Cloudera Engineering Blog · Guest Posts
The following post, by Sarah Cannon of Digital Reasoning, was originally published in that company’s blog. Digital Reasoning has graciously permitted us to re-publish here for your convenience.
At the beginning of each release cycle, engineers at Digital Reasoning are given time to explore the latest in Big Data technologies, examining how the frequently changing landscape might be best adapted to serve our mission. As we sat down in the early stages of planning for Synthesys 3.8 one of the biggest issues we faced involved reconciling the tradeoff between flexibility and performance. How can users quickly and easily retrieve knowledge from Synthesys without being tied to one strict data model?
Our thanks to Russell Cardullo and Michael Ruggiero, Data Infrastructure Engineers at Sharethrough, for the guest post below about its use case for Spark Streaming.
At Sharethrough, which offers an advertising exchange for delivering in-feed ads, we’ve been running on CDH for the past three years (after migrating from Amazon EMR), primarily for ETL. With the launch of our exchange platform in early 2013 and our desire to optimize content distribution in real time, our needs changed, yet CDH remains an important part of our infrastructure.
The guest post below was originally authored by Pinterest engineer Raghavendra Prabhu and published by the Pinterest Engineering blog. Being big ZooKeeper fans, we re-publish it here for your convenience. Thanks, Pinterest!
Apache ZooKeeper is an open source distributed coordination service that’s popular for use cases like service discovery, dynamic configuration management and distributed locking. While it’s versatile and useful, it has failure modes that can be hard to prepare for and recover from, and if used for site critical functionality, can have a significant impact on site availability.
Learn how to use Cloudera Search along with RBL-JE to search and index documents in multiple languages.
Our thanks to Basis Technology for providing the how-to below!
Set up a CDH-based Hadoop cluster in less than an hour using VirtualBox and Cloudera Manager.
Thanks to Christian Javet for his permission to republish his blog post below!
Thanks to Marshall Bockrath-Vandegrift of advanced threat detection/malware company (and CDH user) Damballa for the following post about his Parkour project, which offers libraries for writing MapReduce jobs in Clojure. Parkour has been tested (but is not supported) on CDH 3 and CDH 4.
Clojure is Lisp-family functional programming language which targets the JVM. On the Damballa R&D team, Clojure has become the language of choice for implementing everything from web services to machine learning systems. One of Clojure’s key features for us is that it was designed from the start as an explicitly hosted language, building on rather than replacing the semantics of its underlying platform. Clojure’s mapping from language features to JVM implementation is frequently simpler and clearer even than Java’s.
Our thanks to Databricks, the company behind Apache Spark (incubating), for providing the guest post below. Cloudera and Databricks recently announced that Cloudera will distribute and support Spark in CDH. Look for more posts describing Spark internals and Spark + CDH use cases in the near future.
Our thanks to Telvis Calhoun, Zach Hanif, and Jason Trost of Endgame for the guest post below about their BinaryPig application for large-scale malware analysis on Apache Hadoop. Endgame uses data science to bring clarity to the digital domain, allowing its federal and commercial partners to sense, discover, and act in real time.
Our thanks to Concurrent Inc. for the how-to below about using Cascading Pattern with CDH. Cloudera recently tested CDH 4.4 with the Cascading Compatibility Test Suite verifying compatibility with Cascading 2.2.
Cascading Pattern is a machine-learning project within the Cascading development framework used to build enterprise data workflows. Cascading provides an abstraction layer on top of Apache Hadoop and other computing topologies that allows enterprises to leverage existing skills and resources to build data processing applications on Hadoop, without the need for specialized Hadoop skills.
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.