Extract, transform, load (ETL) is the process of extracting data from various sources, transforming it through various methods, and loading it into a data warehouse or data lake.
Some background: Each day, companies collect massive amounts of data. And while this data could be a treasure trove of strategic insights, many enterprises struggle to make sense of it all.
According to a recent survey, 76% of organizations are still struggling to understand their data. And while a lack of talent and resistance to change may be driving forces behind the struggle, another factor must be considered: the sheer number of data sources.
Data comes from a wide range of places: an enterprise’s CRM, social media, customer purchases, and marketing campaigns are only a few examples. The data also comes in many forms, such as structured and unstructured.
To use this data to its fullest potential, it must be moved into one centralized location, also called a data warehouse. Yet, to be moved, data must be extracted from its original source, transformed into something valuable, and then loaded into the data warehouse.
This process, known as ETL (extract, transform, load), is critical for enterprises ready to reap the benefits of high-quality data analytics.
The Extract, Transform, Load (ETL) Process
This process of extracting data from various sources, transforming it through various methods, and loading it into a data warehouse is typically carried out through the use of software tools. Let’s dive further into each step of the process.
During the extract phase, data is extracted from all of its various sources. These sources may include everything from software to relational databases to flat files. Once the data is extracted, it is then moved to a single repository separate from the desired data warehouse.
Keeping the data separate from the endpoint is critical for ensuring data quality. Data will need to be transformed before it can be used, which occurs during the next step.
During the transform phase, data is transformed into a specific format. This process can be completed in a number of ways. For example, one method is deduplication, which involves the removal of multiple identical data entries.
Another method is cleaning, where data is scoured to remove inaccurate values and other anomalies. Data can also be sorted based on type, filtered, and manipulated in other ways.
The transformation stage is critical for ensuring that high-quality and accurate data is all that fills the database. Without this step, the data is completely unreliable.
During the load phase, the transformed data is loaded into a data warehouse. The loading process can be completed all at once or over time, depending on an enterprise’s needs and data management capabilities.
Once the transformed data is loaded into a database, it can then be accessed by data analysis tools and other software. Enterprises can then use the data in a number of ways. For example, users can pull historical reports or real-time data to support strategic decisions.
Alternatives to the ETL process do exist. For example, data virtualization is the process of aggregating data across sources without actually moving the data to a new warehouse. Instead, data is accessed via a virtual data layer. Data virtualization eliminates the need to replicate data, which is an expensive and time-consuming process.
Also see: What Is Data Virtualization?
ETL Use Cases
The main use case of ETL is the ability to bring data together to make sense of it all. And many processes require the unification of data to work.
A small-picture use case for ETL is the ability to clean and store data to make it useful for improving the customer experience. Organizations can analyze the clean data for patterns that highlight what their customers are looking for. As a result, they can make strategic decisions about everything from marketing to product development.
A big-picture use case for ETL is IoT, or the Internet of Things. IoT involves using tech such as sensors to gather data from machines, mobile devices, and even humans. ETL enables the data from these sensors to be stored and accessed from one place, opening a whole new realm of opportunity. For example, centralized IoT data can then be used for other processes such as predictive maintenance and analytics.
Also see: What Is Predictive Analytics?
ETL can also simplify data migration to the cloud, improve data governance, and a variety of other data analytics task.
The Benefits of the ETL Process
ETL’s ability to consolidate data, transform it into a usable format, and store it in a centralized location results in many benefits for enterprises.
Simplifies Data Analysis
Put simply, data analysis is the process of gleaning useful insights from data. However, true data analysis requires accurate and updated data for quality results. ETL ensures that data is properly cleansed, consolidated, organized, and stored, simplifying the analysis process.
Many modern ETL tools can also make this process even easier. For example, some platforms feature automation capabilities, performing ETL without human intervention. All that’s left to do is analyze the results.
See also: Best ETL Tools & Vendors
Improves Data Quality
Data quality is a continuous concern for many enterprises. In fact, a recent survey of 500 data professionals found that 77% have data quality issues, and 91% of professionals report that subpar quality is impacting their organization’s performance.
Low-quality data can result in poor decision-making that leads to detrimental consequences for organizations. ETL is a process that can combat quality concerns by ensuring data is trustworthy, accurate, and up-to-date.
Through the transformation stage, raw data is cleaned, deduplicated, organized, reformatted, and corrected, resulting in higher-quality results.
Saves Time and Costs
In the past, to extract, transform, and load data, organizations would need to write their own code. Not only did the process require specialized (and expensive) programming skill sets, but it also required a ton of time and effort. Furthermore, maintaining these manually coded processes called for even more resources.
Now, modern ETL tools exist that can do the heavy lifting. Instead of writing miles of code, organizations can implement tools that complete the ETL process automatically, saving critical resources.
ETL can also help eliminate the detrimental effects of “bad data.” In the past, IBM has estimated that bad data costs US organizations trillions of dollars each year. For example, bad data can lead to poor decision-making, which directly impacts an organization’s bottom line.
ETL prevents these costly effects by removing data silos so everyone within an enterprise can see the whole picture and make educated business decisions. The process also helps cleanse all data to improve its accuracy.
Data is used across the enterprise, from the C-suite to the frontline. As such, all data should be updated and available for anyone who needs it. Improving collaboration is one of ETL’s superpowers.
By extracting all data and loading it into a central location, everyone within an enterprise can access the same data in real time. ETL also ensures data is consistent across the enterprise by eliminating silos. Stakeholders can use the same data to make financial decisions as the marketing team uses to tailor their campaigns.
The Challenges of ETL
Just like any other data-driven process, ETL isn’t perfect. For example, while ETL tools can save time when compared to manual coding, the process of ETL can be slower than other options such as ELT. This is due to the cleansing process occurring before the data is loaded into the warehouse.
Data loss can also occur during the processing of large and complex datasets. This is why ETL is best used for smaller, more specific datasets.
What Is ELT?
ETL is often confused with ELT (extract, load, transform), however, they are two different processes. Through ETL, data is extracted, transformed inside a single repository, and then loaded into the data warehouse.
Through ELT, raw data is extracted and immediately loaded into the data warehouse. Any cleaning, deduplication, or other transformative processes occur inside the data warehouse rather than in another repository.
A key benefit of using ELT instead of ETL is that enterprises have access to the raw data inside their data warehouses. ELT can also be faster since transformation processes occur inside the data warehouse without the need to be moved to and from an additional repository.