Archive for the ‘InfoSphere BigInsights’ Category
This blog posts refers to the definition of Big Data commonly in use today. I do not include mainframe-based solutions, which some people might argue tackle Big Data challenges.
Both IBM and Oracle are going after the Big Data market. However, they are taking different approaches. I’m going to take a few moments to have a very brief look at what both companies are doing.
First of all, Oracle have introduced an “appliance” for Big Data. IBM have not. I put the word appliance in quotes because I consider this Oracle appliance to be closer in nature to an integrated collection of hardware and software components, rather than a true appliance that is designed for ease of operation. But the more important consideration is whether an appliance even makes sense for Big Data. There is a decent examination of this topic in the following blog post from Curt Monash and the accompanying comment stream: Why you would want an appliance — and when you wouldn’t. But, regardless of your position on this subject, the fact remains that Oracle currently propose an appliance-based approach, while IBM does not.
The other area I will briefly look at is the scope of the respective vendor approaches. In the press release announcing the Oracle Big Data Appliance, Oracle claim that:
Oracle Big Data Appliance is an engineered system optimized for acquiring, organizing, and loading unstructured data into Oracle Database 11g.
IBM takes a very different approach. IBM does not see its Big Data platform as primarily being a feeder for its relational database products. Instead, IBM sees this as being one possible use case. However, the way that customers want to use Big Data technologies extend well beyond that use case. IBM is designing its Big Data platform to cater for a wide variety of solutions, some of which involve relational solutions and some of which do not. For instance, the IBM Big Data platform includes:
- BigInsights for Hadoop-based data processing (regardless of the destination of the data)
- Streams for analyzing data in motion (where you don’t necessarily store the data)
- TimeSeries for smart meter and sensor data management
- and more
Today, Forrester published its Wave analysis for enterprise Hadoop solutions. It has detailed coverage of the Hadoop solutions from vendors like IBM, MapR, Cloudera, Hortonworks, and others. If you are considering an enterprise Hadoop solution, such as IBM InfoSphere BigInsights, it will make for very interesting reading. You can download a free copy of the report from The Forrester Wave™: Enterprise Hadoop Solutions, Q1 2012.
IBM is actively working on adaptive features for the Map and Reduce phases of its InfoSphere BigInsights product (which is based on Apache Hadoop). In some cases, this involves applying techniques commonly found in mature data management products, and in some cases it involves developing new techniques. While a number of these adaptive features are still under development, there are some features in the product today. For instance, BigInsights currently includes an Adaptive Mapper capability that allows Mappers to successively process multiple splits for a job, and avoid the start-up costs for subsequent splits.
When a MapReduce job begins, Hadoop divides the data into multiple splits. It then creates Mapper tasks for each split. Hadoop deploys the first wave of Mapper tasks to the available processors. Then, as Mapper tasks complete, Hadoop deploys the next Mapper tasks in the queue to the available processors. However, each Mapper task has a start-up cost, and that start-up cost is repeated each time a Mapper task starts.
With BigInsights, there is not a separate Mapper task for each split. Instead, BigInsights creates Mapper tasks on each available processor, and those Mapper tasks successively process the splits. This means that BigInsights significantly reduces the Mapper start-up cost. You can see the results of a benchmark for a set-similarity join workload in the following chart. In this case, the tasks have a high start-up cost. The AM bar (Adaptive Mapper) in the chart is based on a 32MB split size. You can see that by avoiding the recurring start-up costs, you can significantly improve performance.
Of course, if you chose the largest split size (2GB), you would achieve similar results to the Adaptive Mapper. However, the you might potentially expose yourself to the imbalanced workloads that sometimes accompany very large splits.
The following chart shows the results of a benchmark for a join query on TERASORT records. Again the AM bar (Adaptive Mapper) in the chart is based on a 32MB split size.
In this case, the Adaptive Mapper results in a more modest performance improvement. Although, it is still an improvement. The key benefit of these Adaptive MapReduce features is that they eliminate some of the hassles associated with determining the split sizes, while also improving performance.
As I mentioned earlier in this post, a number of additional Adaptive MapReduce features are currently in development for future versions of BigInsights. I look forward to telling you about them when they are released…
In the mean time, make sure to check out the free online Hadoop courses at Big Data University. I previous blogged about my experiences with these courses in Hadoop Fundamentals Course on BigDataUniversity.com.
Here is a chart that compares the performance of Hadoop Distributed File System (HDFS) with General Parallel File System-Shared Nothing Cluster (GPFS-SNC) for certain Hadoop-based workloads (it comes from the Understanding Big Data book). As you can see, GPFS-SNC easily out-performs HDFS. In fact, the book claims that a 10-node GPFS-SNC-based Hadoop cluster can match the performance of a 16-node HDFS-based Hadoop cluster.
GPFS was developed by IBM in the 1990s for high-performance computing applications. It has been used in many of the world’s fastest computers (including Blue Gene and Watson). Recently, IBM extended GPFS to develop GPFS-SNC, which is suitable for Hadoop environments. A key difference between GPFS-SNC and HDFS is that GPFS-SNC is a kernel-level file system, whereas HDFS runs on top of the operating system. This means that GPFS-SNC offers several advantages over HDFS, including:
- Better performance
- Storage flexibility
- Concurrent read/write
- Improved security
If you are interested in seeing how GPFS-SNC performs in your Hadoop cluster, please contact IBM. Although GPFS-SNC is not in the current release of InfoSphere BigInsights (IBM’s Hadoop-based product), GPFS-SNC is currently available to select clients as a technology preview.
Last week, I included a demonstration of Using Hadoop to Extract and Analyze Unstructured Information. Now I’d like to share another demo. This demo also shows InfoSphere BigInsights and InfoSphere BigSheets. BigInsights is essentially Apache Hadoop together with extensions for installation, management, security, and integration, while BigSheets is basically an easy-to-use interface for creating and running Map and Reduce jobs.
This demo shows you how to run sentiment analysis on Tweets. Some of the details of creating the specific text analytics are not included. But it is interesting and useful nontheless. It also shows how you can easily run some cool visualizations on that data. Make sure to keep watching until the end where David Barnes show a great visualization on the UK Parliment data.
Don’t forget there is no charge for BigInsights Basic Edition. You can freely download it from InfoSphere BigInsights.
Here’s a nice demo. It shows InfoSphere BigInsights, which is IBM’s Hadoop product. BigInsights is essentially Apache Hadoop together with extensions for installation, management, security, integration, and so on. The demo also shows InfoShpere BigSheets. BigSheets is basically an easy-to-use interface for creating and running Map and Reduce jobs. As you can see from the demo, BigSheets makes it quick and easy to apply text analytics extractors and filters to unstructured or semi-structured data. The demo itself shows how you can quickly analyze several aspects of revenue information pulled from earnings press releases. It even includes a nice round-trip to the annotated source data to see “why” certain conditions occurred.
Don’t forget there is no charge for BigInsights Basic Edition. You can freely download it from InfoSphere BigInsights.
After spending some time reading about Apache Hadoop, I decided it was time to get my hands dirty. So this weekend, I took the Hadoop Fundamentals 1 self-paced course on BigDataUniversity.com. It is a really nice way to play with Hadoop. You have the choice of downloading the software and installing it on your computer, working with a VMware image, or working in the cloud. I chose the option of working in the cloud. Within a few minutes I had a Amazon AWS account, a RightScale account, and the software installed in the cloud. By the way, although the course is FREE, I did incur some cloud-related usage charges. It amounted to approximately $1 in Amazon charges for the time it took me to complete the course.
If you are curious about Hadoop, I’d recommend this course. I’m eagerly anticipating the availability of the follow-on Hadoop course…
Here’s a short video that was recorded at the IDUG conference, where I talk about the characteristics of Big Data solutions, discuss some of the technologies involved, and describe some real world Big Data solutions that IBM has implemented. Its a high-level introduction, but if you’re not sure what this “Big Data” term refers to, you may find it useful.
In the video, I try to quantify what “big” means today, as well as describing some lessons we have learned while implementing Big Data solutions. Technologies introduced include Map/Reduce systems, systems for analyzing streaming data, Massive Parallel Processing data warehouse systems, and in-memory database systems.
Those of you that know me in person, will see that I was a little under-the-weather when the video was recorded. You can hear it in my voice, see it in my demeanor, and notice it in my cadence. I hope you can get past this, and find this video useful.
As many of you know, IBM has been making big investments in Big Data. This includes InfoSphere BigInsights (which is based on Apache Hadoop), InfoSphere Streams, IBM Netezza, and more than $14B in analytics-based acquisitions. IBM is now announcing a set of hands-on workshops that will be held around the world to help you get to grips with Big Data. There will be 1,200 of these free workshops held in more than 150 cities in 60 countries in 2011. For more information, see IBM Launches Global Bootcamps to Help Companies Tackle Big Data Challenges.
Yesterday, IBM issued a press release that Unveils Software and Services to Help Organizations Make Sense of Their Deluge of Data. There is a lot of information in the press release. Basically, IBM is announcing IBM InfoSphere BigInsights, which is based on Apache Hadoop. So, in other words, IBM is announcing an offering that allows you to work with pedabytes of data. At present, IBM InfoSphere BigInsights consists of:
- BigInsights Core, which is software and services for implementing Apache Hadoop
- BigSheets, which acts as an insight engine for information in Hadoop. It’s a Web-based spreadsheet-like infrastructure for Big Data that includes a plug-in framework for analysis and presentation extensions. You can use BigSheets for extracting information, adding annotations, visualizing with pie charts, visualizing with tag clouds, and so on.
- Industry-specific solutions for finance, risk management, and media.
You might be wondering what IBM is bringing to the table here, aside from experience with deploying Hadoop-based solutions. Well, IBM sees its role as making Hadoop enterprise-ready. This includes the kinds of things that IBM is good at, like creating software with robust quality, accessibility, and localization. But it also includes adding key features that allow you to fully leverage the information in a Hadoop environment.
IBM is working to provide integration with DBMS, ETL, and MDM systems. Remember, ideally you want such environments to work with both existing and new data repositories. After all, you don’t want to create yet another silo of information within your organization. It is only by working with all information at your fingertips that organizations can see the full picture, and make good business decisions. Which leads me nicely to the other big thing that IBM brings to the table–the ability to add business value to the Hadoop deployment with Cognos, SPSS, and ECM application layers.
You can see more coverage on this topic, including Cloudera’s reaction at IBM punts commercial Hadoop distro.