In the early days of data warehousing, there was a raging dispute between 2 architectural methods. There was a camp that promoted Ralph Kimball’s federated data mart architecture, and a camp that promoted Costs Inmon’s enterprise data warehouse architecture.
The old “Kimbalite” vs “Inmonite” conversations of the 1990’s are reminiscent of a comparable discussion going on today about the relative benefits and promise of Hadoop versus conventional data warehouses built on relational databases. And I believe the issue will certainly get fixed in a comparable fashion. Individuals will certainly get tired of discussing it, and both architectures will certainly co-exist in ideal harmony. Each will certainly find its’ appropriate area in the corporate IT landscape.
There are compelling arguments on each side of the concern. Hadoop’s totally free open source distributions run on low cost commodity hardware, and offer virtually limitless warehouse of organized and unstructured data. Nevertheless, few organizations have stable, production- all set Hadoop deployments. And the tools and innovations presently available for accessing and evaluating Hadoop data are in early stages of maturity. There are concerns related to inquiry performance, the ability to carry out real time analytics, and the preference of business analysts and developers to take advantage of existing SQL abilities.
In spite of these to-be-expected early stage difficulties, I am coming across some real world use cases for Hadoop-based analytics. At a recent Silicon Valley Forum on Big Data, Pandora’s director of software engineering discussed how they have moved their relational data warehouse to an analytic infrastructure built on Hadoop, using Tableau as the front end to Hive for visualization and evaluation.
Data warehouse stand for the well-known technology, and they aren’t likely to go away. Nearly all medium to large scale ventures have data warehouse and marts in place that took years to construct, and they are providing undoubted business value. The old axiom “if it ain’t broke, do not repair it” is difficult to argue with. However, data warehouse are not designed to accommodate the increasing volumes of unstructured data from web logs, social networks, cell phones, sensing units, clinical equipment, commercial machines, and other sources. And there are both economic and efficiency constraints on the amount of data that can be kept and accessed.
The current market argument about the relative merits of Hadoop and data warehouses is as vibrant as the data warehouse architecture disputes of the 90’s, but possibly a bit less questionable and enthusiastic. Co-existence seems to be the currenting sentiment among the majority of specialists, along with the vendors of both Hadoop distributions and conventional data warehousing innovations. Cloudera, Hortonworks, MapR, and more recent Hadoop distro suppliers varying from Intel to WanDisco are promoting side-by-side utilize case scenarios, while IBM, Oracle, and Teradata are incorporating Hadoop into their core offerings.
So what’s it going to require to stir up more controversy and interest into the argument? New developments that make Hadoop data more available, more usable, and more relevant to company users will obfuscate the distinctions in between Hadoop and the conventional data warehouse. As the lines blur, the debate will intensify. Those innovations are concerning market at a quick and angry rate, requiring organizations to make architectural decisions that will essentially identify how effectively they can exploit Big Data.
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