What is an ETL?

ETL (Extract, Transform, Load) is a data integration process that allows for the transfer of raw data from a source system, the preparation of data for appropriate use, and the sending of data to a target database. Before the data is sent, it is transformed on an intermediary server.

ETL: Definition

The ETL process (Extract Transform Load) allows for the extraction of raw data from the database, reconstructing it, and finally loading it into a data warehouse. ETL has existed for several years but has evolved significantly to meet new demands associated with the rise of cloud, Software as a Service (SaaS), and big data.

Today's ETL must enable data enrichment and handle billions of transactions. It also supports structured or unstructured data coming from different sources (website, cloud).

The first ETL appeared in the 1970s when large companies began aggregating and storing different types of data from multiple sources. ETL software was developed to meet the need to integrate these diverse data.

Over time, the number of data sources and types of data has increased along with the number of ETL providers. This has helped keep prices low until these solutions became available for most companies. Thus, these tools contributed to the emergence of data-driven businesses.

How does ETL work?

As we saw earlier, the role of an ETL solution is to collect relevant data from multiple sources, transform it to make it compatible with the Data Warehouse, and finally load it into the database.

The functioning of ETL is divided into three phases. The Extraction phase, the Transformation phase, and the Loading phase.

Extraction : The goal of ETL is to produce clean, accessible data that can be used effectively in analysis, business intelligence, or business operations. Raw data can be extracted from various sources. Specifically: 

• Existing databases,

• Activity logs (network traffic, error reports, etc.),

• Application behavior, performance, and anomalies,

• Security events.

The extracted data can be stored in locations such as a data lake (a tool for storing and preserving a large volume of raw data for an indefinite period) or a DW (Data Warehouse). 

The Transformation: The transformation phase of the ETL process is one of the most important operations. This phase consists of applying the company’s internal rules to the raw data to meet reporting requirements. The raw data is cleaned and converted into appropriate reporting formats (uncleaned data makes it difficult to apply reporting rules internally).

The Loading : The last step of the standard ETL process is to load the extracted and transformed data into a new location. Generally, data warehouses support two modes of data loading: full loading and incremental loading. 

The ETL processing (point cycles or cycle programs) can be launched from the command line or graphical interface. But there are certain elements to consider. For example, exception handling can be a very tedious process. In many cases, generating data extracts can fail if one or more systems go down. Incorrect data in one system  can affect the data extracted from another system. Monitoring and error handling are therefore essential tasks!

What is the purpose of ETL?

ETL has several use cases. Its main goal is to transform data to transfer it to a data warehouse, but it can also be used to transfer data from legacy systems to modern systems with different data formats. In the era of big data, the Internet of Things, social networks, video, or even open data, ETL also adapts to new types of data and sources.

ETL VS ELT:

In recent years, we have witnessed the explosion of new types of data such as Big Data. The growth of these different types and volumes of data complicates the architecture of the data warehouse and makes the collection of all this information more tedious. One of the challenges for companies is the centralization of data; this is where ETL comes in, but also ELT (Extract, Load, Transform), which is a variation of ETL. So how do these two processes differ?

The difference primarily lies in three points: 

• A different process: In the case of ETL, the transformation takes place in a transit area; therefore, loading only occurs after structuring. In contrast, transformation occurs after loading during the ELT process, as there is no intermediary server, which is why data is directly loaded into the target warehouse.

• A different storage mode: ETL retains all data in a data warehouse but does not support data lakes, unlike ELT which stores data in both a DW and in data lakes. Thus, it has the advantage of storing a larger volume of data.

• Data security: Data security is a crucial issue in data processing. In this respect, ETL responds perfectly as it ensures the security of exchanges throughout integration. Personal data remains anonymous, and governance is ensured in a transient server. Unlike ELT, which does not fulfill this role. In an ELT process, the absence of governance can lead to security gaps.

In summary:

ETL (Extract, Transform, Load) is a data integration process that allows for the transfer of raw data from a source system, prepares the data for appropriate use, and sends it to a target database. ETL has existed for several years and has evolved to meet new demands associated with the rise of cloud, SaaS, and big data.

The ETL process is divided into three phases: extraction, transformation, and loading. The main objective of ETL is to produce clean, accessible data that can be used effectively in analysis, business intelligence, or commercial operations. Raw data can be extracted from various sources, stored in locations such as a data lake or a data warehouse, and then transformed by applying the company’s internal rules to meet reporting requirements.

ETL has several use cases, including transforming data to transfer it to a data warehouse and transferring data from legacy systems to modern systems with different data formats.

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F.A.Q:

What is ETL?

ETL (Extract, Transform, Load) is a data integration process that allows for the transfer of raw data from a source system, prepares the data for appropriate use, and sends it to a target database.

How does ETL work?

The ETL process is divided into three phases: extraction, transformation, and loading. The extraction phase consists of collecting relevant data from multiple sources. The transformation phase involves applying the company’s internal rules to the raw data to meet reporting requirements. The last step consists of loading the extracted and transformed data into a new location, usually a data warehouse.

What types of data can be extracted?

Raw data can be extracted from various sources, such as existing databases, activity logs, applications, security events, etc.