In simple words, an ETL pipeline is what helps you get data from one place (a database, app, or something else) or multiple places, modify it on the way, and then put them in some other place.
Now let’s put it in more official terms. ETL pipeline is a set of processes that transfer data from one or more sources to a destination, which is often a data warehouse or a database. The processes are ETL processes, which means they have three separate stages: Extraction, Transformation, and Loading.
ETL pipelines are vital for data management. They are widely used for multiple different purposes, including data archiving, data analysis, backup, data reporting, and many other business activities.
Understanding ETL Process
Let’s discuss the ETL process in more details. ETL stands for Extract Transform Load – the three stages of the process.
Extract: The First Step of ETL
At the Extract stage, the data are retrieved from the source. The source can be a database, a cloud or desktop app, a file, etc.
Transform: The Heart of ETL
At this stage, we modify the obtained data. This stage is also what differentiates ETL pipelines and other data pipelines. There is a number of reasons to transform data. For example, you have data about customers, but phones are all written in different formats – 12345678012, +38-(056)-111-11-11, 7-11-22-333-44-55, etc.; however, you want to have them all in one format. But the most common reason for data transformation is when data in the source are stored in one format and structure, but in the target they are stored differently.
Load: The Final Step of ETL
Finally, the ETL process loads the transformed data to a target destination. Usually, a destination is a data warehouse or a database for centralized data storage; however, in some cases it can be any other destination.
Benefits of ETL Pipelines
The wide use of ETL pipelines in data management signifies that it’s quite a pretty valuable tool with a number of benefits.
- ETL pipelines are a good solution for collecting data from different sources. They allow extracting data from different sources, transforming it into a unified format, and loading it to a destination of your choice.
- ETL pipelines can reduce time for data analysis, because you can prepare data for analysis with transformations. This ensures deeper analytics and business intelligence.
- Finally, ETL pipelines are almost always designed with data quality assurance in mind. With ETL data pipelines, you can filter out low-quality data during extraction, and improve data quality with necessary data transformations. Besides, data pipelines can be used for enriching data in one system with data from another system.
Potential Drawbacks of ETL Pipelines
ETL is a well-established approach for building data pipelines; however, it has a few drawbacks that need consideration:
- It loads data periodically in batches. In most cases, this is OK, but it means that the most recent changes in source data may not be reflected in the destination. If you need real-time data updates, consider other approaches. Recently there is the rise of real-time ETL tools, so you may consider them.
- ETL pipelines also require a bit more effort to configure them than some alternate approaches, like ELT.
Real-world Use Cases of ETL Pipelines
ETL pipelines are a popular way of loading data, and many businesses from different areas use them.
- For example, ETL pipelines can be used in online review analysis for processing a large volumes of customer reviews and comments to analyze customer satisfaction.
- Another good case for ETL pipelines is healthcare data analysis. ETL pipelines are used for getting both fresh and legacy data from multiple healthcare facilities and loading them to a data warehouse for further analysis in order to improve healthcare services provided to patients.
- In retail & e-commerce analytics, ETL pipelines are also used to process large amounts of data, prepare them for analysis with transformations, and load into a data warehouse. This helps businesses to gain a better understanding of customer behavior, purchase patterns and trends, optimize inventory management, etc.
- In manufacturing, ETL pipelines are used to get data from production, quality control, maintenance, and other systems. Then, the data are transformed and loaded to data warehouse, and after this, used for product lifecycle analysis, predictive maintenance, quality assurance, and production planning.
- One of the most wide-spread use case for ETL pipelines is digital marketing. Marketing specialists need to analyze data from their marketing tools, social media, websites, customer databases, etc., so they use ETL pipelines to load these data to data warehouses. Additionally, marketing specialists may use ETL pipelines for data migration between different marketing tools as well as for importing leads from various sources.
- Similar challenges are solved by ETL in social media & video platform analytics and other areas, so ETL pipelines are widely used in these and other industries too.
ETL Pipeline vs. Data Pipeline: A Comparative Analysis
The key point in understanding data pipelines is that ETL pipelines are data pipelines; however, not all data pipelines. So what characterizes ETL data pipelines? ETL pipelines consist of well-defined steps – extraction, transformation, and loading. Other data pipelines can have different steps and step order. Some data pipelines, like ELT, do not include transformation. Others may not end with the loading step, but have more steps after it.
Another important difference is that ETL pipelines load data in batches every so often. There are real-time data pipelines that load each new or modified record immediately.
So let’s sum up the differences:
Key Differences Between ETL and Data Pipelines
|Other Data Pipelines
|Always have three steps: Extract, Transform, and Load
|May have different steps and different step order
|Transformation is the core step
|Some data pipelines don’t include transformation
|Load data once or in batches periodically
|May load data in batches or in real-time, for every new record
Top ETL Pipeline Tools in 2023
Here are several popular ETL tools that you can consider:
Skyvia is a powerful data platform for solving different data-related tasks. It has several tools for different use cases, including ETL and ELT tools, and supports 150+ different data sources. All the tools are visual and require no coding.
Skyvia pricing depends of the features that you can use and number of records loaded per month, and the latter can be adjusted. If you surpass the pricing plan limit, you can load additional records for additional cost. Skyvia has a free plan; however, the ETL features available in it are limited, and it includes only the most basic ETL tools.
2. Hevo Data
Hevo Data is a zero maintenance platform for quick creating data pipelines and syncing of data from different sources to a data warehouse. Hevo supports both configuring transformations visually and coding them in Python. It supports 150+ different connectors.
Hevo Data pricing depends on the available support options and the volume of data updates. Initial data can be loaded for free.
Integrate.io is a no-code data pipeline platform with support for ETL, ELT, and other approaches. It has a visual diagram designer for data pipelines that allow you to build them via drag-n-drop. Note that Integrate.io pricing plans start from $15000, and this is for the most limited plan with two connectors and daily frequency.
Talend has several tools for creating ETL pipelines, including a free and open-source Talend Data Studio. Talend’s commercial solution, Talend Data Fabric, includes multiple Talend’s tools: Data Studio, Big Data, Management Console, Stitch, API Services, Pipeline Designer, Data Preparation, Data Inventory, and Data Stewardship.
Talend tools include support for over 1000 connectors and include Talend Component Kit for creating custom connectors.
5. Apache Spark
Apache Spark is an open-source data transformation engine for data analysis, batch streaming data, etc. that can run on clusters of machines. It supports different databases and file formats via JDBC, and has a lot of third-party connectors. However, Apache Spark requires coding knowledge and offers no dedicated technical support.
Matillion allows you to connect to different data sources and load their data into a data warehouse while applying different data transformations. It offers both a simple GUI for configuring ETL pipelines and lets you write Python code for advanced cases.
Matillion supports over 150 connectors and has a (rather limited) free tier.
7. Pentaho Data Integration
Pentaho Data Integration (PDI) is an ETL solution that allows checking, fixing, and improving your data quality. PDI also provides both visual configuration and scripting languages support and has templates for the most common tasks.
Pentaho has a free community version and offers 30 day trial period for the paid version.
Step-by-Step Guide to Building an ETL Pipeline
While building an ETL pipeline, you want to get your data from sources to the destination fast, securely, and reliably. Here are some common steps and tips on how to achieve this.
1. Analyzing Requirements
The first and foremost step is to analyze all your requirements for the ETL process. You need to know which data exactly from which sources you need to have in your destination. You should determine what structure and format the data must have in the destination too, which volumes of data will be loaded, whether and how often you need to load the new data, etc.
You also need to plan your budget for the operation, and how you want to do it – yourself or use consulting services.
2. Choosing the Right ETL Tool
After the requirements are clarified, you can proceed to selecting an ETL tool to do the job. Make sure that you select a tool that matches the requirements you have collected. Make sure that it supports all the required sources and destinations and that it has all the required transformation features to convert source data to the format and structure, required in the destination. The tool must support error logging and email notifications.
Finally, make sure that the selected tool can handle the data volumes you need to load, the necessary frequency of data updates, and, above all, fit this all into the limits of your budget for loading data.
You also need to consider if the tool requires coding, and how hard it is to configure ETL pipelines with the tool. It’s better to find out in advance what support options are offered and whether there are additional costs for support.
You can choose between on-premises and cloud ETL tools. The first lets you keep everything under control, as data are loaded via your own infrastructure. The latter, however, make things easier, requiring no infrastructure, installation, and maintenance.
3. Setting Up Your ETL Pipeline
Finally, create your ETL pipeline. Depending on how easy is the chosen ETL tool you may do it yourself or require an IT specialist to do it for you. You also may use services of a consulting company. Additionally, some ETL solutions offer building ETL pipelines for you for an additional fee.
4. Testing and Optimizing Your ETL Pipeline
It’s better to perform the first run with a small amount of test sandbox data and make sure that everything is working correctly. Besides, most ETL tools offer free trials, so you can test whether the tool suites to your needs within the trial period.
Check that data is loaded to the destination as necessary and transformation are performed correctly, and estimate the tool performance. If everything works correctly, you may finally run your pipeline with the production data.
ETL pipelines are widely used for loading data in various industries, and are crucial for modern data-oriented businesses. There is a large number of ETL tools on the market, and it’s important to select a suitable one for your needs. We hope that this article helps you understand what ETL pipelines are, where to use them, and how to select an ETL solution for your needs.