Big data software deployments might be launched because existing data warehouse systems are beginning to sag under the weight of the data that’s flooding into organizations. But that doesn’t mean data warehouses are all of a sudden obsolete — just that the nature of warehousing data is changing to make room for big data.
“Different styles of data warehouse architecture have come and gone over the years,” said Philip Russom, data management research director at The Data Warehousing Institute (TDWI) in Renton, Wash. “As we move to bigger volumes and diversity of data, we have to again evolve the data warehouse, just as we have in the past.”
Hadoop-based big data systems initially were viewed as potential data warehouse killers, but that sentiment has largely given way to expectations of peaceful coexistence. For example, 78% of 263 IT professionals, business users and consultants surveyed by TDWI in November 2012 said they thought Hadoop systems could be a useful complement to their data warehouses for supporting advanced analytics applications. In addition, 41% saw Hadoop as an effective staging area for information on its way to an enterprise data warehouse (EDW). Asked if Hadoop clusters could fully replace an EDW, more than half of the respondents said no; just 4% said yes.
Russom thinks that using Hadoop to stage data for loading into data warehouses is a “beachhead” for big data technologies in companies. But the staging process itself is one aspect of data warehousing that has changed significantly in recent years, he said. In many cases, raw data is likely to pile up in Hadoop clusters and initially be analyzed there. “In the old days, the data staging area was pretty temporary,” Russom said. “But it has evolved to become a kind of archive.”
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