Cloudera Developer Blog · Use Case Posts
Because raising the visibility of Apache Hadoop use cases is so important, in this post we bring you a re-posted story about how and why Rapleaf, a marketing data company based in San Francisco, uses Cloudera Enterprise (CDH and Cloudera Manager).
Founded in 2006, Rapleaf’s mission is to make it incredibly easy for marketers to access the data they need so they can personalize content for their customers. Rapleaf helps clients “fill in the blanks” about their customers by taking contact lists and, in real time, providing supplemental data points, statistics and aggregate charts and graphs that are guaranteed to have greater than 90% accuracy. Rapleaf is powered by Cloudera.
Business Challenges Before Cloudera
This is the first post in series that will get you going on how to write, compile, and run a simple MapReduce job on Apache Hadoop. The full code, along with tests, is available at http://github.com/cloudera/mapreduce-tutorial. The program will run on either MR1 or MR2.
We’ll assume that you have a running Hadoop installation, either locally or on a cluster, and your environment is set up correctly so that typing “hadoop” into your command line gives you some notes on usage. Detailed instructions for installing CDH, Cloudera’s open-source, enterprise-ready distro of Hadoop and related projects, are available here: https://ccp.cloudera.com/display/CDH4DOC/CDH4+Installation. We’ll also assume you have Maven installed on your system, as this will make compiling your code easier. Note that Maven is not a strict dependency; we could also compile using Java on the command line or with an IDE like Eclipse.
The Use Case
At Cloudera, we put great pride into drinking our own champagne. That pride extends to our support team, in particular.
Cloudera Manager, our end-to-end management platform for CDH (Cloudera’s open-source, enterprise-ready distribution of Apache Hadoop and related projects), has a feature that allows subscription customers to send a snapshot of their cluster to us. When these cluster snapshots come to us from customers, they end up in a CDH cluster at Cloudera where various forms of data processing and aggregation can be performed.
The following is a re-post from CTOVision.com.
The Government Big Data Solutions Award was established to highlight innovative solutions and facilitate the exchange of best practices, lessons learned and creative ideas for addressing Big Data challenges. The Top Five Nominees of 2012 were chosen for criteria that included:
The following is a re-post from Bob Gourley of CTOVision.com.
The amount of data being created in governments is growing faster than humans can analyze. But analysis can solve tough challenges. Those two facts are driving the continual pursuit of new Big Data solutions. Big Data solutions are of particular importance in government. The government has special abilities to focus research in areas like Health Sciences, Economics, Law Enforcement, Defense, Geographic Studies, Environmental Studies, Bioinformatics, and Computer Security. Each of those area can be well served by Big Data approaches, and each has exemplars of solutions worthy of highlighting to the community.
This is the third article in a series about analyzing Twitter data using some of the components of the Apache Hadoop ecosystem that are available in CDH (Cloudera’s open-source distribution of Apache Hadoop and related projects). If you’re looking for an introduction to the application and a high-level view, check out the first article in the series.
In the previous article in this series, we saw how Flume can be utilized to ingest data into Hadoop. However, that data is useless without some way to analyze the data. Personally, I come from the relational world, and SQL is a language that I speak fluently. Apache Hive provides an interface that allows users to easily access data in Hadoop via SQL. Hive compiles SQL statements into MapReduce jobs, and then executes them across a Hadoop cluster.
This is a guest post by Oliver Guinan, VP Ground Software, at Skybox Imaging. Oliver is a 15-year veteran of the internet industry and is responsible for all ground system design, architecture and implementation at Skybox.
One of the great promises of the big data movement is using networks of ubiquitous sensors to deliver insights about the world around us. Skybox Imaging is attempting to do just that for millions of locations across our planet.
This is the second article in a series about analyzing Twitter data using some of the components of the Hadoop ecosystem available in CDH, Cloudera’s open-source distribution of Apache Hadoop and related projects. In the first article, you learned how to pull CDH components together into a single cohesive application, but to really appreciate the flexibility of each of these components, we need to dive deeper.
Every story has a beginning, and every data pipeline has a source. So, to build Hadoop applications, we need to get data from a source into HDFS.
We at Cloudera are tremendously excited by the power of data to effect large-scale change in the healthcare industry. Many of the projects that our data science team worked on in the past year originated as data-intensive problems in healthcare, such as analyzing adverse drug events and constructing case-control studies. Last summer, we announced that our Chief Scientist Jeff Hammerbacher would be collaborating with the Mt. Sinai School of Medicine to leverage large-scale data analysis with Apache Hadoop for the treatment and prevention of disease. And next week, it will be my great pleasure to host a panel of data scientists and researchers at the Strata Rx Conference (register with discount code SHARON for 25% off) to discuss the meaningful use of natural language processing in clinical care.
Of course, the cost-effective storage and analysis of massive quantities of text is one of Hadoop’s strengths, and Jimmy Lin’s book on text processing is an excellent way to learn how to think in MapReduce. But a close study of how the applications of natural language processing technology in healthcare have evolved over the last few years is instructive for anyone who wants to understand how to use data science in order to tackle seemingly intractable problems.
Lesson 1: Choose the Right Problem
This guest post is provided by Dan McClary, Principal Product Manager for Big Data and Hadoop at Oracle.
One of the constants in discussions around Big Data is the desire for richer analytics and models. However, for those who don’t have a deep background in statistics or machine learning, it can be difficult to know not only just what techniques to apply, but on what data to apply them. Moreover, how can we leverage the power of Apache Hadoop to effectively operationalize the model-building process? In this post we’re going to take a look at a simple approach for applying well-known machine learning approaches to our big datasets. We’ll use Pig and Hadoop to quickly parallelize a standalone machine-learning program written in Jython.