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TDWI Blog: Data 360

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Q&A RE: Data Warehouse Architecture Issues

Attendees of a recent TDWI Webinar asked excellent questions.
By Philip Russom, TDWI Research Director for Data Management

Recently, on Tuesday April 15, 2014, I broadcasted a TDWI Webinar in which I presented some of the findings from my new TDWI report, Evolving Data Warehouse Architectures in the Age of Big Data. You can download a free copy of the report in a PDF file. And you can replay the Webinar.

Attendees of the Webinar posed several very good questions about various issues in data warehouse architecture. Please allow me to share a few of the attendees’ questions and the answers I sent them via e-mail:

Q. As we update our data warehouse from more reporting to more analytics functions, should we design a brand new data warehouse architecture, or improve from the existing one?

If the existing data warehouse and its architecture fulfill business requirements and technical performance requirements (for speed and scale), then you should try to build out the existing architecture. For that to work, your existing vendor platform under the warehouse must perform well with multiple mixed workloads, including analytic workloads; ask your vendor representative for customer references who’ve succeeded with mixed workloads. Also, building up data sets for advanced analytics typically means loading large data volumes into the warehouse, which may cost more money with some licenses; again, ask your vendor if there are such ramifications under your current license. 

If your current core warehouse platform cannot support mixed workloads with high performance (or adding analytic data costs too much money), you may decide to manage and process large data sets for advanced analytics on a separate standalone platform that integrates with your warehouse. But in that case, you still keep your existing data warehouse and most of its data structures intact, just making slight changes for better integration with the new additional platform(s) for advanced analytics.

Q. Given the lack of integration across this multi-platform [data warehouse] environment, how do we avoid the need to replicate DW transactional sources into the big data platforms, as transactions are required in mining?

Good question, and there are number of issues here. First, a well-designed multi-platform environment won’t suffer a “lack of integration.” TDWI’s definition of “logical data warehouse” is that the logical design specifies integration schemes (not just data models) across physically distinct platforms, whether that integration takes a data model approach (as in shared or conformed dimensions, etc.) or a data integration approach (as in jobs for ETL, replication, etc.) or both. Second, I take your point, that replicating data more than needed can lead to a variety of problems, as data gets out of sync and loses integrity. A good architecture can minimize replication, and sometimes alleviate it. Third, for decades, users have faced the same decision you’re looking at: do we store, manage, and analytically process our rich, valuable collection of transactional data in the warehouse proper or on a standalone but integrated platform, such as the usual operational data store (ODS)?

For years, a solution I’ve seen users successfully adopt is to deploy a homegrown ODS that they’ve designed and optimized for transactions. The ODS is on a standalone platform that’s integrated with the core warehouse (plus other ODSs, marts, etc.), running on a relational DBMS atop commodity priced hardware. Note that the upcoming trend is toward ODSs atop Hadoop (but only if the data volumes are massive). The idea is to manage transactional data on a platform that’s much cheaper than the DW, on a standalone platform where the relentless sorting, updating, and processing of that data won’t degrade warehouse performance. Yet, the ODS is easily reached from all tools, plus through data federation and virtualization as well, which minimizes the replication of transactional data.

If you give the ODS the capacity it needs to persist multiple sort orders and data subsets in the ODS, then copying data outside the ODS is further reduced. Also, if you use data mining tools that can work on data “in situ” (i.e., in the ODS’s relational database) without moving data to the tool, then that also reduces copying and moving transactional data.

Q. The need for data warehouses is never going to go away. But isn’t the separation between "operations" and "analytics" starting to blur? In other words, the future isn't DWE; it's a "data environment" that does both.

Operational BI is all about getting operational data into BI faster and more frequently, while also embedding BI functions in operational applications and their processes as well. Operational BI is a very popular practice. It has been for years, and will get even more popular, as organizations adjust their BI efforts to bring them closer to real time (to be more competitive, customer conscious, efficient, etc.). The widespread existence of operational BI corroborates that the line between operations and BI is already quite blurred and will become even more so.

In another trend, many organizations are purposefully evolving toward a more or less loosely unified data environment for most enterprise data. I say “more or less” and “loosely” because early adopters are quick to say that the architecture is not 100 percent of the enterprise and integration is spotty, on an “as needed” basis. As one architect joked, “it’s more archaeology than architecture, because the work usually consists of imposing a logical architecture over mature, preexisting systems.” For early adopters, it makes sense to architect data globally, when customer data and some other data domains are pervasively shared across multiple applications, departments, and processes. It also makes sense in firms where business processes ramble across multiple business units and IT systems. Obviously, there’s an infinitude of resulting enterprise data architectures.

The data warehouse environment (DWE) I’m describing is a local microcosm of such a broad and loosely unified multi-platform data architecture. However, in some organizations today, the data warehouse and similar data platforms are just a few among many other data platforms, integrated on an enterprise scale. But those organizations are as yet the minority, although we at TDWI expect it to be the norm for IT-intense organizations within five years. TDWI’s Vegas conference has been devoted to issues in enterprise-scale data architecture for years, and will continue to be. You might consider attending next February.

Q. Can you point us to white papers on the difference between reporting and analytics [and how that affects DW architecture]?

You can read my blog on the subject. Or you could read the new report on evolving data warehouse architectures, because I adapted material from the blog to become a section in the report, starting on page 24.

Q. What’s the role, or is there a role, for variants like an ODS in the new world [of data warehouse architectures]? Is it part of the real-time world?”

Historically, some of the first standalone systems in a multi-platform data warehouse (going back to the mid-1990s) were ODSs deployed on their own hardware sever with their own DBMS instances. These are still with us, and will continue to be with us, as data warehouse environments evolve into even more platforms used at once. An ODS can be designed and optimized by users for a wide range of data domains and uses (including real-time data), but I’m currently seeing a lot of users deploying ODSs for various types of big data and other data earmarked for advanced analytics.

Q. Saying Inmon vs. Kimball is no longer relevant is like saying Newton is no longer relevant in the world of physics today. It's still important, maybe not as fundamental as 1–2 decades ago.

For decades, Newton practiced alchemy in his copious spare time, because he was convinced that changing lead to gold was possible. Our heroes aren’t always 100 percent right.

Concerning Inmon and Kimball, see the top of page 7 in the report. Also please read the User Story on that same page. “No longer relevant” is your phrase, not mine. In my view, Inmon and Kimball’s innovations are as relevant as ever, and are still being applied daily. And they just keep giving: Inmon has recently extended our understanding of unstructured data and Kimball is currently working new best practices for Hadoop.

It’s the users who’ve changed. Instead of arguing about which to choose, users choose to apply Inmon and Kimball techniques (and others, too) in the same extended warehouse environment. And that’s a wise choice on their part, since hybrids and diversity seem to be winning strategies for a growing number of user organizations and their diversified DW architectures nowadays.

Q. Some organizations consider Hadoop a replacement for their current DW appliance. How is this possible?

As I said in the Webinar, I’ve only found two organizations that took out a data warehouse and put Hadoop in its place. While that corroborates that a replacement is possible, it’s not likely, nor is it a compelling trend.

Instead of replacement, we at TDWI see far more users augmenting their data warehouse environment with the Hadoop Distributed File System (HDFS), plus related Hadoop tools, especially MapReduce, Hive, HBase, and Pig. In short, HDFS handles things that relational warehouses are not designed for, such as unstructured data, algorithmic analytics, millions of files, and petabyte-size data sets. But the relational warehouse is still best for the structured and multidimensional data that goes into standard reports, performance management, and set-based analytics (typically OLAP or SQL-based analytics).

Another possibility is that Hive atop MapReduce and HDFS makes a highly scalable “row store” type of database. Sometimes you don’t need a full-featured (and expensive) relational DBMS, and hence a row store will do just fine. For example, many of the ODSs found today in data warehouse environments are candidates for migration to Hadoop. That includes ODSs that manage large “archives” (I use the word loosely) of transactional data and other operational data that’s persisted and kept long-term for advanced analytics that just need simple tabular structures. Most standalone ODSs of that description today run on mature DBMSs, but could run almost as well (for less money) on Hadoop.

Finally, let’s remember that not all organizations need a data warehouse, as represented by 15 percent of survey respondents.

Q. Can you recommend any sample success stories on how to integrate Hadoop or similar big data into an existing data warehouse [environment]?

Yes, many real-world use cases and user stories are discussed in the 2013 TDWI report Integrating Hadoop into Business Intelligence and Data Warehousing.

Posted by Philip Russom, Ph.D. on April 30, 2014


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