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ETL and ELT Architectures

Trifacta’s Open and Flexible Solution for Your Data


The Best for Both Architectures

ELT and ETL are both prominent data architectures. ETL (Extract, Transform, Load) extracts data from different sources, transforms it into the desired format, and then loads it onto the target destination. In contrast, ELT (Extract, Load, Transform) adjusts the order of these steps, extracting data and loading it directly onto the target, such as a data warehouse, before performing the transformation steps using the target’s computing resources.

Regardless of which approach you use, Trifacta’s open and flexible data engineering solution can help you profile, prepare, and pipeline data with a scalable solution across any cloud ecosystem. On both ETL and ELT architectures, data practitioners can use Trifacta to build robust, end-to-end data pipelines quickly and easily using an open, interactive, and collaborative data engineering cloud solution.


Build Automated Scalable Data Pipelines

With Trifacta, you can build, orchestrate, and automate robust ETL/ELT data pipelines at any scale. The open, interactive solution from Trifacta can help you connect to over 180 data sources, load them on to the destination of your choice including leading cloud data warehouses, transform the data, and automatically publish outputs to your downstream applications.

Enable Self-Service ELT Architectures

Trifacta enables data practitioners including analysts and engineers to on-board raw data and transform the data using a visual, intuitive interface or use code with SQL or Python. With live previews, feedback, and continuous validation, you have complete visibility into your data. Additionally, you can operationalize your data flows for both parallel and conditional execution.

Ensure High Quality at Every Step

Whether your data is structured or unstructured, Trifacta makes it easy to profile datasets and identify data quality issues at any scale, ensuring that you can trust the data you load and transform. Trifacta uses structure, content, and relationship profiling to automatically identify dataset formats, schemas, specific attributes and relationships across attributes and datasets.



There is currently an industry trend of moving from ETL to ELT. ETL forces you to build the entire data pipeline when data models change since it needs to be transformed whenever there is a change. This can be a significant drain on resources. With ELT, there are no assumptions on how the data will be used. Since the transformation happens within the target, data pipelines need not be built every time there is a change. This makes ELT popular, since it requires less resources and allows you to quickly and easily work with any scale of data. As data approaches move from ETL to ELT, Trifacta makes it easy to profile, prepare, and pipeline data at any scale across any cloud ecosystem.


“Combining native cloud support with their best-in-class data preparation functionality, it was easy for our team to choose Trifacta over traditional desktop ETL platforms such as Alteryx. Trifacta excels at onboarding custom data for reporting and analytics. With the amount of data we receive from clients and third parties, we can quickly and easily make this data usable for our clients.”

“We selected Trifacta as our data preparation platform and Microsoft Azure as our cloud provider so that we can shift to a modern stack and retire old ETL and any legacy tools that have little to no resource support. The legacy systems that are currently deployed could restrict us from being competitive as the cost of managing these tools has become too expensive. Additionally, it has become increasingly hard to find people with the skills to maintain these legacy tools and even harder to train our internal people.”

“With Dataprep by Trifacta, you don’t need to have a large team. In the past, you needed to have a lot of developers or Excel experts. Now they’re no longer required, which translates into payroll savings for the company. There’s also a huge amount of flexibility—if something breaks, it’s a lot easier to troubleshoot a Dataprep flow than it is to troubleshoot multiple queries, especially if you have multiple developers with different coding styles.”