What is ETL?
In our articles related to AI and Big Data in healthcare, we always talk about ETL as the core of the core process. We do not write a lot about ETL itself, though. In this post, we’ll give a short overview of this procedure and its applications in businesses.

ETL is the abbreviation for Extract, Transform, Load that are three database functions:

Extract is the process of reading data that is assumed to be important. The data can be either raw collected from multiple and different types of sources or taken from a source database.

is the process of reading data that is assumed to be important. The data can be either raw collected from multiple and different types of sources or taken from a source database. Transform is the process of converting the extracted data from its previous format into the format required by another database. The transformation occurs by using rules or lookup tables or by combining the data with other data.

is the process of converting the extracted data from its previous format into the format required by another database. The transformation occurs by using rules or lookup tables or by combining the data with other data. Load is the process of writing the data into the target database, data warehouse or another system

ETL in its essence is a type of data integration used to blend data from multiple sources.

ETL vs. ELT

The ETL paradigm is inherent to Data Warehousing, and Big Data has significantly changed the order of the processes. In Big Data, data is “lifted and shifted” wholesale to a repository, such as a Data Lake, and is held there in the original format. It is transformed “on the fly” when needed by Data Scientists, creating the procedure of ELT, or Extract, Load, Transform.

One of the main benefits of ELT is a shorter load time. As we can take advantage of the built-in processing capability of data warehouses, we can reduce the time that data spends in transit. This capability is most useful when processing large data sets required for business intelligence and big data analytics.

In practice, however, things are not so black and white. Many Data Lakes, for example, contain intermediate merged and transformed data structures to ensure that each Data Scientist doesn’t repeat the same work, or carry it out in a different way.

Where are ETL/ELT used?

ETL is not a new technology: businesses have relied on it for many years to get a consolidated view of the data. The most common uses of ETL include:

ETL and traditional uses

Traditionally, ETL is used to consolidate, collect and join data from external suppliers or to migrate data from legacy systems to new systems with different data formats. ETL tools surface data from a data store in a comprehensible for business people format, making it easier to analyze and report on. The key beneficiaries of these applications are retailers and healthcare providers.

ETL and metadata

ETL provides a deep historical context and a consolidated view for the business by surfacing the metadata. As data architectures become more complex, it’s important to track how the different data elements are used and related within one organization. Metadata helps understand the lineage of data and its impact on other data assets in the organization.

ETL and Data Quality

ETL and ELT are extensively used for data cleansing, profiling and auditing ensuring that data is trustworthy. ETL tools can be integrated with data quality tools, such as those used for data profiling, deduplication or validation.

ETL and Self-Service Data Access

Self-service data preparation is a fast-growing field that puts the power of accessing, blending and transforming data into the hands of business users and other nontechnical data professionals. ETL codifies and reuses processes that move data without requiring technical skills to write code or scripts. With this approach integrated into the ETL process, less time is spent on data preparation, improving professionals’ productivity.
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['Artificial Intelligence', 'Healthcare', 'Big Data', 'Technology', 'Data Science']