Extract, transform, and load (ETL) is the process of integrating data from multiple, typically disparate, sources and bringing them together into one central location. It is a key component to businesses successfully making use of data in a data warehouse.
The ETL process itself is fairly straightforward, and when done right, it prepares an organization for powerful business intelligence initiatives. However, a successful ETL process requires a few key steps. Let’s discuss the three steps involved and why data management practices are an essential foundation to carrying ETL out properly.
What happens during an ETL process?
ETL stands for Extract, Transform, and Load, and helps bring data from several sources together in one location. In doing so, the process makes the data easier to work with and analyze, while ensuring all data remains up to date, helping to improve data management and drive more accurate decision-making. A successful ETL process involves three key steps.
1. Extraction of data from the database
Extraction is the action of extracting data from multiple source systems to be processed at a later stage. This step is focused on obtaining data as efficiently, and with as little impact on the source system, as possible.
Businesses today collect and store data in a variety of sources—each with their own way of organizing and formatting that data—and oftentimes the sheer volume of data can make this first step in the ETL process the most time-consuming. The extraction process helps pull data from a wide range of sources, such as
- Analytics tools
- Current databases
- Cloud-based storage locations
- CRM tools and systems
- Data storage platforms
- Data warehouses
- Email platforms
- Existing databases
- Mobile apps
2. Transformation of data to its proper formatting
After the desired data has been extracted, it then undergoes a transformation (i.e. conversion) to meet the requirements of the target system. To ensure that data meets the proper formatting, this step can involve:
- Data cleansing and validating data to ensure only quality data is migrated to the new system
- Sorting the data into columns or rows to improve usability and searchability
- Combining or merging data from multiple source systems and deduplicating that data
- Applying business rules to data
- Creating data validation rules that can be automated to check for data for quality and accuracy
This process entails several transformation types that ensure the quality and integrity of data. Without this step, businesses can’t be confident in the data being migrated or integrated into the target system—which can mean weeks or even months of effort and budget lost!
3. Load data into the final system
The Load step concludes the ETL process with the loading of the extracted and transformed data into the end system. The complexity of this step is dependent on the volume of your data, the structure of that data, and how often you load it to the final system. There are two main ways to load your data to the target system, which are:
Full loading- During a full loading process, all data that is extracted and transformed is placed into new records in your data warehouse. Full loading is rarer, and more time-consuming, but leaves you with a complete record of all data. However, consistent full loading can be difficult to maintain as it results in large, new datasets of all current data every time the process is carried out.
Incremental loading- Incremental loading means uploading data at scheduled intervals. This strategy is less extensive than full loading and a bit simpler. Instead of creating multiple new reports every time, this process only creates new records when there is new information. This results in more, smaller data warehouses.
Why is the ETL process important?
The most significant benefit of the ETL process is that it helps to consolidate all of your organization’s data in a single source, which is valuable for several reasons. Consolidated data is easier to access, easier to understand, and therefore, easier to use for informing your business’s decisions. When all of your valuable data can be stored in one place, your data science team has a much easier time working with it.
Altogether, the ETL process allows you to improve efficiency, gain a single point-of-view of your customers and operations, and develop a more accurate understanding of your data, all of which help you to better inform your business intelligence.
When the ETL process pulls data from multiple sources, some may be historic or legacy data and therefore may not be optimized for business use and analytics. Through ETL, it can be migrated to a central ETL data warehouse in a standardized format, which is much more suitable for business intelligence.
Best practices for the ETL process
With the importance placed on being able to make data-driven decisions, it is critical to take proactive measures to ensure data quality. Cleansing, validating, deduplicating, and profiling data are foundational best practices that not only provide faster time-to-value for ETL and other data integration and migration processes but empower organizations to make far better use of the data they have. Data cleansing and effective data management strategies are vital for maintaining accurate, insightful data.
For best results, the ETL process should happen during hours when traffic is low to prevent possible interruptions. Many organizations choose to automate data management solutions in order to prevent disruption from other tasks. Using the proper tools can help to automate all three steps of the ETL process and improve overall efficiency by saving time and reducing the chances of human error.
ETL process meaning vs. ELT
As technology and cloud-based data storage continues to evolve, the ELT, or extract, load, transform, process is starting to gain popularity over the ETL process. The ELT process is similar, involving the same steps, but in a different order. Instead of transforming the data first, this process allows you to transfer the data to a cloud-based data warehouse and make any necessary changes there.
The advantage of ELT processes is that they are more scalable and adaptable, especially for cloud-based businesses. The ability to transform data in its target storage system helps save a step by eliminating the need for additional software and data processing before the information is loaded into the data warehouse.
Regardless, both strategies are effective for preparing data to be used for informing key business decisions. Both of these strategies start with clean and accurate data.
Handle your ETL solutions with Experian
ETL is a valuable process for organizations seeking to make the most of their datasets and consolidate all of their data into one place for informing business intelligence. At Experian, we have a variety of data cleansing and ETL tools available to help streamline data management processes for your organization.
Find out how we help businesses undergo successful ETL processes. From email verification to address verification and beyond, we ensure that all of your data is accurate so that it can properly inform all of your key business functions. Contact Experian today to learn more about how we can help take your data management processes to the next level.
