Unless you work in the world of data warehousing, you probably draw a blank when you hear someone say ETL.
Used by organizations for decades, ETL essentially describes the end-to-end process by which a company takes its full breadth of data and gets it to a state where it’s useful for business purposes.
Still confused? Not to worry — The Word Counter is here to help!
In this post, we’re exploring ETL to uncover what this acronym stands for and more. So, if you’ve ever wondered what ETL is or how the process works, keep reading.
What Does ETL Stand For?
According to Your Dictionary, the acronym ETL stands for Extract, Transform, and Load and refers to the functions performed when pulling data out of one database and placing it into another of a different type.
- Extract: First, data is extracted from one (or more) locations, such as a database or an application.
- Transform: The extracted data then needs to prepare for the target data warehouse, so it transforms from its original form.
- Load: Finally, the transformed data is loaded into a data warehouse where it can be used for analytics and reporting.
Not to get confused with ELT (extract load transform), ETL loads data into the staging server first and then into the target system whereas ELT loads data directly into the target system.
What Does the ETL Process Look Like?
Now that you have a basic understanding of ETL, let’s take a peek at what the process looks like:
Data extraction is where it all begins. This is essentially the staging area for the entire ETL process.
What we mean is that during the extraction process, raw data is exported or copied from the source location to a staging area where the raw data will then undergo processing — or data cleansing and transformation.
Data management teams are able to extract data from a myriad of various data sources that can be both unstructured and structured — a few of those sources include: web pages, emails, NoSQL servers, CRM systems, flat files, SQL servers, and even ERP systems.
In this stage, data quality problems are addressed independently as multi-source challenges or single-source challenges.
- Multiple source potential data problems: structural conflicts, inconsistent aggregating, inconsistent timing, and/or naming conflicts on a schema level — using a different name for the same things or using the same name but for different things
- Single source potential date problems: contradictory values, referential integrity, uniqueness, outside domain range, misspelling, data entry errors, duplicates, and/or redundancy
Throughout the transformation phase, you and your team will utilize various tasks to get the job done, and below, we have listed a few of these — though if allowed, the list could go on and on, so keep that in mind when utilizing the right task for your team:
- Validating, de-duplicating, cleansing, filtering, and authenticating the data
- Perform summarizations, translations, and/or calculations based upon the information within the raw data — for instance, things like editing text strings, converting currency or other various units of measurement, changing column and row headers for consistency, and much more
- Encrypting, protecting, and often removing data governed by governmental or industry regulators
- To ensure the data matches the schema of the target data warehouse, you may have to format the data into tables or joined tables
- Ensure data compliance and data quality by performing audits on the data
This is the final step in the ETL process; the data loading process can be done either at predetermined intervals (incremental loading) or all at once (aka full loading).
Either way, all of the newly transformed and extracted data will arrive at its new home — a data warehouse, data lake, or even something as simple as a delimited flat file.
- Incremental loading: This form of the data loading process is often looked at as the more manageable but also a tad less comprehensive form of two of the loading processes.
Incremental loading allows less expensive, smaller data warehouses to manage and maintain business intelligence by comparing all the incoming data with information already stored in the warehouse and only proceeding with the process if unique or new information is found.
- Full loading: During this form of the process, everything that had just recently gone through the transformation process heads to a data repository or data warehouse as new, unique records.
Are There Other Data Integration Methods?
Our word of the day — ETL is just the tip of the iceberg when it comes to data integration methods in fact there are a plethora of other data integration methods that one may utilize to facilitate data integration workflows.
To help clear up some confusion, we have listed the most commonly used data integration methods below:
- SDI or Stream Data Integration
- CDC or Change Data Capture
- Data virtualization
- Data replication
Does ETL Stand For Anything Else?
Although our word of the day is most commonly known as “extract, transform, load,” it does have a few other meanings. The acronym ETL can also stand for:
- Eutelsat Communications (France)
- Enemies to Lovers
- Electrotechnical Laboratory
- Executive Team Leader
- Extraction, Transformation, and Loading
- Educational Technology Learning (Southlake, Texas)
- Electron Transport Layer
- Environmental Testing Laboratories
- Enterprise of Telecommunications (Lao)
- Engineering Technical Letter
- Echo Train Length
- Extra-Terrestrial Life
- Estimated Tour Length (US Air Force)
ETL tools are an essential part of today’s business intelligence processes and systems. They automate the extraction process and create a much more efficient and reliable workflow.