For Cloudera, the first vendor to offer a Hadoop distribution, the answer is
an unequivocal yes. Last November, Cloudera finally exposed its true
sentiments by introducing the Enterprise Data Hub in which Hadoop replaces
the data warehouse, among other things, as the center of an organization’s
data management strategy. In contrast, Hortonworks takes a hybrid approach,
partnering with leading commercial data management and analytics vendors to
create a data environment that blends the best of Hadoop and commercial
software. In short, Cloudera offers revolution, Hortonworks evolution.
You know what? Both are right. To replace existing enterprise data warehouse, the first step is in cohabiting with them.
Original title and link: Does Hadoop replace or augment the enterprise data warehouse?
Stephen Swoyer (tdwi) is summarizing Richard Winter’s research into the topic of cost-based efficiency of Hadoop vs data warehouses:
“Under what circumstances, in fact, does Hadoop save you a lot of money, and
under what circumstances does a data warehouse save you a lot of money?”
The conversation happened at a Teradata event, so you might already guess some of the findings. Anyways without seeing the data it’s difficult to agree or disagree:
In fact, he argued that misusing Hadoop for some types of
decision support workloads could cost up to 2.8x more than
a data warehouse.
Original title and link: Picking the Right Platform: Big Data or Traditional Warehouse?
Nancy Kopp for IBM data magazine:
Why is Hadoop the metaphorical bun for the big data burger?
Well, as Hadoop moved further into production environments,
two very prominent use cases emerged. We at IBM refer to the first use
case as the landing zone. It is an area of the architecture where
organizations are building out the capability
to land all data—both structured and unstructured. […]
The other prominent use case is leveraging Hadoop for archiving
and offloading the data warehouse.
That’s a pretty tasty comparison. But there’s something missing from this sandwich: the gravy—how is data moving between these layers? I’d bet that in most of the cases that’s still Hadoop.
Original title and link: How Hadoop wraps the data warehouse in a savory big data sandwich ( ©myNoSQL)