Cloudera Engineering Blog · Impala Posts
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:
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.
Editor’s note (added Feb. 2, 2014): You can review the latest (and exciting) Impala performance benchmark results by Cloudera here.
In the presentation below, Scott Leberknight of Near Infinity has done such a good and thorough job of dissecting Cloudera Impala, we want to share it with you here.
For years, Cloudera has provided virtual machines that give you a working Apache Hadoop environment out-of-the-box. It’s the quickest way to learn and experiment with Hadoop right from your desktop.
We’re constantly updating and improving the QuickStart VM, and in the latest release there are two of Cloudera’s new products that give you easier and faster access to your data: Cloudera Search and Cloudera Impala. We’ve also added corresponding applications to Hue – an open source web-based interface for Hadoop, and the easiest way to interact with your data.
Data analysts and business intelligence specialists have been at the heart of new trends driving business growth over the past decade, including log file and social media analytics. However, Big Data heretofore has been beyond the reach of analysts because traditional tools like relational databases don’t scale, and scalable systems like Apache Hadoop have historically required Java expertise.
Cloudera Impala has many exciting features, but one of the most impressive is the ability to analyze data in multiple formats, with no ETL needed, in HDFS and Apache HBase. Furthermore, you can use multiple frameworks, such as MapReduce and Impala, to analyze that same data. Consequently, Impala will often run side-by-side with MapReduce on the same physical hardware, with both supporting business-critical workloads. For such multi-tenant clusters, Impala and MapReduce both need to perform well despite potentially conflicting demands for cluster resources.
In this post, we’ll share our experiences configuring Impala and MapReduce for optimal multi-tenant performance. Our goal is to help users understand how to tune their multi-tenant clusters to meet production service level objectives (SLOs), and to contribute to the community some test methods and performance models that can be helpful beyond Cloudera.
Defining Realistic Test Scenarios
Our thanks to Brian Dirking, Director of Product Marketing for Alteryx, for the guest post below:
At Alteryx we are excited about the release of Cloudera Impala. The impact on Big Data Analytics is that the ability to perform real-time queries on Apache Hadoop will provide faster access and results. This is applicable to our customers, the business users who are running analytics to get access to data, perform analytics, and then follow up with new questions. Insight doesn’t happen all at once. The ability to query and refine quickly is ultimately what will lead business users to insight.
“Are data warehouses becoming victims of their own success?”, Tony Baer asks in a recent blog post:
Our thanks to Ted Wasserman, product manager for Tableau, for the guest post below:
Many of our customers are turning to Apache Hadoop as they grapple with their big data challenges. Hadoop offers many benefits such as its scalability, economics, and versatility. Even so, adoption-to-date has largely centered around applications with “batch”-oriented workloads because of the latency imposed by the MapReduce framework. To increase Hadoop’s usefulness and adoption in the business intelligence space where users need fast, interactive response times when they ask a question, a new approach was needed.
Our thanks to Yves de Montcheuil, Vice President of Marketing for Talend, for the guest post below:
According to Wikipedia, the impala is a medium-sized African antelope; its name comes from the Zulu language meaning “gazelle”. Like elephants, it is found in savannas, and this may be the link with Hadoop. Impala is also the name of Cloudera’s SQL-on-Apache Hadoop project, launched in beta at Strata last October and just released in version 1.0.