An interesting thing has happened with the rise of cloud data platforms. While our data warehouses and data lakes modernized and moved to the cloud, most ETL platforms did not. Traditional ETL solutions have capabilities more suited to on-premises data architectures than the modern cloud data platform. Enter cloud-native ELT.
If you’re analyzing data in a lakehouse environment, you need a cloud-native solution that supports a slightly different take on the data transformation stage. You need to move from ETL to ELT, taking advantage of the power of the cloud platform’s compute capabilities by “pushing down” the transformation step. Using the power of the cloud to help transform data is faster, more economical, and better suited to supporting modern analytics.
But beyond the basics, there are several other reasons why it makes sense to embrace cloud-native ELT when it comes to the lakehouse.
Remove complexity, don’t add it
For Spark- and Scala- heavy processes that are commonplace in a lakehouse environment, traditional ETL tools won’t do. For example, some ETL vendors have tried to modernize by having older tools to generate heavy Spark and Scala code. But tools that are scripted and text-based don’t remove any complexity. If anything, they add to it. Sometimes it seems that the only way to adopt these tools is to get a computer science degree and/or learn a new language, which is basically akin to learning a new programming framework and acts as a barrier to entry for fresh talent.
Older tools weren’t created with the flexibility, features, and scale of the cloud in mind. They are harder to use and require a lot of work that can be made obsolete by automated features and visual interfaces in modern ELT platforms. They simply can’t keep up in the cloud, which is the last thing a time-strapped data team needs. In short, be wary of tools that take older paradigms and try to shoehorn them into a very different modern context.
Unify data engineers and data scientists with a common framework
ELT plays an important role in creating a unified approach to data ingestion and enrichment in the lakehouse, helping bridge the gap between data engineers and data scientists. SQL is the one protocol that is consistent in almost every cloud data platform. It’s the layer in ELT that abstracts the complexity of these very technical yet powerful data platforms.
Cloud-native ELT and low-code tools
With the right set of unified tools or platforms that can abstract the complexity of Spark and Scala using SQL and low-code processes, data engineers can eliminate hours of hand-coding and produce repeatable data pipelines that enable less technical data professionals to easily collaborate and work with data in the cloud.
A common language for streamlined collaboration
The right toolset will also develop and nurture a common language for describing and communicating data requirements, helping to reduce misunderstanding and wasted effort and accelerate productivity.
Scale up at a speed that matches the cloud
Data teams of all kinds and technical abilities benefit from common, easily transferable skills. A highly visual interface, drag-and-drop components, and a common underlying language, such as SQL, all facilitate faster cross-functional collaboration and deliver more value for your business, faster.
Increasingly, organizations cannot afford the time it takes to develop and maintain highly specialized skills like Scala or Java, using niche tools and code intensive solutions. And such solutions are not easily scalable beyond a few key team members. Getting more data workers on board, faster and easier than before, and getting the value of their expertise right away, is a key step in unlocking your data-driven business potential.
Learn more about cloud-native ELT and the lakehouse
To learn more about how cloud-native ELT like Matillion ETL for Delta Lake on Databricks fosters communication, collaboration, and increased productivity among data teams, download our latest ebook, Guide to the Lakehouse: Unite Your Data Teams in the Cloud to Bridge the Information Gap
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