Data transformation may involve steps such as cleansing, normalizing, structuring, validating, sorting, joining, or enriching data. Reasons for doing this could be to optimize the data for a different use case than it was originally intended for, or to meet the requirements for storing data in a different system. ELT step three: Transform dataĭata transformation is the process of converting data from one format into a different format. Additionally, given the breadth of destinations that may be used for storing data in an enterprise, an ELT tool should be able to send data into a multitude of systems. Depending on the replication mode that has been selected, the raw table in the destination will either be overwritten or appended to. In the ELT approach, the data that is extracted from the source system should be loaded into the destination in a raw and unmodified form. ELT pipelines are often used for data integration into a database, a data warehouse or a data lake. The Load phase of ELT is responsible for writing (loading) data that was extracted from the source into a destination system. In the case that change data capture replication is used, then a log of changes made to the source is read from the source. On the other hand, full refresh replication modes read the entire source dataset during a given sync run. For example, incremental replication modes only extract new or modified data from the source during a given sync run. The replication mode that is chosen determines how data is extracted from the source, which data is extracted from the source, and how often source data is sent to the destination. Airbyte supports hundreds of data sources and their associated data formats.ĮLT tools often support various replication modes. Common formats for source data include relational data, XML, JSON, and files. Your ELT solution must be flexible enough to extract data from a multitude of systems, in different formats, with different structures, and via different APIs. ELT step one: Extract dataĮxtracting data from a source system is one of the most important aspects of ELT, as this sets the stage for the next steps. Now that I have covered ELT at a high level, let's dive into the details of each step that is executed by an ELT pipeline. Airbyte is an example of an open-source ELT tool that meets these requirements. It is therefore becoming increasingly common to make use of fully-featured ELT tools that support hundreds of sources and destinations rather than building custom solutions. However, with the explosion of systems that are generating and collecting data, this approach has become infeasible. Historically, because the number of sources and destinations that were deployed in an enterprise were limited, enterprises may have created custom scripts or tools to move data between their systems. The ELT process is demonstrated in the following image:Īn example of an ELT pipeline What is an ELT tool?Īn extract, load, transform (ELT) tool executes the ELT pipeline that is used for moving data between systems. Once the data is loaded into the destination, dbt is commonly used for the creation and management of SQL statements that are executed by the destination to transform the data. ELT is often used for data integration into a database (Postgres, MySQL, etc), a data warehouse (BigQuery, Snowflake, etc.) or a data lake (S3, GCS, etc.). First raw data is read (extracted) from a source system, then it is written (loaded) into a destination system, and finally the data is modified (transformed) after being written to the destination. Let’s get started! What is an ELT pipeline?ĮLT is an acronym that refers to the three steps that are executed when moving data from a source to a destination system: extract, load, and transform. What are the benefits of ELT versus ETL?.What is the difference between ELT and ETL?.In this article, I will introduce you to the key ELT concepts, and will cover ELT-related questions that you may have, including the following: The ELT process that is executed by an ELT pipeline is often used by the modern data stack to move data from across the enterprise into analytics systems. An ELT pipeline is a data pipeline that extracts (E) data from a source, loads (L) the data into a destination, and then transforms (T) data after it has been stored in the destination.
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