At PASS Summit 2011 in Seattle, one of the biggest surprise announcements was Microsoft's support for Apache Hadoop as a part of its SQL Server 2012 announcements. Several DBAs I ran into were wondering what exactly Hadoop had to do with SQL Server and if it meant Microsoft was moving away from SQL Server toward one of the new NoSQL implementations that has recently garnered so much press coverage.

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To understand why Hadoop is important and how it relates to SQL Server, we need to get an idea of what Hadoop actually is and what it isn’t. First, Hadoop isn’t a relational database system, so it’s not a replacement or substitute for SQL Server. Hadoop is an open-source project that’s managed by the Apache Software Foundation. It was designed to solve a somewhat different problem—the problem of handling large amounts of unstructured data. SQL Server and other relational databases primarily store structured data. Data can be stored by using the XML and FileStream data types, but there can be limitations to the size, as well as the amount of processing power that can be applied to access the data. The basic technology behind Hadoop was originally developed by Google so that it could index all types of textual information. Google’s ideas were then incorporated into an open-source project named Nutch and later Yahoo! worked to transform Hadoop into an enterprise application. Hadoop is used by several notable companies, perhaps the most recognizable company is Facebook. In 2010, Facebook had the largest Hadoop cluster in the world, with more than 20PB of storage.

Hadoop is written in Java and runs on a collection of commodity shared-nothing servers. You can add or remove servers from a Hadoop cluster at anytime without disrupting the service. The more servers you use, the more computing power you get. A Hadoop implementation consists of two key components: the Hadoop Distributed File System (HDFS), which provides data storage across multiple servers, and high-performance parallel data processing, which uses a technique called MapReduce. MapReduce essentially splits up data discovery and indexing tasks by sending different parts to all of the servers in your cluster. Each server works on its own piece of the data. The results are then delivered back to the user as a complete set. In essence, MapReduce maps the operation out to all of the servers in the cluster and reduces the results into a single result set.

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To implement Hadoop you can buy a collection of commodity servers and run the Hadoop software on each server to create a high-performance Hadoop cluster. For better scalability, you can add more servers. When you load all your data into Hadoop, the software breaks the data into pieces and distributes it across all the available servers. There's no central location in which you access your data. The Hadoop cluster keeps track of where the data resides and automatically stores multiple copies of the data. If a server fails or is removed from the cluster, Hadoop automatically replicates the data from a known copy.

Being an open-source product you might wonder what Hadoop has to do with Windows. At PASS Summit 2011, Microsoft announced that the company had created a Windows version of Hadoop that's able to run on Windows Server for on-premises implementations or on Windows Azure for cloud implementations. In addition, Microsoft is working with HortonWorks to develop bi-directional connectors for Hadoop and SQL Server. The SQL Server connector for Apache Hadoop lets customers move large volumes of data between Hadoop and SQL Server 2008 R2 or SQL Server 2012. There will also be a SQL Server Parallel Data Warehouse (PDW) connector for Hadoop that transfers data between Hadoop and SQL Server PDW. These new connectors will enable customers to work with both structured SQL Server data and unstructured data from Hadoop.

Hadoop isn’t a replacement for SQL Server’s relational database. Instead, it provides new capabilities that weren’t previously unavailable. I think Microsoft’s view is that Hadoop will be used in conjunction with SQL Server’s relational and analytic capabilities to enable enterprises to deploy Hadoop implementations alongside their exiting IT systems. This will extend the types of data that you can use in your applications similarly to how SQL Server Analysis Services (SSAS) does with the SQL Server relational database engine. In addition, it'll help SQL Server better compete with both Oracle and IBM’s DB2, which have also embraced Hadoop. Big data is a rapidly growing trend and the ability to incorporate big data with SQL Server is a big deal.