Why do Column oriented databases such as Vertica/InfoBright/GreenPlum make a fuss of Hadoop?

Question

What is the point in feeding an Hadoop cluster and using that cluster to feed data into a Vertica/InfoBright datawarehouse ?

All thse vendor keep saying "we can connect with Hadoop", but I don't understand what's the point. What is the interest of storing in Hadoop and transfering into InfoBright ? Why not have the applications store directly in the Infobright/Vertica DW ?

Thank you !

Answer 1

Why combine the solutions? Hadoop has some great capabilities (see url below). These capabilities though do not include allowing business users to run quick analytics. Queries that take 30 minutes to hours in Hadoop are being delivered in 10’s of seconds with Infobright.

BTW, your initial question did not presuppose an MPP architecture and for good reason. Infobright customers Liverail, AdSafe Media & InMobi, among others, utilize IEE with Hadoop.

If you register for an Industry White Paper http://support.infobright.com/Support/Resource-Library/Whitepapers/ you will see a view of the current marketplace where four suggested Use Cases for Hadoop are outlined. It was authored by Wayne Eckerson , Director of Research, Business Applications and Architecture Group, TechTarget, in September 2011.

1) Create an online archive.
With Hadoop, organizations don’t have to delete or ship the data to offline storage; they can keep it online indefinitely by adding commodity servers to meet storage and processing requirements. Hadoop becomes a low-cost alternative for meeting online archival requirements.

2) Feed the data warehouse.
Organizations can also use Hadoop to parse, integrate and aggregate large volumes of Web or other types of data and then ship it to the data warehouse, where both casual and power users can query and analyze the data using familiar BI tools. Here, Hadoop becomes an ETL tool for processing large volumes of Web data before it lands in the corporate data warehouse.

3) Support analytics.
The big data crowd (i.e., Internet developers) views Hadoop primarily as an analytical engine for running analytical computations against large volumes of data. To query Hadoop, analysts currently need to write programs in Java or other languages and understand MapReduce, a framework for writing distributed (or parallel) applications. The advantage here is that analysts aren’t restricted by SQL when formulating queries. SQL does not support many types of analytics, especially those that involve inter-row calculations, which are common in Web traffic analysis. The disadvantage is that Hadoop is batch-oriented and not conducive to iterative querying.

4) Run reports.
Hadoop’s batch-orientation, however, makes it suitable for executing regularly scheduled reports. Rather than running reports against summary data, organizations can now run them against raw data, guaranteeing the most accurate results.