No, ETL is not an appropriate process for data lakes, data marts or data lakehouses. Yes, ELT is the modern process for transforming and integrating structured or unstructured data into a cloud-based data warehouse. Yes, ETL is the traditional process for transforming and integrating structured or relational data into a cloud-based or on-premises data warehouse. And, the ELT process typically requires low maintenance given that all data is always available and the transformation process is usually automated and cloud-based. Lower cost and lower maintenance. ELT benefits from a robust ecosystem of cloud-based platforms which offer much lower costs and a variety of plan options to store and process data. Real-time, flexible data analysis. Users have the flexibility to explore the complete data set, including real-time data, in any direction, without having to wait for IT to extract, transform and load more data. The cloud platforms transform the data for any BI, analytics, or predictive modeling use case at any time. This allows users to extract and load any and all data they may need in near real time. These cloud-based platforms such as Amazon Redshift, Snowflake, Azure Synapse, Databricks and Amazon EMR offer near-unlimited storage and extensive processing power. Typically, the target system for ELT is a cloud-based data lake, data mart, data warehouse or data lakehouse. This can include raw, unstructured, semi-structured and structured data types.ĭata is transformed in the target system and is ready to be analyzed by BI tools or data analytics tools ELT ProcessĪll data is immediately loaded into the target system (either a data warehouse, data mart, or data lake). The ELT process is more cost effective then ETL, is appropriate for larger, structured and unstructured data sets and when timeliness is important. Transforming the data before it is loaded is necessary to deal with the constraints of traditional data warehouses.ĭata is loaded into the target data warehouse system and is ready to be analyzed by BI tools or data analytics tools.ĭata analysis on a single, pre-defined use case can be slightly more stable and faster with the ETL process given that the data set has already been structured and transformed.Ĭompliance with GDPR, HIPAA, and CCPA standards is easier with ETL given that users can omit any sensitive data prior to loading in the target system.ĮLT is an acronym for “Extract, Load, and Transform” and describes the three stages of the modern data pipeline. ETL ProcessĪ predetermined subset of data is extracted from the source.ĭata is transformed in a staging area in some way such as data mapping, applying concatenations or calculations. The ETL process is appropriate for small data sets which require complex transformations. The ELT process is more appropriate for larger, structured and unstructured data sets and when timeliness is important.ĮTL is an acronym for “Extract, Transform, and Load” and describes the three stages of the traditional data pipeline. As a result, the transformation step takes little time but can slow down the querying and analysis processes if there is not sufficient processing power in the cloud solution. As a result, transforming larger data sets can take a long time up front but analysis can take place immediately once the ETL process is complete.ĮLT stands for Extract > Load > Transform In the ELT process, data transformation is performed on an as-needed basis in the target system itself. Here is a side-by-side comparison of the two processes: ETL stands for Extract > Transform > Load In the ETL process, data transformation is performed in a staging area outside of the data warehouse and the entire data must be transformed before loading. The difference between ETL and ELT is when data transformation happens. Load refers to the process of placing a data set into a target system. Transform refers to the process of converting the format or structure of a data set to match that of a target system. The letters stand for Extract, Transform, and LoadĮxtract refers to the process of pulling data from a source such as an SQL or NoSQL database, an XML file or a cloud platform.
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