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Query old and new rows and confirm existing measures and keys remain valid. This validate action addresses a different case (dimension evolution), where a destructive table recreation would invalidate downstream consumers. Additive schema evolution can preserve contracts when null defaults are acceptable. In the stated problem, malformed source events contaminate metrics without an intermediate quality boundary; this choice would not establish the required medallion bronze silver gold decision.

Reconcile sample reports and verify dimensions join without duplication. This validate action addresses a different case (star schema analytics), where a denormalized mixed-grain table produces redundant attributes and expensive corrections. A star schema links a clear-grain fact to reusable dimensions for consistent analytics. In the stated problem, malformed source events contaminate metrics without an intermediate quality boundary; this choice would not establish the required medallion bronze silver gold decision.

Reconcile gold totals and dashboard response time against direct fact queries. This validate action addresses a different case (aggregate gold model), where interactive queries scan billions of detail rows for identical totals. A summarized gold table can serve a stable reporting grain efficiently. In the stated problem, malformed source events contaminate metrics without an intermediate quality boundary; this choice would not establish the required medallion bronze silver gold decision.

Make writes idempotent before enabling retries. This design action addresses a different case (job retry semantics), where the downstream write repeats non-idempotent side effects on retry. Retries re-execute task logic; application code must protect repeated effects. In the stated problem, dependent tasks sometimes run before the bronze load finishes; this choice would not establish the required job task dag decision.

Branch downstream tasks using an If/else condition before divergent paths. This design action addresses a different case (conditional task branches), where both the normal and repair tasks run despite a branching requirement. Lakeflow Jobs condition tasks can route downstream execution based on a result. In the stated problem, dependent tasks sometimes run before the bronze load finishes; this choice would not establish the required job task dag decision.

Defer expensive materialization until the required action. This design action addresses a different case (spark lazy evaluation), where engineers assume an intermediate filter executed before any action. Spark transformations are lazy until an action triggers a job. In the stated problem, dependent tasks sometimes run before the bronze load finishes; this choice would not establish the required job task dag decision.

Inspect effective grants on source, target and parent containers for the run-as principal. This diagnose action addresses a different case (job run permission debug), where the run-as principal lacks MODIFY on the target table. Unity Catalog authorization applies to the job execution identity. In the stated problem, the scheduler launches a full rebuild instead of rerunning the affected date; this choice would not establish the required override run parameters decision.

Inspect CI stages for tests, bundle validation and approvals. This diagnose action addresses a different case (ci quality gates), where the deployment pipeline only checks YAML syntax. CI must run transformation tests and controlled integration checks before deployment. In the stated problem, the scheduler launches a full rebuild instead of rerunning the affected date; this choice would not establish the required override run parameters decision.

Inspect CLI target resolution and workspace host for the command. This diagnose action addresses a different case (target-safe deployment), where the wrong workspace is selected because target is implicit. Explicit bundle target selection prevents accidental environment deployment. In the stated problem, the scheduler launches a full rebuild instead of rerunning the affected date; this choice would not establish the required override run parameters decision.

Package shared transformations into importable modules with explicit dependencies. This design action addresses a different case (python wheel packaging), where a refactor broke a notebook-relative import in the deployed job. A properly packaged versioned wheel gives Python code stable import paths outside notebooks. In the stated problem, development job arguments were deployed into the production target; this choice would not establish the required bundle target overrides decision.

Move resource definitions into bundle YAML with review gates. This design action addresses a different case (databricks cli resource updates), where production configuration drifts from repository source definitions. Declarative deployment describes resources as reviewed version-controlled artifacts. In the stated problem, development job arguments were deployed into the production target; this choice would not establish the required bundle target overrides decision.

Refactor business rules as pure DataFrame transform functions. This design action addresses a different case (transform composability), where business logic depends on notebook state and widgets. DataFrame.transform supports composable DataFrame-to-DataFrame functions. In the stated problem, development job arguments were deployed into the production target; this choice would not establish the required bundle target overrides decision.

Deploy the wheel task with its documented entrypoint and parameters. This implement action addresses a different case (python task entrypoints), where the task exits before setting up its argument parser. A standard Python entrypoint and explicit arguments avoid notebook runtime assumptions. In the stated problem, production configuration drifts from repository source definitions; this choice would not establish the required databricks cli resource updates decision.

Move task source under version control and declare jobs or pipelines in bundle resources. This implement action addresses a different case (bundle repository layout), where source files and deployment definitions are coupled to one developer workspace. A versioned Declarative Automation Bundle defines code and resource configurations separately from the target workspace. In the stated problem, production configuration drifts from repository source definitions; this choice would not establish the required databricks cli resource updates decision.

Set explicit target overrides and deploy using the approved production target. This implement action addresses a different case (bundle target overrides), where development job arguments were deployed into the production target. Bundle targets can override environment-specific variables and resource settings. In the stated problem, production configuration drifts from repository source definitions; this choice would not establish the required databricks cli resource updates decision.

Monitor newly arriving file-schema mismatches. This monitor action addresses a different case (parquet schema mismatch), where a merge step fails after a producer changes an integer field to string. Parquet schema compatibility matters even when column names are unchanged. In the stated problem, restarting with a fresh checkpoint replays previously ingested messages; this choice would not establish the required kafka offset durability decision.

Monitor schema-registry incompatibility and decode failures. This monitor action addresses a different case (avro schema evolution), where an older consumer cannot deserialize records from a newer producer schema. Avro writer-reader schema compatibility must be maintained for evolution. In the stated problem, restarting with a fresh checkpoint replays previously ingested messages; this choice would not establish the required kafka offset durability decision.

Monitor unexpected overwrite or delete operations on bronze. This monitor action addresses a different case (append-only delta bronze), where a nightly overwrite accidentally removes historical audit events. Append-only ingestion keeps previous raw records while adding new batches. In the stated problem, restarting with a fresh checkpoint replays previously ingested messages; this choice would not establish the required kafka offset durability decision.

Use lineage alongside data-quality checks to trace source-to-output transformations. This design action addresses a different case (lineage tracking), where investigators cannot tell which upstream transformation produced it. Unity Catalog lineage records relationships between governed data assets. In the stated problem, naming conventions alone are inconsistent across teams; this choice would not establish the required governed tags decision.

Permit metadata discovery while requiring separate approvals for SELECT. This design action addresses a different case (browse metadata privilege), where teams grant SELECT merely to expose object listings. BROWSE enables controlled metadata discovery without granting data read access. In the stated problem, naming conventions alone are inconsistent across teams; this choice would not establish the required governed tags decision.

Choose approved managed storage locations for governed tables. This design action addresses a different case (managed storage ownership), where the data platform cannot apply consistent lifecycle governance. Managed locations centralize storage ownership for Unity Catalog managed assets. In the stated problem, naming conventions alone are inconsistent across teams; this choice would not establish the required governed tags decision.

Monitor overlapping intervals and multiple current versions. This monitor action addresses a different case (scd type 2 effective dates), where a single current tier overwrites history in the dimension. SCD Type 2 stores effective start/end intervals for distinct historical dimension versions. In the stated problem, joining facts directly to the membership table multiplies totals; this choice would not establish the required many-to-many bridge decision.

Monitor aggregate freshness and reconciliation differences. This monitor action addresses a different case (aggregate gold model), where interactive queries scan billions of detail rows for identical totals. A summarized gold table can serve a stable reporting grain efficiently. In the stated problem, joining facts directly to the membership table multiplies totals; this choice would not establish the required many-to-many bridge decision.

Monitor orphaned foreign keys and repeated business identifiers. This monitor action addresses a different case (surrogate dimension keys), where joining facts directly on mutable business IDs links history incorrectly. Stable surrogate keys decouple dimensional identity from changing source identifiers. In the stated problem, joining facts directly to the membership table multiplies totals; this choice would not establish the required many-to-many bridge decision.

Deploy the previous approved bundle revision with correct target. This implement action addresses a different case (rollback deployment), where deployers have no record of the previous working configuration. Versioned bundle releases enable controlled rollback to a known commit. In the stated problem, re-running a huge source in development is slow and risky; this choice would not establish the required pipeline debug sampling decision.

Deploy using an explicit reviewed production target. This implement action addresses a different case (target-safe deployment), where the wrong workspace is selected because target is implicit. Explicit bundle target selection prevents accidental environment deployment. In the stated problem, re-running a huge source in development is slow and risky; this choice would not establish the required pipeline debug sampling decision.

Commit and deploy the approved setting through the bundle. This implement action addresses a different case (unmanaged resource drift), where subsequent CI redeployment unexpectedly overwrites it. Declarative source of truth should include intentional job-resource changes. In the stated problem, re-running a huge source in development is slow and risky; this choice would not establish the required pipeline debug sampling decision.

Inspect the streaming table definition and expectation violation counts. This diagnose action addresses a different case (expectation drop action), where invalid rows currently reach downstream aggregates. A Lakeflow expectation configured to drop invalid rows filters violations from target output. In the stated problem, implicit type coercion risks invalid numeric values being stored; this choice would not establish the required silver schema enforcement decision.

Inspect malformed timestamps and pipeline data-quality rules. This diagnose action addresses a different case (quarantine bad records), where dropping invalid rows silently hides upstream quality failures. Quarantine preserves invalid events separately while allowing valid records through. In the stated problem, implicit type coercion risks invalid numeric values being stored; this choice would not establish the required silver schema enforcement decision.

Inspect null key prevalence and join type in the physical plan. This diagnose action addresses a different case (join with nullable keys), where rows with null external IDs disappear from an inner join. SQL null equality and join type determine whether unmatched rows survive. In the stated problem, implicit type coercion risks invalid numeric values being stored; this choice would not establish the required silver schema enforcement decision.

Confirm stewards can manage the table while readers retain intended access. This validate action addresses a different case (ownership reassignment), where new data stewards cannot maintain its grants. Unity Catalog ownership should be assigned to a maintained principal. In the stated problem, the data platform cannot apply consistent lifecycle governance; this choice would not establish the required managed storage ownership decision.

Confirm users can discover metadata but cannot read restricted table rows. This validate action addresses a different case (browse metadata privilege), where teams grant SELECT merely to expose object listings. BROWSE enables controlled metadata discovery without granting data read access. In the stated problem, the data platform cannot apply consistent lifecycle governance; this choice would not establish the required managed storage ownership decision.

Create a test table and confirm approved analysts can query it. This validate action addresses a different case (schema-level grant inheritance), where analysts must repeatedly request identical SELECT grants for each table. Unity Catalog schema-level privileges can apply to existing and future child objects. In the stated problem, the data platform cannot apply consistent lifecycle governance; this choice would not establish the required managed storage ownership decision.