I kid you not.

What’s clear is that Hadoop has already proven its initial footprint in the enterprise data warehousing (EDW) arena: as a petabyte-scalable staging cloud for unstructured content and embedded execution of advanced analytics. As noted in a recent blog post, this is in fact the dominant use case for which Hadoop has been deployed in production environments.

Yes, traditional (Hadoop-less) EDWs can in fact address this specific use case reasonably well — from an architectural standpoint. But given that the most cutting-edge cloud analytics is happening in Hadoop clusters, it’s just a matter of time — one to two years, tops — before all EDW vendors bring Hadoop into their heart of their architectures. For those EDW vendors who haven’t yet fully committed to full Hadoop integration, the growing real-world adoption of this open-source approach will force their hands.

Where the next-generation EDW is concerned, the petabyte staging cloud is merely Hadoop’s initial footprint. Enterprises are moving rapidly toward the EDW as the hub for all advanced analytics. Forrester strongly expects vendors to incorporate the core Hadoop technologies — especially MapReduce, Hadoop Distributed File System, Hive, and Pig — into their core architectures. Again, the impressive growth in MapReduce as a lingua franca for predictive modeling, data mining, and content analytics will practically compel EDW vendors to optimize their platforms for MapReduce, alongside high-performance support for SAS, SPSS, R, and other statistical modeling languages and formats. We see clear signs that this is already happening, as with EMC Greenplum’s recent announcement of a Hadoop product family and indications from some of that company’s competitors that they have similar near-term road maps.

Enterprises have options. One of the questions I asked the firms I interviewed as Hadoop case studies for my upcoming Forrester report is whether they considered using the tried and true approach of a petabyte-scale enterprise data warehouse (EDW). It’s not a stretch, unless you are a Hadoop bigot and have willfully ignored the commercial platforms that already offer shared-nothing massively parallel processing for in-database advanced analytics and high-performance data management. If you need to brush up, check out my recent Forrester Wave™ for EDW platforms.

Many of the case study companies did in fact consider an EDW like those from Teradata and Oracle. But they chose to build out their Big Data initiatives on Hadoop for many good reasons. Most of those are the same reasons any user adopts any open-source platform: By using Apache Hadoop, they could avoid paying expensive software licenses; give themselves the flexibility to modify source code to meet their evolving needs; and avail themselves of leading-edge innovations coming from the worldwide Hadoop community.

But the basic fact is that Hadoop is not a radically new approach to processing extremely scalable data analytics. You can use a high-end EDW to do most of what you can do with Hadoop with all the core features — including petabyte scale-out, in-database analytics, mixed-workload support, cloud-based deployment, and complex data sources — that characterize most real-world Hadoop deployments. And the open-source Apache Hadoop code base, by its devotees’ own admission, still lacks such critical features as the real-time integration and robust high availability you find in EDWs everywhere.