Cloudera Engineering Blog · Spark Posts
Thanks to Matthew Dixon, principal consultant at Quiota LLC and Professor of Analytics at the University of San Francisco, and Mohammad Zubair, Professor of Computer Science at Old Dominion University, for this guest post that demonstrates how to easily deploy exposure calculations on Apache Spark for in-memory analytics on scenario data.
Since the 2007 global financial crisis, financial institutions now more accurately measure the risks of over-the-counter (OTC) products. It is now standard practice for institutions to adjust derivative prices for the risk of the counter-party’s, or one’s own, default by means of credit or debit valuation adjustments (CVA/DVA).
A Hive-on-Spark beta is now available via CDH parcel. Give it a try!
The Hive-on-Spark project (HIVE-7292) is one of the most watched projects in Apache Hive history. It has attracted developers from across the ecosystem, including from organizations such as Intel, MapR, IBM, and Cloudera, and gained critical help from the Spark community.
Many thanks to David Whiting of Spotify for allowing us to re-publish the following Spotify Labs post about its Apache Crunch use cases.
(Note: Since this post was originally published in November 2014, many of the library functions described have been added into crunch-core, so they’ll soon be available to all Crunch users by default.)
Authored by a substantial portion of Cloudera’s Data Science team (Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills), Advanced Analytics with Spark (currently in Early Release from O’Reilly Media) is the newest addition to the pipeline of ecosystem books by Cloudera engineers. I talked to the authors recently.
Why did you decide to write this book?
Cloudera and Google are collaborating to bring Google Cloud Dataflow to Apache Spark users (and vice-versa). This new project is now incubating in Cloudera Labs!
“The future is already here—it’s just not evenly distributed.” —William Gibson
Cloudera and Intel engineers are collaborating to make Spark’s shuffle process more scalable and reliable. Here are the details about the approach’s design.
What separates computation engines like MapReduce and Apache Spark (the next-generation data processing engine for Apache Hadoop) from embarrassingly parallel systems is their support for “all-to-all” operations. As distributed engines, MapReduce and Spark operate on sub-slices of a dataset partitioned across the cluster. Many operations process single data-points at a time and can be carried out fully within each partition. All-to-all operations must consider the dataset as a whole; the contents of each output record can depend on records that come from many different partitions. In Spark,
reduceByKey are popular examples of these types of operations.
A new Spark tutorial and Trifacta deployment option make Cloudera Live even more useful for getting started with Apache Hadoop.
When it comes to learning Hadoop and CDH (Cloudera’s open source platform including Hadoop), there is no better place to start than Cloudera Live (cloudera.com/live). With a quick, one-button deployment option, Cloudera Live launches a four-node Cloudera cluster that you can learn and experiment in free for two-weeks. To help plan and extend the capabilities of your cluster, we also offer various partner deployments. Building on the addition of interactive tutorials and Tableau and Zoomdata integration, we have added a new tutorial on Apache Spark and a new Trifacta partner deployment.
As we progressively move from MapReduce to Spark, we shouldn’t have to give up good HBase integration. Hence the newest Cloudera Labs project, SparkOnHBase!
Apache Spark is making a huge impact across our industry, changing the way we think about batch processing and stream processing. However, as we progressively migrate from MapReduce toward Spark, we shouldn’t have to “give up” anything. One of those capabilities we need to retain is the ability to interact with Apache HBase.
Our “Top 10″ list of blog posts published during a calendar year is a crowd favorite (see the 2013 version here), in particular because it serves as informal, crowdsourced research about popular interests. Page views don’t lie (although skew for publishing date—clearly, posts that publish earlier in the year have pole position—has to be taken into account).
In 2014, a strong interest in various new components that bring real time or near-real time capabilities to the Apache Hadoop ecosystem is apparent. And we’re particularly proud that the most popular post was authored by a non-employee.
- How-to: Create a Simple Hadoop Cluster with VirtualBox
by Christian Javet
Explains how t set up a CDH-based Hadoop cluster in less than an hour using VirtualBox and Cloudera Manager.
- Why Apache Spark is a Crossover Hit for Data Scientists
by Sean Owen
An explanation of why Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.
- How-to: Run a Simple Spark App in CDH 5
by Sandy Ryza
Helps you get started with Spark using a simple example.
- New SQL Choices in the Apache Hadoop Ecosystem: Why Impala Continues to Lead
by Justin Erickson, Marcel Kornacker & Dileep Kumar
Open benchmark testing of Impala 1.3 demonstrates performance leadership compared to alternatives (by 950% or more), while providing greater query throughput and with a far smaller CPU footprint.
- Apache Kafka for Beginners
by Gwen Shapira & Jeff Holoman
When used in the right way and for the right use case, Kafka has unique attributes that make it a highly attractive option for data integration.
- Apache Hadoop YARN: Avoiding 6 Time-Consuming “Gotchas”
by Jeff Bean
Understanding some key differences between MR1 and MR2/YARN will make your migration much easier.
- Impala Performance Update: Now Reaching DBMS-Class Speed
by Justin Erickson, Greg Rahn, Marcel Kornacker & Yanpei Chen
As of release 1.1.1, Impala’s speed beat the fastest SQL-on-Hadoop alternatives–including a popular analytic DBMS running on its own proprietary data store.
- The Truth About MapReduce Performance on SSDs
by Karthik Kambatla & Yanpei Chen
It turns out that cost-per-performance, not cost-per-capacity, is the better metric for evaluating the true value of SSDs. (See the session on this topic at Strata+Hadoop World San Jose in Feb. 2015!)
- How-to: Translate from MapReduce to Spark
by Sean Owen
The key to getting the most out of Spark is to understand the differences between its RDD API and the original Mapper and Reducer API.
- How-to: Write and Run Apache Giraph Jobs on Hadoop
by Mirko Kämpf
Explains how to create a test environment for writing and testing Giraph jobs, or just for playing around with Giraph and small sample datasets.
Interested in Hive-on-Spark progress? This new AMI gives you a hands-on experience.
Nearly one year ago, the Apache Hadoop community began to embrace Apache Spark as a powerful batch-processing engine. Today, many organizations and projects are augmenting their Hadoop capabilities with Spark. As part of this shift, the Apache Hive community is working to add Spark as an execution engine for Hive. The Hive-on-Spark work is being tracked by HIVE-7292 which is one of the most popular JIRAs in the Hadoop ecosystem. Furthermore, three weeks ago, the Hive-on-Spark team offered the first demo of Hive on Spark.