ExpertWe pointed nine iPaaS platforms at the same messy stack - a Postgres database, three SaaS APIs, and a webhook - and asked each to move data without a line of glue code. The split that surprised us was not open source versus enterprise. It was how few let an engineer drop in real code the moment the visual builder ran out of road.
We pushed the same 5,000-row file of mangled postal and email records through ten address verification tools, wiring each into a real ETL job instead of a demo form. The surprise was not raw accuracy. It was how differently these tools behave once a record fails, and how few return a confidence score a pipeline can branch on cleanly.
After running a synthetic finance estate through nine federation platforms with Postgres, Snowflake, an Iceberg lake, Salesforce, and a Kafka topic, the surprise our team kept landing on was how fast every demo query ran and how badly the same query bent the moment the largest source forced a hash spill.
Data extraction covers three jobs that vendors deliberately blur together: scraping the public web, ingesting SaaS and database sources through managed connectors, and automating the glue between them. The best data extraction tool depends almost entirely on which of those three you are actually doing.