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Rick van der Lans

Welcome to my blog where I will talk about a variety of topics related to data warehousing, business intelligence, application integration, and database technology. Currently my special interests include data virtualization, NoSQL technology, and service-oriented architectures. If there are any topics you'd like me to address, send them to me at rick@r20.nl.

About the author >

Rick is an independent consultant, speaker and author, specializing in data warehousing, business intelligence, database technology and data virtualization. He is managing director and founder of R20/Consultancy. An internationally acclaimed speaker who has lectured worldwide for the last 25 years, he is the chairman of the successful annual European Enterprise Data and Business Intelligence Conference held annually in London. In the summer of 2012 he published his new book Data Virtualization for Business Intelligence Systems. He is also the author of one of the most successful books on SQL, the popular Introduction to SQL, which is available in English, Chinese, Dutch, Italian and German. He has written many white papers for various software vendors. Rick can be contacted by sending an email to rick@r20.nl.

Editor's Note: Rick's blog and more articles can be accessed through his BeyeNETWORK Expert Channel.

If one thing became clear to me at the Strata Conference this month was that the popularity of Hadoop is unmistakable and that SQL-on-Hadoop follows closely in its footsteps. A SQL-on-Hadoop engine makes it possible to access big data, stored in Hadoop HDFS or HBase, using the language so familiar to many developers, namely SQL. SQL-on-Hadoop also makes it easier for popular reporting and analytical tools to access big data in Hadoop.

Tools that have been offering access to non-SQL data sources using SQL for a long time are the data virtualization servers. Most of them allow SQL access to data stored in spreadsheets, XML documents, sequential files, pre-relational database servers, data hidden behind APIs such as SOAP and REST, and data stored in applications such as SAP and Salesforce.com.

Most of the current SQL-on-Hadoop engines offer only SQL query access to one or two data sources: HDFS and HBase. Sounds easy, but it's not. The technical problem they have to solve is how to turn all the non-relational data stored in Hadoop, such as, variable data, self-describing data, and schema-less data , into flat relational structures.

However, the question is whether offering query capabilities on Hadoop is sufficient, because the bar is being raised for SQL-on-Hadoop engines. Some, such as SpliceMachine, offer transactional support on Hadoop in addition to the queries. Others, such as Cirro and ScleraDB, support data federation: data stored in SQL databases can be joined with Hadoop data. So, maybe offering SQL query capabilities on Hadoop will not be enough anymore in the near future.

Data virtualization servers have started to offer access to Hadoop as well, and with that they have entered the market of SQL-on-Hadoop engines. When they do, they will raise the bar for SQL-on-Hadoop engines even more. Current data virtualization servers are not simply runtime engines that offer SQL access to various data sources. Most of them also offer data federation capabilities for many non-SQL data sources , a high-level design and modeling environment with lineage and impact analysis features, caching capabilities to minimize access of the data source, distributed join optimization techniques, and data security features.

In the near future, SQL-on-Hadoop engines are expected to be extended with these typical data virtualization features. And data virtualization servers will have to enrich themselves with full-blown support for Hadoop. But whatever happens, the two markets will slowly converge into one. Products will merge together and others will be extended. This is definitely a market to keep an eye on in the coming years.

Posted February 24, 2014 3:39 AM
Permalink | 2 Comments |


Great blog post!

Great post! In the world of Hadoop and NoSQL, the spotlight is now on SQL-on-Hadoop engines. Today, many different engines are available, making it hard for organizations to choose. This article presents some important requirements to consider when selecting one of these engines. With SQL-on-Hadoop technologies, it's possible to access big data stored in Hadoop by using the familiar SQL language. Users can plug in almost any reporting or analytical tool to analyze and study the data. Before SQL-on-Hadoop, accessing big data was restricted to the happy few. You had to have in-depth knowledge of technical application programming interfaces, such as the ones for the Hadoop Distributed File System, MapReduce or HBase, to work with the data. Now, thanks to SQL-on-Hadoop, everyone can use his favorite tool. For an organization, that opens up big data to a much larger audience, which can increase the return on its big data investment. More at www.youtube.com/watch?v=1jMR4cHBwZE

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