If you’re working with data in a lakehouse, you’re going to want a cloud-native ELT platform for the lakehouse that takes advantage of the power and scalability of the cloud to create analytics-ready datasets. You’ll also want features that uncomplicate the ELT process, such as a low-code visual interface, without sacrificing the depth and sophistication of the underlying jobs.
One of the biggest benefits of a cloud-native ELT platform in a unified analytics environment such as a lakehouse, is how shared cloud ELT can enhance communication and collaboration between data engineers, data scientists, business analysts, and others, that helps increase data science value and agility, helps get more data workers onboard, faster, and get everyone working in a common language and framework for all data projects.
6 essential things to look for in a cloud-native ELT platform for the lakehouse
However, not all ELT solutions are created equal. You want to ensure that a few essential capabilities are there. Besides cloud-native architecture and features that make the ETL/ELT process less complicated, data engineers and data scientists need an ELT platform that will:
Orchestrate data workflows that ingest data from a variety of sources
You want to ensure that you can get all of your essential data into the cloud, but it’s about more than just having pre-built connectors. You could be pulling from thousands of data sources, and the ability to create your own connectors that fit your data needs is extremely valuable.
Support overlapping dataset needs between data analysis and data science applications
The beauty of the lakehouse is that data analysts and data scientist can work with the same data in shared datasets. Done right, it can increase data quality and accuracy, decrease maintenance, and improve collaboration and productivity. But you want to ensure that your cloud ELT supports these overlapping use cases without creating duplicate data and transformation logic across multiple systems.
Enables the entire organization to work with data, not just a few
Right now you may be focused on data teams exclusively, but the number of data users working within a lakehouse is only going to grow. Keep that in mind as you choose an ELT solution. You want ELT that will rationalize data transformation workflows with rapidly changing business logic to support the democratization of data in the lakehouse for everyone in the organization.
Provide support for modern datasets that are by default shared, secured, and unified
Modern analytics requires a modern approach to data access. You need to bring data from thousands of sources together, make it accessible to the people who need it, and, above all, ensure that data is secure and complies with all of your company’s requirements.
Promote collaboration between data science and engineering
Cloud-native ELT shouldn’t just make data transformation easier for one team; it should facilitate collaboration between data engineering and data science to productionize data products. You want to ensure that you have support for things like real-time collaboration environments (think Google Docs for data projects), Git integration, and automated documentation.
Nurture a common language and common skillset to achieve mutually beneficial goals
The right ETL platform is more than just a tool: It’s a common way of working and managing requirements that data engineering, data science, and everyone else involved can use to improve analytics consistency and productivity.
Learn more about cloud-native ELT lakehouse and cloud-native ELT
To learn more about the benefits of a cloud-native ELT platform like Matillion ETL for Delta Lake on Databricks and the lakehouse, download our latest ebook, Guide to the Lakehouse: Unite Your Data Teams in the Cloud to Bridge the Information Gap.
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