Data pipelines are like insurance. You only know they exist when something goes wrong. ETL processes are constantly toiling away behind the scenes, doing heavy lifting to connect the sources of data from the real world with the warehouses and lakes that make the data useful.
Products like DBT and AirTran demonstrate the lebanon whatsapp number data repeatability and scriptability of data pipelines. They are the ultimate input-to-output headless IT middleware, like a router for IP packets but transferring data instead.
What could GenAI add to this tidy bit of infrastructure? It turns out, a lot. Combine the scriptability of data pipelines with a language model’s ability to generate code and you get dynamic, self-updating ETL processes. Using the language model’s ability to understand and correct errors, those ETLs can be self-healing when disruptions like a typical schema change, while numeric overflow or a full disk would have previously crippled the pipe.
One level higher in the stack, it’s not uncommon for errors to be introduced in harmonization when the input contract changes with, for example, the renaming of a brand or the addition of a new market. Traditional FLITE (for-loop-if-then-else) code would either fail to notice the issue (kicking the problem downstream) or error-out. When a language model is monitoring the ETL process, this sort of logical change can be detected and semantically corrected. When an issue does make it through the pipeline, it is easy to update the prompts that are managing the process. Data updates are the new “model drift.