Cloudera Engineering Blog
Big Data best practices, how-to's, and internals from Cloudera Engineering and the community
While the new Spark Developer training from Cloudera University is valuable for developers who are new to Big Data, it’s also a great call for MapReduce veterans.
When I set out to learn Apache Spark (which ships inside Cloudera’s open source platform) about six months ago, I started where many other people do: by following the various online tutorials available from UC Berkeley’s AMPLab, the creators of Spark. I quickly developed an appreciation for the elegant, easy-to-use API and super-fast results, and was eager to learn more.
Cloudera Enterprise’s newest release contains important new security and performance features, and offers support for the latest innovations in the open source platform.
We’re pleased to announce the release of Cloudera Enterprise 5.1 (comprising CDH 5.1, Cloudera Manager 5.1, and Cloudera Navigator 2.0).
It was good to see Jay Kreps (@jaykreps), the LinkedIn engineer who is the tech lead for that company’s online data infrastructure, visit Cloudera Engineering yesterday to spread the good word about Apache Kafka.
Kafka, of course, was originally developed inside LinkedIn and entered the Apache Incubator in 2011. Today, it is being widely adopted as a pub/sub framework that works at massive scale (and which is commonly used to write to Apache Hadoop clusters, and even data warehouses).
There’s an important new addition coming to the Apache Hadoop book ecosystem. It’s now in early release!
We are very happy to announce that the new Apache Hadoop book we have been writing for O’Reilly Media, Hadoop Application Architectures, is now available as an early release! It contains the first two chapters and can be found in O’Reilly’s Catalog and via Safari.
Learn how Spark facilitates the calculation of computationally-intensive statistics such as VaR via the Monte Carlo method.
Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations, some use it to better understand the risk characteristics of large portfolios, and others compute it before executing trades to help make informed and immediate decisions.
Pretty busy for early Summer:
Google’s Jeff Dean — among the original architects of MapReduce, Bigtable, and Spanner — revealed some fascinating facts about Google’s internal environment at Cloudera HQ recently.
Earlier this week, we were pleased to welcome Google Senior Fellow Jeff Dean to Cloudera’s Palo Alto HQ to give an overview of some of his group’s current research. Jeff has a peerless pedigree in distributed computing circles, having been deeply involved in the design and implementation of Google’s original advertising serving system, MapReduce, Bigtable, Spanner, and a host of other projects.
Learn how creating dataflow pipelines for time-series analysis is a lot easier with Apache Crunch.
In a previous blog post, I described a data-driven market study based on Wikipedia access data and content. I explained how useful it is to combine several public data sources, and how this approach sheds light onto the hidden correlations across Wikipedia pages.
Two of the most vibrant communities in the Apache Hadoop ecosystem are now working together to bring users a Hive-on-Spark option that combines the best elements of both.
Apache Hive is a popular SQL interface for batch processing and ETL using Apache Hadoop. Until recently, MapReduce was the only execution engine in the Hadoop ecosystem, and Hive queries could only run on MapReduce. But today, alternative execution engines to MapReduce are available — such as Apache Spark and Apache Tez (incubating).
Extended attributes in HDFS will facilitate at-rest encryption for Project Rhino, but they have many other uses, too.
Many mainstream Linux filesystems implement extended attributes, which let you associate metadata with a file or directory beyond common “fixed” attributes like filesize, permissions, modification dates, and so on. Extended attributes are key/value pairs in which the values are optional; generally, the key and value sizes are limited to some implementation-specific limit. A filesystem that implements extended attributes also provides system calls and shell commands to get, list, set, and remove attributes (and values) to/from a file or directory.