While ELT adoption is growing, we still see ETL use cases for processing large volumes of data and adhering to strong data governance principles. This can make it harder to ensure that transformations are performing the correct functionality. Because transformations occur before load, only transformed data lives in your data warehouse in the ETL process. 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. Ad platforms (Facebook Ads, Google Ads, etc.).Some examples of these data sources include: Data that is extracted at this stage is likely going to be eventually used by end business users to make decisions. In this first step, data is extracted from different data sources. We’ll go into greater depth for all three steps below. In an ETL process, data is first extracted from a source, transformed, and then loaded into a target data platform. If you don’t talk about the benefits and drawbacks of systems, how can you expect to improve them? How ETL works It’s important to talk about ETL and understand how it works, where it provides value, and how it can hold people back. The same transformations can occur in both ETL and ELT workflows, the primary difference is when (inside or outside the primary ETL workflow) and where the data is transformed (ETL platform/BI tool/data warehouse). In many ways, the ETL workflow could have been renamed the ETLT workflow, because a considerable portion of meaningful data transformations happen outside the data pipeline. In ETL workflows, much of the meaningful data transformation occurs outside this primary pipeline in a downstream business intelligence (BI) platform.ĮTL is contrasted with the newer ELT (Extract, Load, Transform) workflow, where transformation occurs after data has been loaded into the target data warehouse. ETL, or “Extract, Transform, Load”, is the process of first extracting data from a data source, transforming it, and then loading it into a target data warehouse.
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