data

Definition: ETL

The information that websites collect from you is intended to model consumption habits. This information can even be used for other causes. In all cases, it takes an entire industrial process to make the data speak. ETL brings together the first 3 steps towards in-depth analysis in a data center. This post invites you to better understand the trio.

Data extraction, transformation and loading, or ETL

As in a physical supply chain, a data center is regularly supplied. The data warehouse accommodates a large amount of raw information. These are integrated and classified to form silos. Various information and parameters are thus taken from operational systems. Copies are sent to the data warehouse for future analysis. This unified system is intended for better understanding of business models.

Extracting data and transferring it to the virtual warehouse is called ETL. As the acronym from English suggests, the process includes 3 distinct stages. That said, this is a simplistic presentation of what happens in a data center. In reality, the information follows a much more complex path. It encompasses other related phases including transfer and authentication.

A process based on the identification and collection of data

During data extraction, special algorithms take care of taking information from various places. The information can come from a browser, billing software, geolocation, etc. In addition to files in various formats, spreadsheets, application recordings and various contents are extracted. At this stage, the information is raw and cannot yet be used.

Sometimes data is transformed before being sent to a data center. This is the case for certain content emanating from mobiles whose bandwidth remains limited. Thus, the extraction is done with file size compression. That said, certain information in the gigabyte range can also be taken at the source. Some data is transmitted in real time while others are compiled before being collected.

The transport and digital transformation of information

Data extracted from any source can follow two outcomes. Sometimes, they are directly intended for analysis software. They also sometimes pass through an intermediary system. The latter can be a storage location while awaiting exploitation. Occasionally, data scientists schedule the transformation of information immediately after extraction.

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Most data analysis processes require content transformation. This step varies depending on the circuit. Most often, this involves changing the appropriate format. That said, dedicated algorithms can take care of cleaning the raw data. Some robots also have the mission of assembling or grouping files. Then, there is also the validation of the data in order to have much more reliable results at the output.

data extraction

Two methods for loading databases

A Data Warehouse is provisioned in two ways.

  • Full load refers to the very first time data is delivered. This involves a large amount of information delivered in one piece.
  • Incremental load involves small amounts of information relayed at regular intervals or in larger batches.

Once received in a data center, the dissected information follows several possible paths. Often, specialists analyze them with software that uses queries. Summary statistics or forecasts can emerge. Sometimes the task is so complex that it must be entrusted to an entire Business Intelligence team. These experts will know how to make the donations speak in different ways depending on the expectations of the sponsor.

Strength and limit of standardized data exploitation

ETL or ELT processes are specific to companies specializing in data mining. That said, large corporations and start-ups can benefit from it on a small scale. Data extraction and analysis has enabled commercial brands to make the best decisions. The accuracy of forecasts depends on the multiplicity of sources, but also on the quality of the information collected. Furthermore, marketing strategies must be decided based on activity and geographic location.

The automation of analyses, machine learning and Artificial Intelligence become reality thanks to the perfect mastery of ETL. The latter also contributes to the evolution of the Internet of Things. The interface of household appliances was designed taking into account feedback and expectations. Although it remains discreet, the field of data mining contributes enormously to the simplification of daily life. From modern automobiles to digital medicine, biotechnical agriculture and robotics, everything can be improved with ETL.