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    AWS Glue vs Databricks: Choose at the Right Level

    Most comparisons put two feature lists side by side and stop. This AWS Glue vs Databricks comparison goes further: twelve comparison dimensions in detail, cost modeled across six layers rather than one hourly rate, and seven weighted questions worth scoring before you commit. Vendor-neutral throughout, and built around the workloads you actually run.

    WHO IT’S FOR  

    The AWS Glue vs Databricks guide is written for those of you who have to defend the decision afterwards:

    • Heads and VPs of data, analytics and engineering
    • Enterprise and data architects
    • CIOs, CTOs and CDOs
    • and the finance and procurement stakeholders who own the three-year number.

    It assumes you know what Spark, CDC and a catalog are, and does not explain them.

    INSIDE, YOU’LL GET 

    Five outcomes, each tied to something the guide contains.

    • Compare at the right level. Why a job-level comparison misleads, and how the workload-fit and platform-fit lenses resolve it.
    • Model cost across all six layers. Processing, storage and data movement, surrounding services, engineering, operations and commercial terms — with the denominator that makes the numbers comparable.
    • Score the decision. Seven weighted questions covering workload complexity, platform breadth, AWS concentration, governance, team model, service levels and three-year cost.
    • Settle catalog authority before you run both. The six questions a combined Glue and Databricks architecture has to answer first.
    • Scope a migration honestly. Seven asset classes — code, metadata, orchestration, incremental state, data quality, security and networking, operations — and the work each one implies.

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