Why Zero-ETL is Revolutionizing Data Engineering
Are you ready for a zero-ETL future?
Data engineering is now widely accepted as an important field. This has become even more apparent as companies look to get a piece of the AI pie. With many pre-built models available from companies like OpenAI and Anthropic, the challenge for many companies is no longer building their own models, but engineering solutions to work with existing models.
This has increased the demand for data engineers and has transformed job expectations from building ETL pipelines to building AI and ML pipelines.
Luckily, there are now tools that can simplify some of your existing workload as a data engineer so you can focus on driving business value with AI and ML pipelines.
Let’s take a look at just one slice of the data engineering pie, data ingestion, to see how it can be simplified and automated with no code and very low maintenance overheads.
Note: I’ll be focussing on AWS-specific tech in this post but a lot of what is discussed here can be transferred to other cloud providers
Simplifying Data Ingestion
Data ingestion is at the top of the list for data engineering tasks. We can’t do anything before we get all of our data sources in the right…
