The Extract, Transformation, and Load (ETL) system is the most time-consuming and expensive part of building a data warehouse and delivering business intelligence to your user community. A decade ago the majority of ETL systems were hand crafted, but the market for ETL software has steadily grown and the majority of practitioners now use ETL tools in place of hand-coded systems.

Does it make sense to hand-code an ETL system in 2008, or is an ETL tool a better choice? Kimball Group now generally recommends using an ETL tool, but a custom-built approach can still makes sense. This article summarizes the advantages and disadvantages of ETL tools and offers advice on making the choice that's right for you.

ADVANTAGES OF ETL TOOLS

Visual flow and self-documentation.The single greatest advantage of an ETL tool is that it provides a visual flow of the system's logic. Each ETL tool presents these flows differently, but even the least-appealing of these user interfaces compare favorably to custom systems consisting of stored procedures, SQL and operating system scripts, and a handful of other technologies. Ironically, some ETL tools have no practical way to print the otherwise-attractive self documentation.

Structured system design. ETL tools are designed for the specific problem of populating a data warehouse. Although they are only tools, they do provide a metadata-driven structure to the development team. This is particularly valuable for teams building their first ETL system.

Operational resilience. Many of the home-grown ETL systems I've evaluated are fragile: they have too many operational problems. ETL tools provide functionality and practices for operating and monitoring the ETL system in production. You can certainly design and build a well instrumented hand-coded ETL application, and ETL tool operational features have yet to mature. Nonetheless, it's easier for a data warehouse / business intelligence team to build on the management features of an ETL tool to build a resilient ETL system.

Data-lineage and data-dependency functionality. We would like to be able to right-click on a number in a report and see exactly how it was calculated, where the data was stored in the data warehouse, how it was transformed, when the data was most recently refreshed, and what source system or systems underlay the numbers. Dependency is the flip side of lineage: we'd like to look at a table or column in the source system and know which ETL modules, data warehouse tables, OLAP cubes, and user reports might be affected by a structural change. In the absence of ETL standards that hand-coded systems could conform to, we must rely on ETL tool vendors to supply this functionality — though, unfortunately, few have done so to date.

Advanced data cleansing functionality. Most ETL systems are structurally complex, with many sources and targets. At the same time, requirements for transformation are often fairly simple, consisting primarily of lookups and substitutions. If you have a complex transformation requirement, for example if you need to de-duplicate your customer list, you should use a specialized tool. Most ETL tools either offer advanced cleansing and de-duplication modules (usually for a substantial additional price) or they integrate smoothly with other specialized tools. At the very least, ETL tools provide a richer set of cleansing functions than are available in SQL.

Performance. You might be surprised that performance is listed last under the advantages of the ETL tools. It's possible to build a high-performance ETL system whether you use a tool or not. It's also possible to build an absolute dog of an ETL system whether you use a tool or not. I've never been able to test whether an excellent hand-coded ETL system outperforms an excellent tool-based ETL system; I believe the answer is that it's situational. But the structure imposed by an ETL tool makes it easier for an inexperienced ETL developer to build a quality system.