What is ETL?
In the world of data engineering, ETL stands for extract, transform, and load. It refers to extracting data from a source, transforming it in some way, and then loading it into another system.
ETL is a common task in data workflows. For example, a company might want to take data about the usage of its SaaS application from several databases, join it, and move it into Tableau to create visualizations for reporting. This process can be broken down into the three steps of ETL:
- Extract: Extracting the data from the different databases
- Transform: Joining the data together and performing any cleaning and data type conversion that’s required
- Load: Loading the data to generate the visualization
What is an ETL pipeline?
An ETL pipeline is a set of automated processes designed to extract data from a particular source, transform that data, and load the transformed data into a different system or repository.
While it is certainly possible to work through the steps of an ETL process manually, it is often more practical to create an ETL pipeline, particularly in the following situations:
- The task is likely to recur (for example, if you must produce new visualizations each week from that week’s data, or move new customer data each month from a CRM into a data lake for long-term storage). Many batch processing tasks fall into this category.
- The task is complex and doing all the data processing manually is likely to take longer and/or be more prone to errors than simply building a pipeline to process it.
ETL pipelines are important for everything from application functionality to business intelligence, as they facilitate and automate the movement of mission-critical data from a variety of source systems to wherever the data needs to end up, making any necessary data transformations along the way.
Building an ETL pipeline can be as simple as writing a quick script, or it can be a complex engineering undertaking. DataOps and ETL tools such as Shipyard exist to make building ETL pipelines faster and easier. Many have pre-built connectors for popular services to make the data extraction and loading process smoother.
The difference between ETL pipelines and data pipelines
The terms “ETL pipeline” and “data pipeline” are sometimes used interchangeably, but they’re not exactly the same thing. A data pipeline is any system or process that’s used to move data from one place to another. An ETL pipeline is a specific type of data pipeline in which data is extracted from a source or sources, transformed, and then loaded into a target system or systems.
Not all data pipelines are ETL data pipelines. Some data pipelines, for example, may simply move data from one database to another without transforming it, or it may be transformed after being loaded into the target system (this is called an ELT pipeline).
Benefits of ETL pipelines
Almost every company has to perform both regular and one-time ETL data tasks. The benefits of implementing ETL pipelines for these tasks typically include the following:
- Saving your team’s time. ETL pipelines allow your team to spend more time on the tasks that matter, because they don’t have to waste time manually moving and transforming the data they need. Having the ability to automate the integration of data from sources as varied as data streaming from APIs, static data in CSVs, on-demand data from databases, and cloud data in AWS, GCP, or Azure services (among many other things) can reduce a lot of headaches.
- Centralizing your data. One common use of ETL pipelines is to extract data from a variety of tools and sources, transform it into a standardized format, and then centralize it, creating a one-stop source of truth. This kind of data integration can enable teams across your company, from developers to marketers, to work more efficiently.
- Unlocking new kinds of analysis. Having data from various sources transformed and centralized can unlock new kinds of analysis that would be difficult to perform if the data remained siloed in different platforms.
- Automated data migration. When migrating data from one system to another, ETL pipelines can be used to execute the migration of huge amounts of data with little or no manual work required beyond the initial pipeline setup.
Of course, depending on the specifics of your data needs and your tech stack, ETL pipelines may have additional benefits too.
In addition, ETL pipelines have relatively few downsides. Typically, the biggest challenge involved in the use of ETL pipelines is building and testing them. However, that challenge can be mitigated by employing a smart DataOps approach and the right data orchestration tool.
A simple ETL pipeline example
Let’s take a quick look at how a simple ETL pipeline might work. Imagine an SaaS company that’s using PostgreSQL for its transactional database and Snowflake as its data warehouse. Given the task of using data from both these locations to build a weekly performance report in Tableau, the ETL pipeline would do the following:
- Extract the relevant data from PostgreSQL and Snowflake. Since a new report is required weekly, the ETL pipeline would be configured to perform this step (and each subsequent step) once per week.
- Transform the data. At a minimum, this would involve joining the data from the two sources into a new table to be loaded into Tableau, but it also could involve additional data cleaning and processing. For example, if some of the data coming from Snowflake is unstructured data, it will have to be transformed prior to being joined into an SQL table with the Postgres data.
- Load the data into Tableau, which will then generate the report. Some ETL pipelines may include functionality to trigger the execution of features in the target service (in this case, Tableau) each time they load data into it.
That’s a very simple example, of course, but we can already see how building a custom ETL pipeline from scratch might get tricky when more complex data transformations are required. In practice, more complex ETL pipelines involving a variety of data sources, formats, transformations, and destinations are common.
For example, one common use of ETL pipelines for SaaS companies is to pull data from various sources (marketing and sales tools and databases, product usage data streams, etc.), merge it all, and insert it into a data warehouse for long-term storage and analytics processing. This entails pulling data from a variety of different databases and tools.
Some of the data may be structured, other data may be unstructured and custom code will be required to ensure that all of the data conforms to the schema and structure of our data warehouse.
Some of the data is also likely to come from real-time streaming sources, while other data will be best processed in batches. With such a wide variety of sources and data types, building a bespoke system that can extract, transform and load all of the data that you need can be challenging—particularly since you also need to make sure that system doesn’t negatively impact the performance of your application.
For example, the ETL processes pulling product usage data must be carefully architected to avoid impacting the performance of any databases that the application uses—you don’t want a user’s read request to take longer because your ETL pipeline is typing up an application database with reads as it extracts.
And of course, the complexity can increase further if support for real-time data or the transformation of unstructured data sets is required.
Even when your team has the skills for it, scripting complicated custom ETL data pipelines from scratch can be a time-sink. Data orchestration tools like Shipyard make building, launching, and monitoring custom ETL pipelines easy, thanks to integrations with dozens of popular services and tools—in addition to a whole host of other features.
Find out if Shipyard is can help you build the ETL pipeline you need. Get started free - no credit card required.