![]() The ETL workflow implies that your raw data does not live in your data warehouse. Once this transformed data is in its final destination, it’s most commonly exposed to end business users either in a BI tool or in the data warehouse directly. In the final stage, the transformed data is loaded into your target data warehouse. ![]() These tools often involve little to no code and instead use Graphical User Interfaces (GUI) to create pipelines and transformations. ETL products: There are ETL products that will extract, transform, and load your data in one platform.Data engineers may leverage technologies such as Apache Spark or Hadoop at this point to help process large volumes of data. Unlike ELT transformations that typically use SQL for modeling, ETL transformations are often written in other programming languages such as Python or Scala. Custom solutions: In this solution, data teams (typically data engineers on the team), will write custom scripts and create automated pipelines to transform the data.To actually transform the data, there’s two primary methods teams will use: As a result, the transformation stage here is focused on data cleanup and normalization – renaming of columns, correct casting of fields, timestamp conversions. In ETL workflows, much of the actual meaningful business logic, metric calculations, and entity joins tend to happen further down in a downstream BI platform. Transform Īt this stage, the raw data that has been extracted is normalized and modeled. ![]() Data teams can also extract from these data sources with open source and Software as a Service (SaaS) products. Data engineers are often incredibly competent at using different programming languages such as Python and Java. In addition, these extraction scripts also involve considerable maintenance since APIs change relatively often. ![]() Because making and automating these API calls gets harder as data sources and data volume grows, this method of extraction often requires strong technical skills. To actually get this data, data engineers may write custom scripts that make Application Programming Interface (API) calls to extract all the relevant data.
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