What is extract, transform and load (ETL)?
Introduction
What is ETL?
ETL stands for extract, transform, and load. In data warehousing, these are the three steps to combine data from multiple different data sources in one centralized location. This process streamlines the data to allow for clear analysis so that marketers can make accurate, informed, and strategic decisions to help grow their app.
What is an ETL pipeline?
An ETL pipeline is the collection of steps that make up the extract, transform, and load procedure. The pipeline follows the set of processes the data goes through as it moves from its original source systems to its end location.
1. Extract the data from its original sources.
The data can come from structured and unstructured sources, including:
- Mobile devices and apps
- Documents
- Emails
- Business applications i.e. sales and marketing applications
- Existing databases including data storage platforms and data warehouses
- Analytics tools
- Equipment
- Customer relationship management (CRM) systems
- Third parties
2. Transform the raw data.
From its original raw form, the data goes through several processes to prepare for combination with data from other sources. These steps can include:
- Extracting unusable data
- Removing duplicate data
- Flagging data anomalies
- Resolving inconsistencies and missing values
- Applying consistent formatting rules
- Organizing data according to type
3. Load the data into the target database.
Once the data is streamlined, it is ready to transfer into the end data warehouse. If this is the first time loading the data into this particular end source, it is likely that all of the source data will be loaded at once. Thereafter, it is more likely that data be loaded in incremental batches as it changes or new data becomes available. Lastly, data can be loaded in real time or in scheduled batches.
What is the difference between data pipeline and ETL?
An ETL pipeline is one of several data pipeline types. Other forms of data pipelines may not involve the transformation of data or its transfer to an end location. Instead, some forms of data pipelines trigger next steps in longer data workflows.
ETL pipeline example
Let’s consider a hypothetical example where an app marketer is looking to streamline data from the social media channels they are currently advertising on using an ETL pipeline.
- Extract: Data is taken from Facebook, Twitter, and TikTok.
- Transform: Data is made consistent in formatting, categorization, and accuracy.
- Load: Prepared data is loaded into an end dashboard providing a consistent view of their marketing insights across all platforms in a central location.
ETL processes allow companies to gather data from multiple sources and consolidate it into one location for consistency, accuracy, and ease of analysis. It facilitates the creation of clear marketing insights.
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