What Are ETL Processes and Why Do You Need Them?
What Are ETL Processes?
ETL (Extract, Transform, Load) processes are a set of tasks to extract (Extract) data from data sources and transform (Transform) them in order to obtain relevant information for different stakeholders, who have to consume it through other systems, tools or applications in which the data will be loaded (Load).
ETL processes are fundamental in companies that have a large amount of data from many sources.
Benefits of ETL Processes
- Analyse large amounts of enterprise data more efficiently than manual processes.
- They increase productivity in collecting and using data, which is more easily gathered from multiple sources.
- At the same time, by automating processes, they reduce potential human error.
- Unify different data sources under one model capable of providing high-quality information that facilitates business decision-making.
- Some solutions do not require technical knowledge, such as knowing how to write code, to put them into operation. This makes them easier for some workers to use.
Phases of an ETL Process
There are three phases of an ETL process. Good design of all internal processes reduces operational failures.
It is the collection of data from a variety of sources and can come in formats as varied as binary files, relational databases, images, etc. On the other hand, they come from various sources, both internal (for example, from a company’s CRM, servers, websites, results of advertising campaigns, etc.) and external (open databases, customer files, etc.).
Before moving on to the transformation phase, it is necessary to guarantee minimum data quality standards that ensure its integrity for its subsequent transformation. Likewise, the data must be extracted in a way that does not affect the systems or the response times at work.
To avoid system downtime, mass extraction operations can be done at times when the system is less used.
It is the modification of collected data in order to make useful decisions with it. If the data is structured, it is easier to process. If it is unstructured, it must first be given an internal structure. In any case, the data has to follow the guidelines of the company.
In this phase, tasks such as data filtering, cleaning, validation, merging, sorting… Unification tasks such as translations or currency conversions, units of measurement… can also be executed.
Once the data has been transformed, it is uploaded, for example, into a Data Warehouse that is accessed by the different business departments concerned or linked analytics solutions. The upload frequency depends on the system.
In this phase, there are also updated processes to delete or replace existing data.
ETL processes involve different business professionals, such as analysts or managers. There may be interim storage tables in which data is temporarily held before it is loaded to its final location.
On the other hand, tasks could be performed in parallel. That is, while some data is being filtered, other previously filtered data is being loaded; and, at the same time, new data is being extracted. Moreover, if the business needs to change, the Data Warehouse or other moments of the processes also change.
Five Steps of the ETL Process
Some divide the phases of ETL processes into five steps, which are: Extract, Clean (to confirm data quality), Transform, Load and Analyse.
ETL Processes: Examples
An example of the use of ETL processes in various corporate departments could be seen in companies in the retail sector. If the marketing department has a customer’s email address and the sales department has a customer’s name and shipping address, the data would be centralized and cleansed in a single database to eliminate duplicate information.
Thus, ETL processes are used to transport data from various sources to be cleaned and formatted in the same way. Once stored in the Data Warehouse, it can be analysed or saved for other processes.
Another example of the use of ETL processes is to migrate data from applications before using a new one or updating the version of the existing one.
How Plain Concepts Uses ETL Processes
The Plain Concepts Data team is well-versed in ETL processes.
When a client like you asks us to develop a data platform, we develop an ETL to bring together all the data you have and enrich that platform. In this way, we centralize the data in one place and you will be aware of all the potential hidden in it, no matter where in what format, or how often you need to collect it.
You’ve already seen the benefits of working with ETL processes. If you are thinking of deploying a new data platform to get the full potential of this information, we work with you under this approach, in order to unify in one place the resources that will help you to keep driving your business forward. You’re just one click away from getting started.