Decoding the human genome originally took 10 years to process; now it can be achieved in one week – The Economist.
This blog post is written in response to the T-SQL Tuesday post of The Big Data. This is a very interesting subject. Data is growing every single day. I remember my first computer which had 1 GB of the Harddrive. I had told my dad that I will never need any more hard drive, we are good for next 10 years. I bought much larger Harddrive after 2 years and today I have NAS at home which can hold 2 TB and have few file hosting in the cloud as well. Well the point is, amount of the data any individual deals with has increased significantly.
There was a time of floppy drives. Today some of the auto correct software even does not recognize that word. However, USB drive, Pen drives and Jump drives are common names across industry. It is race – I really do not know where it will stop.
Same way the amount of the data has grown so wild that relational database is not able to handle the processing of this amount of the data. Conventional RDBMS faces challenges to process and analysis data beyond certain very large data. Big Data is large amount of the data which is difficult or impossible for traditional relational database. Current moving target limits for Big data is terabytes, exabytes and zettabytes.
Hadoop is a software framework which supports data intensive processes and enables applications to work with Big Data. Technically it is inspired by MapReduces technology, however there is very interesting story behind its name. The creator of the Hadoop had named it Hadoop because his son’s toy elephant was named Hadoop. For the same reasons, the logo of the Hadoop is yellow toy elephant.
There are two very famous companies uses Hadoop to process their large data – Facebook and Yahoo. Hadoop platform can solve problems where the deep analysis is complex and unstructured but needs to be done in reasonable time.
Hadoop is architectured to run on a large number of machines where ‘shared nothing’ is the architecture. All the independent server can be put use by Hadoop technology. Hadoop technology maintains and manages the data among all the independent servers. Individual user can not directly gain the access to the data as data is divided among this servers. Additionally, a single data can be shared on multiple server which gives availability of the data in case of the disaster or single machine failure. Hadoop uses MapReduce software framework to return unified data.
This technology is much simpler conceptually but very powerful when put along with Hadoop framework. There are two major steps: 1) Map 2) Reduce.
In Map step master node takes input and divides into simple smaller chunks and provides it to other worker node. In Reduce step it collects all the small solution of the problem and returns as output in one unified answer. Both of this steps uses function which relies on Key-Value pairs. This process runs on the various nodes in parallel and brings faster results for framework.
Pigs and Hives
Pig is high level platform for creating MapReduce programs with Hadoop. Hive is a data warehouse infrastructure built for Hadoop for analysis and aggregation (summary of the data) of the data. Both of this commands are compilation of the MapReduce commands. Pig procedure language where one describes procedures to apply on the Hadoop. Hives is SQL-like declarative language. Yahoo uses Pigs and Hives both in their Hadoop Toolkit. Here is excellent resource from Lars George where he has compared both of this in detail.
Microsoft and Big Data
Microsoft is committed to making Hadoop accessible to a broader class of end users, developers and IT professionals. Accelerate your Hadoop deployment through simplicity of Hadoop on Windows, and the use of familiar Microsoft products.
- Apache Hadoop connector for Microsoft SQL Server
- Apache Hadoop connector for Microsoft Parallel DataWarehouse
Here is the link for further reading.
I can not end this blog post if I do not talk about the one man from whom I have heard about Big Data very first time.
… and of-course – Happy Valentines Day!
Reference: Pinal Dave (http://blog.sqlauthority.com)