Databricks Architecture: Where Your Code Runs and Who Holds the Data
Databricks splits into a control plane it runs and a compute plane that may be yours. See what that means for isolation, governance, and the shape of the bill.
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Enterprise Scale: One Platform, Workloads That Disagree
Three groups share one platform and each slows the others down. See what isolation separates, why concurrency is not size, and when to share instead of copy.
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Storage, Compute, and Metadata: What Each Layer Actually Owns
Splitting a warehouse replaces one product with five choices. See what object storage cannot do, how a table format fills the gap, and what copies cost.
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Data Platform Layers: What Each One Promises and Who Owns It
Layer names describe intent, not guarantees. See what each layer should promise its readers, what it costs to keep, and who is accountable when it breaks.
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Analytical Engines: Where the Speed Actually Comes From
Two engines run the same SQL and one finishes far sooner. See which layer supplies which part of that gap, and where each one stops helping.
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Warehouse, Lake, and Lakehouse: What Each Was Built to Solve
Three architectures, three different assumptions about schema, cost, and who the user is. See what each solved, what it deferred, and why the names now mislead.