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Big Data Analytics: The News from Informatica

Blog by Philip Russom
Research Director for Data Management, TDWI

Early this morning, Informatica Corporation announced Informatica HParser, a new product for parsing data in Apache Hadoop environments. Instead of repeating the details of the announcement – which you can read on www.informatica.com, etc. – I’d rather use the announcement as a springboard for my own thoughts about the bigger trends and issues in Big Data Analytics and Hadoop that the announcement fits into. The catch is that there are so many myths and misconceptions (i.e., “mythconceptions”) about Hadoop right now, that I can’t bust them all in a short piece like this blog. So I’ll just present the two leading mythconceptions as background, plus a brief rant for color.

First Mythconception. Hadoop is not one, monolithic thing, so we need to stop talking about it that way. It’s actually an open source software library administered by the Apache Software Foundation. (Some Hadoop products are also available via vendor distributions; but that’s another story.) The Apache Hadoop library includes several products and technologies, including (in BI priority order) the Hadoop Distributed File System (HDFS), MapReduce, Hive, Hbase, Pig, Zookeeper, Flume, Sqoop, Oozie, Hue, and so on. It’s up to you to figure out which combination of Apache Hadoop products to implement for a given application. For applications in business intelligence (BI) and Big Data Analytics, HDFS and MapReduce (perhaps with Hbase and Hive) constitute a useful technology stack.

Second Mythconception. Theoretically, HDFS can manage the storage and access of any data type, as long as you can put the data in a file and copy that file into HDFS. As outrageously simplistic as that sounds, it’s largely true, and it’s exactly what brings many users to Apache HDFS in the first place. Yet, HDFS’s admirable tolerance for diverse data doesn’t mean that an Apache Hadoop environment operates equally well with all file and data types. According to users I’ve interviewed, if you expect to get speed, scalability, and development simplicity, you need to work with Hadoop’s preference for record-based data. That’s not as limiting as it sounds, because many types of Big Data handled by HDFS are inherently record-based, as in logs from Web servers and sensors or table dumps of call detail records, customer records, transactions, etc. Furthermore, many sources of traditional enterprise data can be converted to records and copied to HDFS for Big Data Analytics and other applications.

And that brings us to Informatica Corporation’s announcement today of the new Informatica HParser. In a Hadoop environment, it’s MapReduce that actually executes the programmatic logic of an application. In the context of Big Data Analytics, the logic is (today) usually hand-coded data transformations or analytic logic. HParser provides an integrated development environment (IDE) for creating data transformation logic, plus ties into MapReduce to ensure that the logic executes in a fully distributed and parallel fashion. Given Apache Hadoop’s preference for record-based data, use cases cited by Informatica focus on how HParser can convert unstructured data into records and tables, plus flatten overly structured or “complex” data (as in the hierarchies of XML and JSON) into records that are more palatable to HDFS and Apache MapReduce. Record structures aside, Informatica HParser also supports a long list of data standards and document types. And Informatica PowerExchange for Hadoop provides additional functionality.

A brief rant. If you’ve been reading my writings on data integration for the last ten years, you know that I consider hand-coded data integration to be non-productive. Hand coding is time-consuming, not very re-usable, hard to update, and inherently feature-poor compared to vendor platforms. Now, we’re faced with Apache MapReduce, which – out of the box – demands huge amounts of hand coding, because it’s a processing engine that manages and provides parallelization for hand-coded routines (whether for analytics, DI, or otherwise). Informatica HParser shows promise for reducing the non-productive hand-coding that open-source environments like Hadoop, MapReduce, and Hive assume.

Conclusion. I feel that the men and women who’ve contributed to open source Hadoop have made an impressive and innovation contribution. And the Apache Software Foundation does a great job enabling the open source community. Thanks to these contributions, Hadoop is successfully used in production, but mostly in large, Internet-based businesses, like Amazon, Comscore, eBay, Google, and LinkedIn. However, for the Hadoop family – and the Big Data Analytics it enables – to become truly useful in a wide range of mainstream organizations across multiple industries, I think that the Hadoop family needs a number of new extensions, improvements, and options for interoperability.

This is why we’re now seeing software vendor companies coming out with various types of support for Apache Hadoop products and technologies. Informatica’s HParser and Informatica PowerExchange for Hadoop are prime examples, and other DI vendors will soon follow suit with similar interfaces and extensions for Hadoop. Some vendors are building administrative tools, which HDFS sorely lacks. And BI and analytic tool vendors are scrambling to sit atop HDFS and MapReduce. Personally, I hope to see more support for Hadoop and soon, because, without it, mainstream user organizations can’t get full value from Hadoop. Hence, they may not adopt it.

So, what do you think? Let me know!

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Do you suffer mythconceptions about Hadoop? If so, TDWI can help you bust them:
• TDWI will soon publish my new Checklist Report on Hadoop, available as a free download on tdwi.org, starting Dec.13, 2011.
• On Dec.14, 2011, I’ll broadcast a TDWI Webinar based on that report. Please register online for the Hadoop Webinar.

Posted by Philip Russom, Ph.D. on November 2, 2011


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