Server-based OLAP (online analytic processing) products are an increasingly popular component of the data warehouse infrastructure. OLAP servers deliver two primary functions:
In all cases, the best source for an OLAP cube is a dimensional data warehouse stored in an RDBMS. The language of the dimensional data warehouse, dimensions, keys, hierarchies, attributes, and facts, translates exactly to the OLAP world. OLAP engines are developed primarily to support fast and complex querying of dimensional structures. OLAP engines, unlike relational databases and ETL tools, are not designed primarily to cleanse data or ensure referential integrity. Even if your OLAP technology provides features that let you build a cube directly from transactional sources, you should seldom plan to use those features. Instead, you should design a relational data warehouse and populate it using the techniques described in this book. That relational store should feed data to the cube.
Cube Data Sources
The various server-based OLAP products and versions have different features. One area of significant difference is the proximate source of the data that flows into a cube. Some products require that data be sourced from a text file; others require that data be sourced from a single brand of relational database; others permit data to be loaded from practically any source.
If you are sourcing your cube from flat files, one of the last steps in your ETL process is, obviously, to write out the appropriate datasets. Though sourcing from flat files is inelegant and slower than sourcing directly from the relational database, it’s not a terrible performance hit: All relational databases and ETL tools can efficiently write the results of a query to files.
Analyze any queries that your cube processing issues against the relational data warehouse. Ensure that such queries are well tuned. If necessary, add indexes or materialized views to improve the performance of these processing queries.
Just as relational dimensions are processed before the fact tables that use them, so must OLAP dimensions be processed before facts. Depending on which OLAP tool you use, you may have the option of processing dimensions and facts in a single transaction, which rolls back if an error is encountered. This is appealing in theory but tends not to scale very well for a large OLAP database. Most large OLAP systems process dimensions one by one, often as the last step in the ETL module that populates the corresponding relational dimension table.
Your ETL system design needs to be aware of a few characteristics of OLAP dimensions. First, recall that OLAP systems are designed for ease of use and good query performance for queries that navigate up and down the strong dimensional hierarchy or hierarchies (such as Product to Brand to Category). With a classic relational data warehouse, you should ensure referential integrity between hierarchy levels (that is, that a product rolls up to one and only one brand, and so on). But with OLAP tools, you absolutely must ensure referential integrity. The OLAP server will insist on referential integrity between the levels of a strong hierarchy. If a violation is found, the dimension processing will either fail, or, at best, the OLAP server will make an assumption about how to proceed. You don’t want either of these events to occur: Thoroughly clean your data before OLAP processing is launched.
Changes in Dimension Data
OLAPservers handle different kinds of dimension-data changes completely differently. MostOLAPservers handle new dimension rows, such as adding a new customer, as gracefully as the relational dimensional model does. Updating attribute values that do not participate in strong hierarchies and thus do not have permanent aggregations built on them is usually graceful as well. With changes in dimension attributes where attributes are part of the OLAP dimension hierarchy, the ETL designer needs to be very careful.
Let’s get the easy case out of the way first. Type 2 slowly changing dimensions, where a new dimension row with a new surrogate key is added for the changed member, is consumed gracefully by the OLAP servers. From the OLAP server’s point of view, this is simply a new customer, indistinguishable from a completely new customer.
A Type 1 slowly changing dimension that updates in place the strong hierarchical attributes is much harder for OLAPservers to manage. The OLAP servers’ challenges are exactly analogous to those faced by relational data warehouses with aggregate tables built on the same dimension attributes. When the dimension hierarchy is restructured, the existing aggregations are invalidated. The OLAP servers handle this problem with a wide variety of gracefulness, from not noticing the change, to simply throwing away the aggregations and rebuilding them as a background process, to invalidating the dimension and all cubes that use that dimension, forcing a full OLAP database reprocessing. You should look to your OLAP server vendor for detailed information about how this problem is handled in your technology. Vendors are improving this area with each release, so it’s important to use the latest software or validate the conditions for your version.
You should test before OLAP dimension processing to verify that no changes have been made that would put your OLAP database into an invalid or inconsistent state. Depending on the costs and system usage, you could decide to design your system to:
In any case, the extraordinary event should be logged into the ETL error event table and the operator e-mailed or paged.
Many people think of cubes as containing only aggregated data. This perception is becoming as old fashioned as the notion that the data warehouse contains only aggregated data. Server-based OLAP products are capable of managing very large volumes of data and are increasingly used to hold data at the same grain as the relational data warehouse. This distinction is important for the design of the cube-processing portion of the ETL system.
Most server-based OLAP products support some form of incremental processing; others support only full processing. Full cube processing is most appropriate for aggregate cubes or small detailed cubes. For good processing performance on a large volume of detailed data, it is vital to use incremental processing for fact data. Two types of cube incremental processing may be available in your OLAP technology: partition-based and incremental facts.
Loading into a partition is an appealing way to load a subset of the cube’s data. If your OLAP technology supports partitions, and your cube is partitioned by time, usually weekly or monthly,youcan easily load only that time period. If you process daily into weekly partitions, your Monday data will actually be dropped and reloaded seen times during the week until the Sunday load closes down the partition. Certainly, this technique doesn’t maximize load efficiency, but it is perfectly acceptable for many applications. It is common to design OLAP cube partitions with the same periodicity as their corresponding relational partitions and to extend the script that manages the relational partitions to also manage the OLAP partitions.
Partitioning the OLAP cube can provide significant benefits for both query and processing performance. Queries against cubes partitioned by one ormore dimensions can be executed only against the partitions included in the query rather than the whole cube. The advantages of processing are both to support a simple pseudo-incremental processing as described previously and also to support the processing of multiple partitions in parallel. If your cubes use a complex partition design, your ETL system should be designed to launch multiple partition processing jobs in parallel. If yourOLAP server doesn’t manage parallel processing on your behalf, you should design this part of your ETL system so you can process a configurable number of partitions in parallel. This is a parameter to optimize during the systemtesting phase.
Some OLAP servers also support true incremental fact processing. You supply a way to identify the new data (usually Date Processed), and the OLAP server will add it to the cube or cube partition. If you have latearriving facts, incremental processing will almost surely be a better approach for you than reprocessing the current partition.
An alternative to full or incremental processing is for the OLAP engine to monitor the source databases for new transactions and to automatically populate the cube with new data. This is a complex process that requires close integration between OLAP engine and relational engine. At the time of this writing, some rudimentary examples of such a feature are available; more functional features are under development.
Common Errors and Problems
One of the most common errors during fact processing is a referential integrity failure: A fact row being processed does not have a corresponding dimension member. If you follow the advice in this and other Toolkit books and use surrogate keys, you should not confront a fact referential integrity failure in the normal course of processing. Nonetheless, you should educate yourself about how your OLAP server will handle this event should an extraordinary event occur.
OLAP servers are not a natural fit with fact data that is updated in place. On the relational side, the preferred design is to use ledgered fact tables that have entries for fact changes as positive or negative transaction amounts. The ledger design enables auditing of fact changes and for that reason alone is preferred. An OLAP cube built on a ledgered fact table can accept changes gracefully, though the processing logic may need to be complex enough to support late-arriving fact data if the cube is partitioned by time.
By contrast, an OLAP cube built on a fact table that supports in-place updates is very likely to need full processing each time an update occurs. If the cube is small, this may not be a problem. If the cube is large, you should investigate whether it is acceptable to business users to group updates into weekly or monthly batches or else forgo incorporating the updateable subject area into the OLAP database. Note also that the OLAP processing, which is designed to consume new fact rows, will not identify that fact rows have been updated. The ETL system must trigger the extraordinary processing.
Occasional Full Processing
OLAP technology is not, at the time of this writing, as reliable as relational technology. We believe firmly that the relational dimensional data warehouse should be managed as the data warehouse system-of-record. OLAP cubes should be regarded as ephemeral. Many companies go six months, a year, or more without having to fully reprocess their cubes. However, all installations should develop and test procedures for fully reprocessing the OLAP database. A corollary to this stricture is that you should not design an OLAP cube to contain data that is not in the relational data warehouse or is not easily recoverable into the data warehouse. This includes writeback data (most common in budgeting applications), which should be populated directly or indirectly into a relational database.
Until OLAP servers are as reliable and open as their relational brethren, they should be considered secondary systems. OLAP vendors are focusing on reliability and recoverability in versions currently under development, so we hope this second-class status will soon be unnecessary.
Integrating OLAP Processing into the ETL System
If your data warehouse includes OLAP cubes, they should be as professionally managed as any other part of the system. This means that you should have service agreements with business users about data currency and system uptime. Although we generally prefer cubes to be published on the same schedule as relational data, it may be acceptable to refresh cubes on a slower schedule than the relational database. The most important thing is to negotiate an agreement with business users, stick to it, and notify them promptly when the inevitably unexpected occurs.
Although the OLAP server vendors haven’t done a great job of providing tools to manage OLAP databases in a professional way, they have all at the very least provided a command-line tool in addition to the more familiar cube management wizard. If you can launch OLAP processing from a command line, you can integrate it, however weakly, into your ETL system. Technologies that include ETL and OLAP offerings from a single vendor provide more elegant integration.
Many systems can fully process the entire OLAP database on a regular schedule, usually weekly or monthly. In this case, the OLAP processing needs merely to verify that week-end or month-end processing of the relational data warehouse has completed successfully. The ETL system can check the system metadata for that successful condition and, if encountered, kick off the cube processing.
For larger systems, a common integration structure includes adding the OLAP dimension processing as the final step in each dimension table processing branch, module, or package in your ETL system. As described previously, you should test for dangerous data operations that might invalidate a cube before launching the OLAP dimension processing script or command. Similarly, the fact table processing module or branch should be extended to include the OLAP processing. The ultimate step of all processing should update metadata of timing and success (or failure) and post this information to the business community.
If an OLAP database is part of your data warehouse system, it should be managed rigorously. The ETL team should be expert in the processing features and quirks of the corporate OLAP technology and ideally should have input into the choice of that technology. This is especially true if you are following the current trend of including most or all data warehouse data, including fine-grained data, in the OLAP cubes. To truly reap the benefits of fine-grained cubes, the data warehouse team must own and integrate OLAP processing with the more familiar relational-focused ETL system.
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