The ETL (extract, transform, load) process is the backbone of enterprise data processing, allowing users to pull information from multiple sources into a single data warehouse. The goal of the ETL process is to have high-quality data quickly and consistently available.
Far too often, however, businesses find that their ETL processes are plagued by problems and inefficiencies, resulting in failed jobs and operational downtime. Even dedicated data integration platforms such as Informatica PowerCenter are susceptible to a variety of performance issues that need attention from expert IT personnel.
In this article, we’ll explore some of the biggest reasons why you might need to fine-tune the performance of your ETL processes.
1. Heavy data loads
Both the volume of, and the demand for, enterprise data has been growing by leaps and bounds in recent years. Unfortunately, many ETL processes are struggling to adequately scale in order to match these heavy loads.
When handling large amounts of data, some common mistakes are:
- Loading the full dataset, rather than the changes between the previous and new data.
- Loading irrelevant and extraneous data.
- Creating bottlenecks due to insufficient CPU or memory resources.
- Processing data serially instead of in parallel.
2. Long-running, inefficient queries
SQL queries that are inefficiently designed may involve far more computation than is actually necessary, running for minutes or even hours before they complete. For example, a query may be scanning the entire dataset when it only needs to be searching through one or two tables.
In general, operations such as INNER joins and UNIONs should be used sparingly and only when necessary, as they can result in performance issues. Simple changes, such as using SELECT fields instead of SELECT *, can go a long way to speeding up your inefficient SQL queries.
3. Poorly coded mappings
Your source and target systems likely aren’t designed with exactly the same data schema. The ETL process therefore relies on source-to-target mappings, which are instructions for how data should be transformed when moving between source and target systems. Some of the actions that you might need to take include setting default values and combining two different fields into one.
However, mappings that are poorly coded are likely to result in errors. Incorrect mappings often occur when the underlying source and target systems change without updating the corresponding mapping. This may cause problems such as missing information and data inserted into the wrong fields.
4. Incorrect design of source and target systems
For the most harmonious ETL processes, your source and target systems should be as easy to align as possible. Unfortunately, many databases play host to some all-too-common design errors that can form the basis of serious inefficiencies and slowdowns.
One frequent error, for example, is the repetition of redundant data in multiple tables. This has the potential to create data integrity issues, since any code that modifies or updates this data will need to do so in several places at once. Another issue is the failure to normalize your database, which can lead to poor performance and inaccurate, out-of-date information.
While there are many things that can go wrong with your ETL processes, there’s one way you can solve them: tuning the performance of Informatica PowerCenter. To learn more, check out Datavail’s white paper, “The Essential Guide to Informatica Health Checks & Performance Tuning.”
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