ETL (Extract, Transform, Load)
What is ETL? (Extract, Transform, Load)
ETL is a data integration process that moves data from a source to a supported target destination, such as a data warehouse. So, what does ETL stand for? Extract, Transform, Load!
The primary aim of Extract, Transform, Load (ETL) is data analysis, allowing you to generate valuable insights about all the data in your organization. In addition, ETL tools will enable you to transfer to a destination without manually building complex data pipelines.
In this guide you will learn all about ETL and what it stands for, how it works and its benefits. You will also see example use cases for ETL pipelines and some tools that can be used to create ETL pipelines.
How Does ETL Work?
Extract, Transform, and Load involves three stages:
You can use the ETL process to move raw data from its source to a data destination. ETL is a core component of data transformation, allowing you to centralize data in a single location for analysis.
The first stage of ETL is extraction. You can extract data from data sources such as:
- Relational databases
- Time-series databases, like InfluxDB
- Customer relationship management (CRM) systems like Salesforce
- Enterprise resource planning (ERP) systems
- Software-as-a-Service (SaaS) tools
- Social media platforms
Extracted data then moves to a staging area or “landing zone” — a temporary storage location for data processing.
You can extract structured and unstructured data from data sources during the ETL “extraction.” However, unstructured data is better suited to another data integration process called Extract, Load, Transform (ETL).
The next stage of ETL is data transformation. Depending on the data integration project, this step might involve the following:
- Transforming extracted data into the correct format for analysis
- Removing inconsistencies and inaccuracies from data sets for improved data quality
- Fixing corrupted, incorrect, or duplicated data in data sets — a process called data cleansing
- Ensuring transformed data complies with data governance frameworks, such as GDPR and CCPA.
The final stage of Extract, Transform, and Load involves loading data to a supported data destination, such as a:
- Data warehouse
- Data lake
- Relational database
There are two ways to load data into a target system:
- Full loading involves loading all data from a data source into a target system
- Incremental loading involves only loading new and updated data from a data source into a target system
You can then push data through a business intelligence (BI) tool like Looker, Tableau, or Microsoft BI. That lets you generate insights about your business for better decision-making and problem-solving.
Real world ETL use cases
In this section we will look at some practical examples of how ETL is being used in the real world so you can get a more concrete understanding of how it can be used.
ETL is commonly used to move data from several different sources and then modify it before placing the data for long term storage and analysis in a data warehouse. Data warehouses are designed for holding analytics data and are optimized for OLAP style queries. They generally are built on some type of columnar storage engine for improved data compression and query performance.
Customer data integration
Another use case is for integrating customer data from things like CRM systems, social media, and web analytics, to create a comprehensive view of the customer and how they are interacting with your business.
Supply chain management
Supply chain management is another area where ETL pipelines are useful. Data from a number of systems can be collected and used for things like inventory management, and logistics. This makes businesses more efficient and improves decision making.
ETL pipelines can be used for fraud detection by taking data like transaction logs or customer profiles to detect patterns or anomalies that may indicate fraudulent activity.
Benefits of ETL
Single source of truth
Using ETL to move data to a centralized location like a data warehouse or lake provides a single source of truth for all the data in your organization. You can view data insights in one system rather than using several different tools, saving time and resources.
Enhanced data quality
You can enhance data quality by performing data cleansing during ETL. Clean data can lead to more accurate data analysis.
The transformation stage of ETL removes incomplete, incorrect, or duplicate data sets from your workflows, helping cleanse data and improve data integration outcomes. You can also flag missing values and ensure data conforms to specific rules.
Running data through business intelligence (BI) tools after ETL helps identify patterns and trends in data sets for better decision-making. That can mean improved productivity in your organization. For example, you can learn which team members perform the best and use this information to improve day-to-day operations.
Remove data silos
Sometimes, data exists in silos — data repositories that don’t communicate because a department or group controls them. That can make it challenging to perform data analysis. ETL removes silos by consolidating data sets from disparate and isolated sources and moving data to a centralized location.
Predict future outcomes
The latest BI tools can forecast future events in your organization. For example, you can learn whether you’ll experience a sales slump based on historical data in a data warehouse. By identifying this information, you can reduce risk to your business or capitalize on opportunities.
Visualize data flows
ETL tools feature a graphical user interface that lets you visualize each stage of the ETL process, from extraction to loading. That provides a greater understanding of the logic behind data flows, improving data integration in your organization.
ETL best practices
Design for flexibility and reuse
ETL pipelines should be designed to be flexible and adaptable to changing requirements. This is especially important when dealing with large volumes of data. The design should also allow for the reuse of components and code to save development resources.
Error handling and data validation
It is important to handle errors and exceptions gracefully, and to implement robust data validation checks to ensure that the data being loaded into the destination system is accurate and complete. This can be done by adding custom validation rules or by leveraging existing data sources such as a master data management (MDM) system.
Monitor and optimize performance
Regularly monitor the performance of ETL pipelines and optimize them as needed to ensure that they run efficiently and within acceptable time limits.
Secure data and protect privacy
Make sure to properly secure data during the ETL process and ensure that any personal or sensitive information is handled in accordance with relevant privacy laws and regulations.
Document and test the ETL process
Document the ETL process, including any transformations and mappings, and test the pipeline thoroughly to ensure that it is functioning correctly. Documentation will help with debugging and also make it easier to onboard new team members.
Common challenges with ETL
Data is useless if the quality can’t be somewhat guaranteed. Making sure data coming from a number of different sources is consistent and standardized is a common issue with setting up ETL processes. This is more of a organizational problem than a purely technical problem
The transformation stage of the ETL process can be complex, especially if the data needs to be cleaned, transformed into a different structure, or combined with other data. Complex transformations will impact performance and the amount of resources required to run your ETL pipeline.
Performance and scalability
Scaling your ETL pipeline can be a challenge depending on your architecture and how much data you need to be able to handle. Some workloads can be “bursty” and require the ability to rapidly increase the amount of hardware available to handle surges in data throughput.
ETL pipelines will need to integrate with a variety of other systems and technologies, which can add complexity and require specialized knowledge or expertise. Many popular ETL tools will include integrations for common data sources but may not be able to connect with more specialized or proprietary systems so you will need to account for this.
Data security and privacy
Ensuring the security of data during the ETL process and protecting the privacy of individuals whose data is being used can be challenging and requires careful planning and attention to detail. You can keep your data secure by carefully monitoring access to data sources and implementing encryption or other security measures during the ETL process.
Maintaining the ETL pipeline
Your ETL process may need to be updated or modified over time due to changing data sources or user requirements. This can be a significant ongoing task, which requires someone with expertise in the ETL process and technologies involved. It is important to have resources available for maintenance and support of the ETL pipeline. It is also important to have a clear understanding of how data flows through the system, so that any changes or updates can be made quickly and accurately without downtime or hurting performance.
ETL is a complicated process that involves lots of coding and data engineering. You’ll need to create data pipelines from scratch to ensure the smooth flow of data from its source to a target system. This process can take weeks or even months, depending on the complexity of your data integration project.
ETL tools automate the ETL process by moving data from its source to a data destination without lots of code and manual work. In addition, these tools often provide pre-built data connectors that seamlessly transfer data to a target system in a few minutes.
ETL tools can be used to automate the following processes:
- Transforming data into the correct format for analytics
- Data cleansing
- Improving data quality
- Data governance
- Loading data to a target system
Some of the most popular ETL tools include:
Apache NiFi is an open-source ETL tool that is designed for data flow and orchestration. It provides a visual interface for building and configuring ETL pipelines and includes features such as data provenance, data governance, and security.
InfluxDB has built in support for doing ETL type workloads without needing a separate tool by using Tasks. Tasks will run on data as it is written into an InfluxDB bucket and can then move the transformed data into a new bucket. Tasks are built on top of the open source Kapacitor project.
Apache Kafka is an open source distributed streaming platform that can be used to build ETL pipelines. It is designed for high-throughput data processing and can be used to extract data from multiple sources, transform it, and load it into a destination system.
DBT is a powerful open source software tool that enables data engineers to build and maintain robust data pipelines. It works well for ETL pipelines because it provides a number of features to effortlessly move data from source systems, manipulate it, and finally store it.
DBT also has built-in secure access control and audit logging capabilities so that companies can be sure their data is safe in transit. DBT’s built-in transformation features allow developers to quickly set up advanced mappings between fields in different databases and execute complex queries with ease.
Airbyte is a powerful open source ETL tool that helps simplify the process of handling data between different applications. It enables users to move, normalize and even sync data across hundreds of services with just a few clicks, saving them from spending hours on manual ETL pipelines.
Airbyte can be used for various purposes including extracting data, transforming it according to business needs, loading into staging storage and warehousing it for analytics. Airbyte’s architecture allows for an optimal level of performance to ensure fast and stable data management processing and integration.
Telegraf is a server agent with over 300 different input and output plugins and has many different plugins to allow transforming data points before sending them to storage. While not explicitly designed for ETL workloads, Telegraf can be used effectively depending on the use case.
What is an ETL pipeline?
An ETL pipeline is an automated process for extracting data from various sources, transforming it into a usable format, and loading it into a target system. It is used to move data from disparate systems and enable effective decision-making.
Why is ETL important?
ETL is important because it enables organizations to extract the data from various sources, transform it into a usable form and load it into a target system for analysis. This allows them to gain insights from their data that can be used to inform business decisions.
What is ETL pipeline testing?
ETL testing is the process of verifying that an ETL pipeline is functioning correctly and producing the expected results. There are several strategies that can be used to test ETL pipelines:
- Unit testing: This involves testing individual components or modules of the ETL pipeline to ensure that they are working correctly.
- Integration testing: This involves testing the integration between different components or modules of the ETL pipeline to ensure that they are working together correctly.
- End-to-end testing: This involves testing the entire ETL pipeline from start to finish to ensure that it is working as expected and producing the desired output.
- Performance testing: This involves testing the performance of the ETL pipeline to ensure that it can handle the required load and meets performance goals.
Ideally these tests will be implemented using an automated testing framework so that each time new code is deployed the tests are run to verify the pipeline still works before pushing the code to production.
ETL vs. ELT: What’s the Difference?
ELT is another three-step data transformation process similar to ETL. However, this method reverses the “transformation” and “loading” stages.
- Extracting raw data from a data source such as a relational database, time series database, or CRM system
- Loading that data into a target destination, such as a data warehouse or lake
- Transforming the data into the correct format for analytics from within the target system
The main difference between ETL and ELT is that the latter lets you push large data sets through a data pipeline and immediately access that information in a target system. As a result, you don’t have to spend time transforming that data before it enters data warehouses or lakes. ETL transforms data on a processing server, while ELT carries out the transformation process within a target system after the loading stage.