5 Challenges of Data Integration (ETL) and How to Fix Them

Decades after it was introduced, ETL (extract, transform, load) remains the predominant method of data integration. Along with modern variants such as ELT and ETLT, the ETL process is used to transfer massive amounts of data every day quickly and efficiently.

The fields of business intelligence and analytics depend on a reliable, consistent ETL pipeline that continually gives users access to fresh, up-to-the-minute information.

ETL is conceptually simple: data is first extracted from one or more sources, transformed to fit its destination, and then loaded into a centralized data repository. In practice, however, ETL can be technically complex and challenging to implement, leading to slowness and inefficiencies.

According to one study, for example, two-thirds of ETL data is more than 5 days old by the time it’s loaded into a database. Without the guarantee that your enterprise data is fresh, updated, and accurate, managers and executives will struggle to identify trends and make the right decisions to steer the course of the business.

To get the most from their ETL pipelines, businesses need to tweak and optimize their workflow on a regular basis. In this article, we’ll discuss some of the common ETL difficulties that users face, as well as the techniques you can use to address them.

5 ETL Challenges

Setting up the connections between point A and point B is only half the battle in ETL. Below are 5 challenges that organizations need to deal with when using ETL:

1. Network latency: Large ETL workloads can transfer many gigabytes or terabytes of data, which means that your network connection needs to be fast. High amounts of network latency may be an unexpected bottleneck, holding you back from performing ETL at maximum speed.
2. Unoptimized code: If you’ve hand-coded parts of the ETL pipeline, your code base may be suffering from inefficiencies and errors behind the scenes. These inefficiencies can be introduced at any stage of the ETL process: reading from source files, transforming data, loading it into the final data warehouse, etc.
3. Not enough resources: If your ETL system is lacking in computing resources such as memory or storage, your workloads can slow down dramatically. Your file systems and caches may also suffer from fragmentation over time, especially on Windows operating systems.
4. Data quality issues: Out-of-date, inaccurate, and duplicate records are just a few data quality issues when performing ETL. Your ETL workflow needs to verify that you’re pulling the newest information possible, and that you aren’t extracting the same information from multiple sources.
5. Long-term maintenance: The ETL needs of almost every organization will evolve over time. This includes changes in data formats and data connections, as well as increased data volume and velocity (i.e. the amount of new data and the speed at which it arrives).

How to Optimize Your ETL Pipeline

Given these potential ETL issues, businesses should constantly be refining and optimizing their ETL pipeline. As a data migration partner with more than a decade of experience, Datavail has come up with a set of ETL optimization techniques that we collectively refer to as the “5S Approach.”

Below is a brief overview of the 5 ways to fine-tune your ETL workflows:

1. SQL: Well-designed SQL queries can be orders of magnitude faster than poorly designed ones. Potential SQL optimizations include removing the use of SELECT * statements and using INNER JOIN instead of WHERE.
2. Space: Large ETL workloads require large amounts of space—not only for storing the data itself, but also for intermediate steps such as indexing, partitioning, and logging. Acquiring enough storage and resolving disk fragmentation issues on a regular basis are essential.
3. Sessions: ETL is highly resource-intensive, which means it can conflict with other sessions and processes. Make sure that other sessions aren’t consuming too many resources or locking up data that you need for your ETL workflow.
4. Statistics: Using database table statistics helps SQL engines make subtle tweaks and quick estimations that add up over time. The more accurate your statistics are, the better your database will perform during the ETL process.
5. Scheduled processes: ETL jobs should ideally be run during less active times (such as the weekends or overnight). Make sure that you schedule ETL processes for off-peak hours, while making sure that the final reports will be on key decision-makers’ desks in time.

Conclusion

With multiple potential pitfalls in the ETL process, working with a skilled ETL partner is a very wise idea. Datavail is a knowledgeable, experienced IT managed services provider, with more than a decade of experience helping clients optimize their ETL workflows and databases.

To fit the needs of our clients, Datavail has a long list of database certifications: we are an Amazon Web Services (AWS) Advanced Tier Consulting Partner, a Microsoft Gold Partner, an Oracle Platinum Partner, and a MongoDB Premier Partner. We can strategically deploy our expertise to work with a wide range of ETL technologies, including Amazon Redshift, Snowflake, Microsoft SQL Server, Oracle Database, MongoDB, MySQL, and more.

If you require assistance with ETL and data integration, Datavail is ready to help. Contact our team today for a chat about your business needs and objectives.

Want to see how Datavail’s ETL optimization works in practice? To learn how one of our clients unlocked the potential of their ETL pipeline, check out our case study “Legal Services Company Wins Big with Enhanced Data Analytics Platform.”