduction to Apache Hadoop” tutorial) — but best of all was the opportunity to meet and mingle with people in the broader open source community. If you visited us at Booth 420, we hope you will now download and install the QuickStart VM after seeing it in our demo, and that your questions were adequately answered (most popular question: “Can you tell me more about Cloudera Impala?”)

In my biased opinion, the crowning achievement was our ability to not only distribute a couple hundred “Data is the New Bacon” Tshirts within a 36-hour period, but to clean ourselves out of the meat-free version shortly thereafter, as well:

With Sentry, Cloudera Fills Hadoop’s Enterprise Security Gap

Every day, more data, users, and applications are accessing ever-larger Apache Hadoop clusters. Although this is good news for data driven organizations overall, for security administrators and compliance officers, there are still lingering questions about how to enable end-users under existing Hadoop infrastructure without compromising security or compliance requirements.

While Hadoop has strong security at the filesystem level, it lacks the granular support needed to adequately secure access to data by users and BI applications. Today, this problem forces organizations in industries for which security is paramount (such as financial services, healthcare, and government) to make a choice: either leave data unprotected or lock out users entirely. Most of the time, the preferred choice is the latter, severely inhibiting access to data in Hadoop.

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