Cloudera Developer Blog · Guest Posts
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.
This guest post comes to us courtesy of Gwen Shapira (@gwenshap), a database consultant for The Pythian Group (and an Oracle ACE Director).
Most western countries use street names and numbers to navigate inside cities. But in Japan, where I live now, very few streets have them.
This post was contributed by Bob Gourley, editor, CTOvision.com.
You are no doubt aware of the interesting situation we face with data today: The amount of data being created is growing faster than humans can analyze, but fast analysis over data can help humanity solve some very tough challenges. This fact is moving the globe towards new “Big Data” solutions.
Government use of Big Data is of particular note.
Up to this point, we’ve described our reasons for using Hadoop and Hive on our neural recordings (Part I), the reasons why the analyses of these recordings are interesting from a scientific perspective, and detailed descriptions of our implementation of these analyses using Apache Hadoop and Apache Hive (Part II). The last part of this story cuts straight to the results and then discusses important lessons we learned along the way and future goals for improving the analysis framework we’ve built so far.
Here are two plots of the output data from our benchmark run. Both plots show the same data, one in three dimensions and the other in a two-dimensional density format.
As mentioned in Part I, although Apache Hadoop and other Big Data technologies are typically applied to I/O intensive workloads, where parallel data channels dramatically increase I/O throughput, there is growing interest in applying these technologies to CPU intensive workloads. In this work, we used Hadoop and Hive to digitally signal process individual neuron voltage signals captured from electrodes embedded in the rat brain. Previously, this processing was performed on a single Matlab workstation, a workload that was both CPU intensive and data intensive, especially for intermediate output data. With Hadoop and Apache Hive, we were not only able to apply parallelism to the various processing steps, but had the additional benefit of having all the data online for additional ad hoc analysis. Here, we describe the technical details of our implementation, including the biological relevance of the neural signals and analysis parameters. In Part III, we will then describe the tradeoffs between the Matlab and Hadoop/Hive approach, performance results, and several issues identified with using Hadoop/Hive in this type of application.
For this work, we used a university Hadoop computing cluster. Note that it is blade-based, and is not an ideal configuration for Hadoop because of the limited number (2) of drive bays per node. It has these specifications:
In this three-part series of posts, we will share our experiences tackling a scientific computing challenge that may serve as a useful practical example for those readers considering Apache Hadoop and Apache Hive as an option to meet their growing technical and scientific computing needs. This first part describes some of the background behind our application and the advantages of Hadoop that make it an attractive framework in which to implement our solution. Part II dives into the technical details of the data we aimed to analyze and of our solution. Finally, we wrap up this series in Part III with a description of some of our main results, and most importantly perhaps, a list of things we learned along the way, as well as future possibilities for improvements.
About a year ago, after hearing increasing buzz about big data in general, and Hadoop in particular, I (Brad Rubin) saw an opportunity to learn more at our Twin Cities (Minnesota) Java User Group. Brock Noland, the local Cloudera representative, gave an introductory talk. I was really intrigued by the thought of leveraging commodity computing to tackle large-scale data processing. I teach several courses at the University of St. Thomas Graduate Programs in Software, including one in information retrieval. While I had taught the abstract principles behind the scale and performance solutions for indexing web-sized document collections, I saw an opportunity to integrate a real-world solution into the course.
This is a guest re-post from Datameer’s Director of Marketing, Rich Taylor. The original post can be found on the Datameer blog.
Datameer uses D3.js to power our Business Infographic™ designer. I thought I would show how we visualized the Apache Hadoop ecosystem connections. First using only D3.js, and second using Datameer 2.0.
This is a guest post by Assaf Yardeni, Head of R&D for Treato, an online social healthcare solution, headquartered in Israel.
Three years ago I joined Treato, a social healthcare analysis firm to help treato.com scale up to its present capability. Treato is a new source for healthcare information where health-related user generated content (UGC) from the Internet is aggregated and organized into usable insights for patients, physicians and other healthcare professionals. With oceans of patient-written health-related information available on the Web, and more being published each day, Treato needs to be able to collect and process vast amounts of data – Treato is Big Data par excellence, and my job has been to bring Treato to this stage.
Before the Hadoop era
This is a guest post contributed by Loren Siebert. Loren is a San Francisco entrepreneur and software developer, and is currently the technical lead for the USASearch program.
A year ago I rolled my first Apache Hadoop system into production. Since then, I’ve spoken to quite a few people who are eager to try Hadoop themselves in order to solve their own big data problems. Despite having similar backgrounds and data problems, few of these people have sunk their teeth into Hadoop. When I go to Hadoop Meetups in San Francisco, I often meet new people who are evaluating Hadoop and have yet to launch a cluster. Based on my own background and experience, I have some ideas on why this is the case.
This is a guest post from RichRelevance Principal Architect and Apache Avro PMC Chair Scott Carey.
In Early 2010 at RichRelevance, we were searching for a new way to store our long lived data that was compact, efficient, and maintainable over time. We had been using Hadoop for about a year, and started with the basics – text formats and SequenceFiles. Neither of these were sufficient. Text formats are not compact enough, and can be painful to maintain over time. A basic binary format may be more compact, but it has the same maintenance issues as text. Furthermore, we needed rich data types including lists and nested records.