Big Data is hard to avoid – what does Microsoft’s embrace of Hadoop mean for IT Managers?
There are two words that seem particularly difficult to avoid at the moment: big data. Infrastructure guys instinctivly shy away from data but such is its prevalence that big data is much more than just the latest IT buzzword and is becoming a major theme in our industry right now
But what does “big data” actually mean? It’s one of those phrases that, like cloud computing earlier, it is being “adopted” by vendors to mean whatever they want it to.
The McKinsey Global Institute describes big data as “the next frontier for innovation, competition and productivity” but, put simply, it’s about analysing masses of unstructured (or semi-structured) data which, until recently, was considered too expensive to do anything with.
That data comes from a variety of sources including sensors, social networks and digital media and it includes text, audio, video, click-streams, log files and more. Cynics who scoff at the description of “big” data (what’s next, “huge” data?) miss the point that it’s not just about the volume of the data (typically many petabytes) but also the variety and frequency of that data. Some even refer to it as “nano data” because what we’re actually looking at is massive sets of very small data.
Processing big data typically involves distributed computer systems and one project that has come to the fore is Apache Hadoop – a framework for development of open-source software for reliable, scalable distributed computing.
Over the last few weeks though, there have been some significant announcements from established IT players, not all of whom are known for embracing open source technology. This indicates a growing acceptance for big data solutions in general and specifically for solutions that include both open- and closed- source elements.
When Microsoft released a SQL Server-Hadoop (SQOOP) Connector,there were questions about what this would mean for CIOs and IT Managers who may previously have viewed technologies like Hadoop as a little esoteric.
The key to understanding what this would mean would be understanding the two main types of data: structured and unstructured. Structured data tends to be stored in a relational database management system (RDBMS), for example Microsoft SQL Server, IBM DB2, Oracle 11G or MySQL.
By structuring the data with a schema, tables, keys and all manner of relationships it’s possible to run queries (with a language like SQL) to analyse the data and techniques have developed over the years to optimise those queries. By contrast, unstructured data has no schema (at least not a formal one) and may be as simple as a set of files. Structured data offers maturity, stability and efficiency but unstructured data offers flexibility.
Secondly, there needs to be an understanding of the term “NoSQL”. Commonly misinterpreted as an instruction (no to SQL), it really means not only SQL – i.e. there are some types of data that are not worth storing in an RDBMS. Rather than following the database model of extract, transform and load (ETL), with a NoSQL system the data arrives and the application knows how to interpret the data, providing a faster time to insight from data acquisition.
Just as there are two main types of data, there are two main types of NoSQL system: key/value stores (like MongoDB or Windows Azure Table Storage) can be thought of as NoSQL OLTP; Hadoop is more like NoSQL data warehousing and is particularly suited to storing and analysing massive data sets.
One of the key elements towards understanding Hadoop is understanding how the various Hadoop components work together. There’s a degree of complexity so perhaps it’s best to summarise by saying that the Hadoop stack consists of a highly distributed, fault tolerant, file system (HDFS) and the MapReduce framework for writing and executing distributed, fault tolerant, algorithms. Built on top of that are query languages (live Hive and Pig) and then we have the layer where Microsoft’s SQOOP connector sits, connecting the two worlds of structured and unstructured data.
The trouble is that SQOOP is just a bridge – and not a particularly efficient one either – working on SQL data in the unstructured world involves subdivision of the SQL database so that MapReduce can work correctly.
Because most enterprises have both the structured and unstructured data, we really need tools that allow us to analyse and manage data in multiple environments – ideally without having to go back and forth. That’s why there are so many vendors jumping on the big data bandwagon but it seems that a SQOOP connector is not the only work Microsoft is doing in the big data space:
In our increasingly cloudy world, infrastructure and platforms are rapidly becoming commoditised. We need to focus on software that allows us to derive value from data to gain some business value. Consider that Microsoft is only one vendor, then think about what Oracle, IBM, Fujitsu and others are doing. If you weren’t convinced before, maybe HP’s Autonomy purchase is starting to make sense now?
Looking specifically at Microsoft’s developments in the big data world, it therefore makes sense to see the company get closer to Hadoop. The world has spoken and the de facto solution for analysing large data sets seems to be HDFS/MapReduce/Hive (or similar).
Maybe Hadoop’s success comes down to HDFS and MapReduce being based on work from Google whilst Hive and Pig are supported by Facebook and Yahoo respectively (i.e. they are all from established Internet businesses). But, by embracing Hadoop (together with porting its tools to competitive platforms), Microsoft is better placed to support the entire enterprise with both their structured and unstructured needs.
[This post was originally written as an article for Cloud Pro.]