ETL refers to “Extract, Transform, and Load”. By this point, the concept of ETL is well-established within the data industry and there are a number of enterprise-proven “ETL Tools” available to assist organizations with the movement and transformation of data. An ETL Tool is simply software that’s designed to help organizations move and transform data into a data warehouse.
With the rise in popularity of open-source data scripting languages, such as Python and R, many organizations are opting to write their own scripts for the movement of data over employing an ETL Tool. This is a fine approach when working with small amounts of data or in environments where the entire system is not complex. ETL Tools, however, provide many benefits over open-source scripting languages when it comes to the movement and transformation of data. This blog post covers the benefits and limitations of using an ETL Tool over open-source data scripting languages.
Benefits of using an ETL Tool over open-source data scripting languages
First, ETL Tools are designed to extract, transform, and load data, so they have transformations built into them that make common data calculations, joins, and other transformations very quick and easy. You don’t need to write code to join data between two sources, ETL Tools take care of this for us. ETL Tools also provide built-in data connectors that extract and load data in batch, which eliminates the need to learn multiple new APIs, potentially saving development time.
ETL Tools are designed to build data pipelines and run transformations concurrently. This means that rows of data are being transformed and loaded into my target table before all of my data have even been extracted from the source and it also means that data can flow through two branches in a pipeline concurrently. This can speed up the process of extracting, transforming, and loading data because you don’t need to wait for the data to completely finish loading into your computer’s memory before you run transformations and insert it into the target table within the data warehouse. Beyond this, ETL Tools are built and maintained by teams of engineers working to constantly improve performance and stability and this sometimes includes complex engineering surrounding how the ETL Tool works which holds data in the server’s memory and CPU cache.
Because of the data pipelining capabilities, ETL Tools make it easy to designate paths of failure (i.e., what to do if a transformation fails for just a subset of rows). If an ETL Tool encounters a string in a numeric field, the transformation will only fail for that specific row. All other rows will be loaded into the target table and we can specify a separate path for the row that failed. To help keep track of complicated data flows, ETL Tools automatically provide data flow visuals that would need to be manually created when using open-source data scripting languages. These types of functionalities can add a lot of value to organizations with complicated environments. In complicated environments, the maintenance associated with moving data via scripting languages often becomes overwhelming.
Finally, ETL Tools offer easy scheduling of notifications (for failures, for example) and built-in monitoring of scheduled jobs. While many of the benefits of ETL Tools are also possible in open source scripting languages, developers would need to reinvent the wheel by creating custom code to achieve all of the above benefits, creating custom code requires constant maintenance and can be very time-consuming.
Limitations of using an ETL Tool over open-source data scripting languages
The biggest limitation of ETL Tools is that they are largely interface-driven, which can make them difficult to navigate, hard to debug, and introduces a reproducibility problem. For engineers used to writing their own code, there’s a learning curve associated with ETL Tool interfaces that you may find frustrating. Beyond this, ETL Tools are limited in terms of the types of transformations you can run. Data science-type data transformations, for example, are not easily accomplished within most industry standard ETL tools. Finally, unless you choose an open source ETL Tool (such as Pentaho and Talend), usage of the ETL Tool would incur expenses for your organization.
If you’re working in the cloud and have a preference for a code-based ETL solution that’s a good hybrid of an “ETL Tool” and data scripting languages, make sure to check out Databricks. Databricks offers many of the benefits of traditional ETL tools while remaining code-driven (Python, R, SQL, Scala, and Java are all supported).
If you’re struggling with the the movement and consolidation of data within your organization, feel free to reach out to Datalere! We’d love to chat about how we can help.