Databricks has launched DLT-META, a framework that uses metadata to automate creating and managing Spark Declarative Pipelines at scale. This tackles challenges that come with expanding data environments. Declarative pipelines let teams define what data workflows should achieve while the system manages how they operate. This cuts down on custom code and boosts governance. As the number of sources and pipelines grows, keeping consistency and standards becomes tough. DLT-META solves this by storing pipeline definitions in metadata files (JSON/YAML).
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A generic pipeline engine can generate and run code from this metadata. This cuts down on duplication and manual scripting. It also makes onboarding new data sources easier. Maintenance needs are lower. Transformations, quality rules, and governance practices are applied consistently across pipelines. Templating and automating pipeline logic help organizations scale their use of declarative pipelines efficiently. This helps maintain consistent engineering patterns across teams and environments, making data engineering more agile and reliable for large workloads.