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Summary: This is a summary of an article originally published by The New Stack. Read the full original article here →
Data integration has significantly evolved since data became centralized in data warehouses and lakes. ELT (Extract-Load-Transform) has replaced ETL (Extract-Transform-Load) by making analysts autonomous at getting access to the data they need in those warehouses.
And that leads to the question: how will data integration evolve given the indications we already have?
The reason is simple: the hard part about data integration is not building the connectors, but maintaining them.
With a platform that can do the following: ELT and reverse-ETL with open source connectors that you can customize at will; An active data engineering community incentivized to maintain the long tail of connectors; Data lineage; Observability across all data pipelines; Integration with other tools of the company data stack to ensure interoperability
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