Cloudera Engineering Blog · Guest Posts
Our thanks to AWS Solutions Architect Rahul Bhartia for allowing us to republish his post below.
Apache Hadoop provides a great ecosystem of tools for extracting value from data in various formats and sizes. Originally focused on large-batch processing with tools like MapReduce, Apache Pig, and Apache Hive, Hadoop now provides many tools for running interactive queries on your data, such as Impala, Drill, and Presto. This post shows you how to use Amazon Elastic MapReduce (Amazon EMR) to analyze a data set available on Amazon Simple Storage Service (Amazon S3) and then use Tableau with Impala to visualize the data.
Our thanks to Melanie Imhof, Jonas Looser, Thierry Musy, and Kurt Stockinger of the Zurich University of Applied Science in Switzerland for the post below about their research into the query performance of Impala for mixed workloads.
Recently, we were approached by an industry partner to research and create a blueprint for a new Big Data, near real-time, query processing architecture that would replace its current architecture based on a popular open source database system.
Our thanks to Rakesh Rao of Quaero, for allowing us to re-publish the post below about Quaero’s experiences using partitioning in Apache Hive.
In this post, we will talk about how we can use the partitioning features available in Hive to improve performance of Hive queries.
The Transaction Processing Council (TPC), working with Cloudera, recently announced the new TPCx-HS benchmark, a good first step toward providing a Big Data benchmark.
In this interview by Roberto Zicari with Francois Raab, the original author of the TPC-C Benchmark, and Yanpei Chen, a Performance Engineer at Cloudera, the interviewees share their thoughts on the next step for benchmarks that reflect real-world use cases.
The following post was written by Jay Vyas (@jayunit100) and originally published in the Gluster.org Community.
I have recently spent some time getting Cloudera’s CDH 5 distribution of Apache Hadoop to work on GlusterFS 3.3 using Distributed Replicated 2 Volumes. This is made possible by the fact that Apache Hadoop has a pluggable filesystem architecture that allows the computational components within the CDH 5 distribution to be configured to use alternative filesystems to HDFS. In this case, one can configure CDH 5 to use the Hadoop FileSystem plugin for GlusterFS (glusterfs-hadoop), which allows it to run on GlusterFS 3.3. I’ve provided a diagram below that illustrates the CDH 5 core processes and how they interact with GlusterFS.
It’s been a while since we provided a how-to for this purpose. Thanks, Daan Debie (@DaanDebie), for allowing us to re-publish the instructions below (for CDH 5)!
I recently started as a Big Data Engineer at The New Motion. While researching our best options for running an Apache Hadoop cluster, I wanted to try out some of the features available in the newest version of Cloudera’s Hadoop distribution: CDH 5. Of course I could’ve downloaded the QuickStart VM, but I rather wanted to run a virtual cluster, making use of the 16GB of RAM my shiny new 15″ Retina Macbook Pro has ;)
Thanks to Bill Podell, VP Big Data and BI Practice, MBI Solutions, for the guest post below.
Capacity planning has long been a critical component of successful implementations for production systems. Today, Big Data calls for a particularly deep understanding of capacity management – because resource utilization explodes as business users, analysts, and data scientists jump onboard to analyze and use newly found data. The resource impact can escalate very quickly, causing poor loading and or response times. The result is throwing more hardware at the issue without any understanding of what impact the new hardware will have on the current issue. Better yet, be proactive and know about the problem before the problem even occurs!
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.
Thanks to Jonathan Natkins of WibiData for the post below about how his company extended Cloudera Manager to manage Kiji. Learn more about Kiji and the organizations using it to build real-time HBase applications at Kiji Sessions, happening on May 6, 2014, the day after HBaseCon.
As a partner of Cloudera, WibiData sees Cloudera Manager’s new extensibility framework as one of the most exciting parts of Cloudera Enterprise 5. Cloudera Manager 5.0.0 provides the single-pane view that Apache Hadoop administrators and operators want to effectively manage a cluster of machines. Additionally, Cloudera Manager now offers tight integration for partners to plug into the CDH ecosystem, which benefits Cloudera as well as WibiData.
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.