![]() Moreover, if the business needs to change, the Data Warehouse or other moments of the processes also change. 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. On the other hand, tasks could be performed in parallel. There may be interim storage tables in which data is temporarily held before it is loaded to its final location. In this phase, there are also updated processes to delete or replace existing data.ĮTL processes involve different business professionals, such as analysts or managers. The upload frequency depends on the system. 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. 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. In any case, the data has to follow the guidelines of the company. If it is unstructured, it must first be given an internal structure. If the data is structured, it is easier to process. It is the modification of collected data in order to make useful decisions with it. To avoid system downtime, mass extraction operations can be done at times when the system is less used. Likewise, the data must be extracted in a way that does not affect the systems or the response times at work. 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.).īefore moving on to the transformation phase, it is necessary to guarantee minimum data quality standards that ensure its integrity for its subsequent transformation. 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. Good design of all internal processes reduces operational failures. There are three phases of an ETL process. ![]() This makes them easier for some workers to use. Some solutions do not require technical knowledge, such as knowing how to write code, to put them into operation.Unify different data sources under one model capable of providing high-quality information that facilitates business decision-making.At the same time, by automating processes, they reduce potential human error.They increase productivity in collecting and using data, which is more easily gathered from multiple sources.Analyse large amounts of enterprise data more efficiently than manual processes.What Are ETL Processes?ĮTL (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).ĮTL processes are fundamental in companies that have a large amount of data from many sources. *With the collaboration of Daniel Álvarez and Juan Luis Montoya.
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