The differences between ELT and ETL and how to decide which one you need.
ELT and ETL are acronyms for the extract, load, transform/extract, transform, load processes used to move data into a desired target. All organizations are collecting data that often comes from all kinds of different places. For example, your organization might keep track of things in Square, SQL Server, Salesforce, etc., and in order to do anything meaningful with that information, you need to get it all into the same place. ELT and ETL make that possible by pulling/synchronizing the data from each source and either loading into a cloud-based data warehouse so that it can be transformed by different workloads to uncover various insights, or by transforming it for analyses before loading it into its final destination.
You can use either process, whether you’re still using an on-premise solution, or you’ve moved on to a cloud-based data warehousing solution where you’re leveraging data in real-time. However, determining the best solution for your business is really a matter of whether all of your data is in a relational and/or structured format, or you have structured, unstructured, and semi-structured data types that you plan to work with. ELT supports structured, unstructured, semi-structured, and raw data types, while traditional ETL has historically struggled with unstructured or semi-structured data, working much easier with a relational or structured data format.
Relational data is information that is related to other information through pre-defined relationships and is organized as a set of tables with columns and rows. It can be accessed in many different ways without reorganizing the database tables themselves, making it extremely easy to work with. Microsoft SQL Server, Oracle Database, MySQL and IBM DB2 are all examples of relational databases.
Structured data is usually quantitative information that is logically organized into tables, making it easy to search in relational databases. Some examples of structured data include names, dates, addresses, credit card numbers, stock information, geolocation, etc.
Unstructured data is mostly qualitative information that is typically more difficult to process, since it must be extracted and categorized before it can be analyzed. Analytics tools with machine learning (like DataLakeHouse) are best for finding insights from unstructured data. A few examples of unstructured data include text, video files, audio files, mobile activity, social media posts, satellite imagery, surveillance imagery, business documents, webpages, open-ended survey responses, etc.
Semi-structured data is somewhat organized information that is categorized with meta tags. Although semi-structured data can be grouped, the information within each group is unstructured. Examples of semi-structured data include emails, CSV, XML, JSON, HTML, RDF, EDI, NoSQL databases, etc.
Now that you have a pretty good understanding of the differences between ELT and ETL, you should be able to pretty easily figure out which process is best for your organization. All that’s left to do is select a tool that supports integration from the sources you’re using into the target you’d like to bring them into. As far as targets go, we highly recommend Snowflake for its incredible ability to support multiple workloads without compromising performance or (more importantly) your data.
DataLakeHouse is a 100% Snowflake-focused, advanced end-to-end analytics platform that offers a trifecta of data tools including ELT, industry-specific pre-built Models, and Analytics combined with Machine Learning to offer actionable insights and predictions for extremely effective business decision-making (woah that was a lengthy sentence)!
The platform will be launching very soon and will be offering ELT solutions for common data sources. Please take a few moments to subscribe to our newsletter by filling out the form in the footer below and follow us on social media (icons linked to our accounts in the footer as well) to stay up to date with what we’re up to and be first to know when we’re launching.
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