Dimensional Relational vs. OLAP: The Final Deployment Conundrum
The choice between deploying relational tables or OLAP cubes is not a trivial matter. Weigh these 34 pros and cons of each approach early in the design of your extract-transform-load system.
The big objection to OLAP is the proprietary, non-standard character of each vendor’s OLAP offering.
Don’t expect to port an OLAP implementation to another vendor’s product. Discard everything including all your application development
There is no accepted, universally implemented access language for OLAP, although Microsoft’s MDX is the closest thing to a standard access language. Significantly, Oracle has not embraced MDX, preferring to rely on SQL for all forms of database access.
MDX in its full glory may be too complex for IT personnel to write by hand, or understand a complex application. But to be fair, I have looked at incomprehensible SQL many times!
Historically there has been much less industry expertise in MDX than in SQL although Microsoft has successfully sponsored a cottage industry of training organizations.
OLAP application development expertise is fragmented by vendor.
OLAP cubes can be catastrophically invalidated if you are not careful, i.e. a Type 1 SCD change to a dimension may cause all the cubes using that dimension to rebuild!
OLAP cubes are not considered stable enough for serious archiving and backup: this is a strong reason for creating a set of dimensional relational tables duplicating the content of the cube for those purposes.
OLAP vendors have certain size limits not present in relational implementations including possibly the number of members in a dimension, the number of distinct values at various levels in a hierarchy and the overall size of the cube.
When you must rebuild a cube, it may be a VERY time consuming process.
Equally Easy in Either Approach
The basic foundations of dimensional modeling, including slowly changing dimensions, the three fundamental grains of fact tables, and achieving integration through conformed dimensions, are equally easy to deliver with either approach, especially since these dimensional structures are carried to the very last step of the ETL pipeline.
Making the Final Choice
So how do senior management and the enterprise BI system designers resolve the final deployment conundrum: Dimensional relational or OLAP? As I hope you appreciate, there is no slam dunk answer since there are significant advantages and disadvantages for both approaches. But let’s consider two extremes. If you are a large distributed enterprise with a number of different database vendors and you are struggling to establish more commonality across your BI deployments, as well as creating an enterprise wide pool of BI development expertise without being beholden to any single vendor, then I recommend dimensional relational. On the other hand, if you are looking for the most potent single-subject solution with high performance and killer analytics, and you are confident that you can source the development expertise you need, then I recommend OLAP supported by underlying dimensional relational schemas. Otherwise, it depends.
Ralph Kimball, founder of the Kimball Group, teaches dimensional data warehouse and ETL design through Kimball University and reviews large warehouses. He has four best-selling data warehousing books in print, including The Data Warehouse ETL Toolkit. Write to him at firstname.lastname@example.org.
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