With enormous amounts of data being collected from various sources, it is crucial to not only have the data stored in a single, secure, and organized manner, but also ensure that it is accessible for reporting, analytics, and machine learning models.
In order to accomplish these goals, data needs to undergo what is called an ETL process. ETL (Extract, Transform, and Load), which corresponds to the order of the process involved in order to pull and clean up data from multiple sources and put it into a database suited for data analysis.
How ETL is done
As the term implied, the traditional ETL data processing method involved extraction, transformation, and loading of data. But as technology evolved, it also gave rise to new processes and ideas that the traditional ETL did not account for, such as the transportation of data, the overlap in these three stages, and how new technologies are influencing how ETL operates. Given these factors, ETL has evolved into a more well-defined five-step process, though the basics of ETL still apply.
Extract – Source data is pulled from a source system and moved to a staging area, making the data available to subsequent stages in the ETL process. Often this process includes extracting data from various sources and varying data types to make the data available for cleaning and transformation. Common sources of data are SQL or NoSQL databases, files, CRMs or ERPS, and other business data sources.
Clean – The data then undergoes cleaning, also known as data cleansing or data scrubbing. This stage takes on several forms, depending on the makeup of the data sources, but typically they include filtering bad data, deduplicating, and authentication.
Transform – This is one of the most critical stages of the ETL process as during this stage, a series of data processing operations are performed on the data such as data translations or rearchitecting the scheme of how data is delivered. Other common procedures in the transform stage include sorting, applying validation rules across the entire dataset, converting currencies, concatenating text strings, and other similar procedures. These procedures aim to create consistency across all input data.
Load – Loading is the last stage before the transformed data is relocated to the data warehouse. This loading is done in an automated manner and can receive periodic updates to data in the data warehouse.
Analyze – Once the data has been extracted, transformed, and ingested into the data warehouse it’s ready for data analysis. Typically, data warehouses will follow an online analytical processing OLAP approach, which allows for multidimensional analysis on massive datasets in order to provide fast, accurate, and efficient analysis.
Relationship with data storage solutions
ETL is designed to prepare the data to make it suited to the database it will be placed in. Traditionally, such a database would be in the form of a data warehouse. But with new technologies and developments, data lakes and flexible storage schemas have become more viable options. Data lakes in particular provided a fundamentally different approach as they can primarily store raw unprocessed data and don’t require a well-known, predefined schema.
There is also the growth of cloud computing as cloud-based data analytics warehouses such as Amazon Redshift, Google BigQuery, and Snowflake. Their increased computing power have changed how businesses interact with data warehousing.
The benefits of ETL
ETL provides a simplified way to manage, view and use that data. For one, it allows businesses to see recent and legacy data side by side, which can help them to better understand factors such as market trends and customer requirements, which can, in turn, inform decisions relating to marketing and production. What’s more, all of a company's data sets are available in a single repository, including data from multiple sources and of various types. Such consolidation allows for easier and faster data search and retrieval.
ETL’s benefits are further maximized with the use of specialized ETL software that can automate routinary processes which improve productivity and efficiency. Team members are thus free to focus on other tasks that add value to the organization.
The inclusion of ETL in the business processes provides a competitive edge overall that enables it to only survive in these challenging conditions but also thrive as well. As such, ETL is no longer considered an option but a must-have in any business’ overall data structure.