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Subsystems of ETL Revisited

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Subsystems of ETL Revisited

The Kimball Group has been exposed to hundreds of successful data warehouses. Careful study of these successes has revealed a set of extract, transformation, and load (ETL) best practices. We first described these best practices in an Intelligent Enterprise column three years ago. Since then we have continued to refine the practices based on client experiences, feedback from students and continued research. As a result, we have carefully restructured these best practices into 34 subsystems that represent the key ETL architecture components required in almost every dimensional data warehouse environment. No wonder the ETL system takes such a large percentage of data warehouse and BI project resources!

The good news is that if you study these 34 subsystems, you’ll recognize almost all of them and will be on the way to leveraging your experience as you build your ETL system. While we understand and accept the industry’s accepted acronym, the “ETL” process really has four major components: Extracting, Cleaning and Conforming, Delivering and Managing. Each of these components and all 34 subsystems contained therein are explained below.

EXTRACTING: GETTING DATA INTO THE DATA WAREHOUSETo no surprise, the initial subsystems of the ETL architecture address the issues of understanding your source data, extracting the data and transferring it to the data warehouse environment where the ETL system can operate on it independent of the operational systems. While the remaining subsystems focus on the transforming, loading and system management within the ETL environment, the initial subsystems interface to the source systems to access the required data. The extract-related ETL subsystems include:

CLEANING AND CONFORMING DATAThese critical steps are where the ETL system adds value to the data. The other activities, extracting and delivering data, are obviously important, but they simply move and load the data. The cleaning and conforming subsystems change data and enhance its value to the organization. In addition, these subsystems should be architected to create metadata used to diagnose source-system problems. Such diagnoses can eventually lead to business process re-engineering initiatives to address the root causes of dirty data and to improve data quality over time.

The ETL data cleaning process is often expected to fix dirty data, yet at the same time the data warehouse is expected to provide an accurate picture of the data as it was captured by the organization’s production systems. It’s essential to strike the proper balance between these conflicting goals. The key is to develop an ETL system capable of correcting, rejecting or loading data as is, and then highlighting, with easy-to-use structures, the modifications, standardizations, rules and assumptions of the underlying cleaning apparatus so the system is self-documenting.

The five major subsystems in the cleaning and conforming step include:

DELIVERING: PREPARE FOR PRESENTATIONThe primary mission of the ETL system is the hand-off of the dimension and fact tables in the delivery step. There is considerable variation in source data structures and cleaning and conforming logic, but the delivery processing techniques are more defined and disciplined. Careful and consistent use of these techniques is critical to building a successful dimensional data warehouse that is reliable, scalable and maintainable.

Many of these subsystems focus on dimension table processing. Dimension tables are the heart of the data warehouse. They provide the context for the fact tables and hence for all the measurements. For many dimensions, the basic load plan is relatively simple: perform basic transformations to the data to build dimension rows to be loaded into the target presentation table.

Preparing fact tables is certainly important as they hold the key measurements of the business that users want to see. Fact tables can be very large and time consuming to load. However, preparing fact tables for presentation is typically more straightforward.

The delivery systems in the ETL architecture consist of:

MANAGING THE ETL ENVIRONMENTA data warehouse will not be a success until it can be relied upon as a dependable source for business decision making. To achieve this goal, the ETL system must constantly work toward fulfilling three criteria:

The ETL management subsystems are the key architectural components that help achieve the goals of reliability, availability and manageability. Operating and maintaining a data warehouse in a professional manner is not much different than other systems operations: follow standard best practices, plan for disaster and practice. Many of you will be very familiar with the following requisite management subsystems:

SUMMING IT UPAs you may now better appreciate, building an ETL system is unusually challenging. The required ETL architecture requires a host of subsystems necessary to meet the demanding requirements placed on the data warehouse. To succeed, carefully consider each of these 34 subsystems. You must understand the breadth of requirements and then place an appropriate and effective architecture in place. ETL is more than just extract, transform and load; it’s a host of complex and important tasks.