What is ETL? A Comprehensive Guide

Data management is full of acronyms, and ETL is one of the most common terms you’ll come across. ETL stands for Extract, Transform, and Load. This process is leveraged in data integration and management. ELT is used to extract data from multiple sources, transform it into a suitable format, and load it into a data repository or warehouse where it is analyzed for business intelligence. 

Let’s take a closer look at each step in the process. 

• Extract: In this first step, data is gathered from different sources such as databases, APIs, and flat files. The extraction may be done by using various tools for database queries. The purpose of the extract phase is to compile the raw data for the next step: transform. 
• Transform: Next, the raw data is transformed by cleaning, filtering, aggregating, and converting it into the chosen format for analysis. This stage ensures the data can be analyzed later. In this stage, the data is sometimes enhanced to improve its overall quality. 
• Load: Finally, the transformed data is loaded into a system such as a repository, lake, or warehouse. The data should now be in a suitable format for business intelligence (BI) operations. 

Why does ETL matter in data management?

ETL is important in data management because it allows data from multiple sources to be sourced, enhanced, and analyzed to help drive decisions. Centralizing data in a single location allows for greater visibility and a centralized view. Those making decisions informed by this data no longer need to manage multiple sources, screens, and platforms to achieve a comprehensive view. 

This comprehensive view is crucial for analyzing and reporting on this data as it is structured for querying. Furthermore, the data is of higher quality due to the transformation stage. By cleaning and standardizing data, analysts can be assured they are working with the most up-to-date, reliable information. 

Benefits of using ETL in business processes

Why use ETL in business processes? There are multiple benefits for any organization that works with data across multiple sources and formats:

• Consistency and quality: Through ETL, organizations can make disparate data in multiple formats consistent and high-quality, allowing for better data-informed decisions. 
• Scalability: There is no need to rely on tedious, manual efforts. Organizations can use ETL and its related tools to extract, transform, and load significant quantities of data. 
• Operational efficiency: By automating the steps of ELT, organizations can alleviate burdens on staff and increase operational efficiency to save resources. 

Evolution and history of ETL

ETL was first created in the 1970s to help integrate data from disparate repositories into a centralized warehouse. The original processes were largely manual, relying on custom coding to extract, transform, and load the data. ETL became the standard process for extracting data from multiple databases and transforming it before later loading it into the target destination. Once data warehouses came on the scene in the late 1980s, organizations began to leverage automated ETL processes and dedicated tools. 

Significant advancements and changes in ETL

ETL has continued to evolve over the last few decades, seeing some of the most significant changes in the 2000s. Big data and cloud computing allowed ETL phases to manage greater volumes of data and work with different technologies. 

• Cloud-based: With cloud computing, organizations were able to move to cloud-based ETL solutions. These tools reduced burden on infrastructure and offered greater scalability, integrating with cloud storage and computing resources. 
• Real-time ETL: Real-time ETL solutions continuously integrate data in real time, allowing for the most accurate information possible. 
• Big data integration: ETL tools began to integrate with big data technologies so organizations could manage large-scale datasets with ease. 

The impact of technology on the evolution of ETL

ETL has evolved alongside some of the biggest recent technological advancements, such as automation. ETL processes had several manual elements in the early days but now rely on automation features. Similarly, as cloud computing has grown in popularity, ETL solutions have become able to manage large volumes of data without requiring on-prem infrastructure. ETL tools can also be integrated with advanced analytics platforms for more detailed analysis. 

ETL vs ELT

ETL and ELT are two common methods for data integration. While ETL stands for “extract, transform, and load,” ELT means “extract, load, and transform.” ETL transforms data into a suitable format before loading it into the final destination. However, with ELT, data is loaded into the desired storage system and then transformed if needed. 

At a high level, ETL is best for scenarios where it is known that data transformation must occur. ELT, on the other hand, is often used in cases where the transformation of data can be performed on demand. The following are advantages and disadvantages of the two data integration methods:

ETL advantages:

• Prioritizes data quality before the loading phase
• Works with on-prem systems
• Delivers a structured, cleaned dataset

ETL disadvantages:

• Preprocessing stage can slow operation down
• Heavy use of computational resources for transformation
• Not as useful for changing data requirements

ELT advantages:

• Highly scalable thanks to cloud-based storage and computing
• Delivers real-time data processing
• Reduces preprocessing time

ELT disadvantages:

• Potential cost of raw data storage
• Requires cloud infrastructure
• Transformation processes may be complex

When to Choose ETL or ELT 

Knowing whether to use ETL or ELT depends on the goals of your data integration process. ELT is often used in the case of legacy systems and stringent data quality standards. ELT works well for projects that require large volumes of data and real-time data processing capabilities.

How ETL Works

1. During the extraction phase, the identified subset of data is pulled from the source, such as a database, flat file, or cloud platform. The data is only gathered during this step. 
2. The transform phase converts the structure or format of the data to align with the target data repository. This phase may have several substeps, such as cleaning, integration, aggregation, and enrichment. 
3. During the loading phase, the data is loaded into its final destination, such as a warehouse. It can then be analyzed by business intelligence (BI) tools. 

Key Components and Tools Used in ETL Workflows

ETL workflows rely primarily on:

• ETL tools: These solutions automate the steps of the ETL process, from extracting the data to loading it in its target location. There is no need for data engineers to move or format the data. Many of these tools are also encrypted and certified compliant with relevant regulations. 
• Data sources: In other words, databases, APIs, flat files, and the like.
• Data warehouse or lake: This is the final destination where the data is loaded and stored to be analyzed. 
• Transformation engine: These tools can be used to clean, enrich, and aggregate the data during the transformation phase. 

Benefits of ETL

The data integration method delivers these benefits:

• Clean, high-quality, standardized data
• Automation of processes
• Reduced manual work and chances for human error
• Ability to apply complex rules and requirements
• Consolidated view of data

Challenges in ETL

As with any data integration method, ETL implementations can present obstacles including:

• Scalability: ETL is best for small batch operations as it can be time-consuming. The resource-heavy operations aren’t always best suited for large-volume datasets.
• Latency in processing: Complex data transformation can lead to long processing times. 

• Quality issues: Taking data from multiple sources may lead to quality issues such as inconsistencies, incomplete information, and errors. 

Strategies to overcome ETL challenges

Organizations can overcome the issue of scalability by using cloud-based ETL tools. These solutions are engineered to manage large datasets. As for latency in preprocessing, it may be useful to lean on tools with pre-built transformation features. Finally, in regard to quality, it is critical to have quality checks and validation processes in place before moving to the loading phase.

It’s also wise to follow ETL best practices, such as:

• Automation of workflows through ETL tools
• Documentation of processes
• Continuous monitoring and optimization of ETL performance
• Adherence to data governance practices

ETL in data integration

Outside of ELT and ETL methods, there is also data virtualization and data federation. Data virtualization delivers a unified view of data to users without moving the data. Users can query the data across multiple sources, which can be useful for scenarios that do not require large-scale data integration. Data federation uses a virtual database to integrate data from multiple sources for real-time data access.

Integration of ETL with Data Warehouses and Data Lakes

• Data warehouses: ETL can load data into data warehouses. The data is then structured and optimized for querying and analysis. 

• Data lakes: ETL can load data into data lakes to store large amounts of raw data. 

The Role of ETL in data governance and data quality

ETL fits into data governance and quality by driving consistency. ETL phases enforce standards on the front end before the data is loaded into its final destination. Users can also track data to see its lineage for auditing purposes. ETL phases may also be pre-designed to meet compliance requirements. 

ETL use cases

ETL may be used for the following:

• Data quality: Cleanse, enrich, and standardize data prior to use
• Data migration: Migrate data from multiple sources into a single repository with a unified view
• Data warehousing: Store data in one location for greater efficiency and reduced infrastructure
• Business intelligence: Analyze and report on data to drive business decisions

The 4 major types of ETL tools

1. Cloud-native ETL tools: These tools operate in cloud environments and are often selected for their scalability and flexibility. Examples include AWS Glue, Google Cloud Dataflow, and Azure Data Factory.
2. Open-source ETL tools: Open-source ETL tools such as Apache NiFi, Talend Open Studio, and Pentaho Data Integration are free to use and good for customization purposes. 
3. Batch processing ETL tools: As the name suggests, batch processing ETL tools process data in batches on a scheduled basis. Examples include Apache Hadoop and IBM InfoSphere DataStage.
4. Real-time ETL tools: Real-time ETL tools handle data integration and processing in real-time or near real-time. Examples include Apache Kafka, StreamSets, and Confluent.

When looking for an ETL tool, consider the following:

• Scalability: Will the tool work for your organization as it and the data it uses grows? 
• Ease of use: Is the interface user-friendly and simple to integrate with your core systems?
• Efficiency: How is the tool’s data processing speed?
• Data sources: Does the tool integrate with the data sources you have?

• Cost: What is the total cost when factoring in licensing, maintenance, etc?

At Domo, we make data transformation possible for everyone. With our drag-and-drop ETL tool, you can extract data from multiple sources, transform it, and load it into Domo. No need to code in SQL. Learn more about how it works.