The primary difference between the traditional ETL and the modern ELT workflow is when data transformation and loading take place. In this step, data teams may ensure primary keys are unique, model relations match-up, column values are appropriate, and more.Ĭommon ways to transform your data include leveraging modern technologies such as dbt, writing custom SQL scripts that are automated by a scheduler, utilizing stored procedures, and more. QA’d: Data is tested according to business standards.Heavily Transformed: Business logic is added, appropriate materializations are established, data is joined together, etc.Lightly Transformed: Fields are cast correctly, timestamp fields’ timezones are made uniform, tables and fields are renamed appropriately, and more.hence the need for transformation! During the transformation process, data from your data sources is usually: Timestamps may be in the incorrect timezone for your reporting.Some columns are potentially the incorrect data type.In the final transformation step, the raw data that has been loaded into your data warehouse is finally ready for modeling! When you first look at this data, you may notice a few things about it… But for all intents and purposes, the data loaded into your data warehouse at this stage is in its raw format. If you use an extraction and loading tool like Fivetran, there may have been some light normalization on your data. Custom or in-house extraction and load processes usually require strong data engineering and technical skills.Īt this point in the ELT process, the data is mostly unchanged from its point of extraction. Most of the SaaS applications that extract data from your data sources will also load it into your target data warehouse. Examples of other data storage platforms include data lakes such as Databricks’s Data Lakes. Some examples of modern data warehouses include Snowflake, Amazon Redshift, and Google BigQuery. Load ĭuring the loading stage, data that was extracted is loaded into the target data warehouse. Since not every data source will integrate with SaaS tools for extraction and loading, it’s sometimes inevitable that teams will write custom ingestion scripts in addition to their SaaS tools. By establishing the option to create and manage pipelines in an automated way, you can extract the data from data sources and load it into data warehouses via a user interface. However, the recent development of certain open-source and Software as a Service (SaaS) products has removed the need for this custom development work. Some examples of data sources can include:Īccessing these data sources using Application Programming Interface (API) calls can be a challenge for individuals and teams who don't have the technical expertise or resources to create their own scripts and automated processes. The data extracted is, for the most part, data that teams eventually want to use for analytics work. In the extraction process, data is extracted from multiple data sources. We’ll go over the three components (extract, load, transform) in detail here. In an ELT process, data is extracted from data sources, loaded into a target data platform, and finally transformed for analytics use. Data teams report that the ELT workflow has several advantages over the traditional ETL workflow which we’ll go over in-depth later in this glossary. Rather, you are able to load all of your data, then build transformations on top of it. Transitioning from ETL to ELT means that you no longer have to capture your transformations during the initial loading of the data into your data warehouse. This represents a fundamental shift from how data previously was handled when Extract, Transform, Load (ETL) was the data workflow most companies implemented. What is ELT (Extract, Load, Transform)?Extract, Load, Transform (ELT) is the process of first extracting data from different data sources, then loading it into a target data warehouse, and finally transforming it.ĮLT has emerged as a paradigm for how to manage information flows in a modern data warehouse.
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