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2777 questions of the kind that actually get asked — in interviews, in architecture review boards, and by the person who has to run the thing at 3 AM. Every answer states the trade-off rather than the slogan, and says when the obvious choice is the wrong one.

All areas2777 Architecture Fundamentals81 Distributed Systems101 Data Architecture90 Cloud Architecture87 Networking86 API & Integration Architecture87 Reliability & Resilience99 Observability92 Performance & Capacity Engineering90 Security Architecture95 Cost Architecture & FinOps92 Business Architecture93 Architecture Communication91 Enterprise Architecture100 Legacy Modernization92 AI-Era Architecture96 Software Architecture & Engineering93 Architecture Patterns84 Architecture Decision-Making91 The Architect's Meta-Skills92 Delivery & Release Engineering93 Platform Engineering & Developer Experience92 Testing & Quality Architecture102 Data Platform Architecture98 Streaming & Real-Time Data93 Data Governance & Semantics91 Frontend & Experience Architecture91 Edge, Mobile & IoT88 Regulatory & Data Protection Architecture99 Assurance, Audit & Model Risk98
Difficulty Any beginner intermediate advanced

98 questions in Data Platform Architecture.

  1. Medallion Architecture beginner

    A team proposes bronze, silver and gold layers for a pipeline that ingests one CSV daily and produces one report. Is that right?

    2 min answer pragmatismlayeringdesign
  2. Medallion Architecture intermediate

    What does a layered bronze-silver-gold structure actually buy, and where does it become ceremony?

    2 min answer medallionlayersreprocessingquality
  3. Open Table Formats intermediate Multiple choice

    A team has 90 TB of Parquet in Hive-style date directories and wants Iceberg tables with no downtime for the 200 dashboards reading them. Which sequence is right?

    3 min answer icebergmigrationhivemetadata
  4. Open Table Formats advanced

    A team is choosing between Iceberg, Delta and Hudi for a new lakehouse. How would you approach the decision?

    1 min answer data-platformtable-formatlock-in
  5. Open Table Formats advanced

    An Iceberg lakehouse on object storage is healthy. Then the storage service starts serving reads at about four times its usual latency with no errors at all. Nothing is down. Walk through what happens over the next ten minutes and what stops it.

    3 min answer icebergmetadataslow dependencyretry storm
  6. Open Table Formats advanced

    An organisation is consolidating a warehouse and a lake onto open table formats. What guarantees do ACID-on-object-storage tables provide, where do small files and compaction bite, and how are streaming and batch writers coordinated?

    3 min answer databricksdelta-lakeicebergcompaction
  7. Open Table Formats advanced Multiple choice

    Which requirement most often forces an open table format over plain files in object storage?

    2 min answer table-formatsdeletestransactionsschema-evolution
  8. Open Table Formats beginner

    Your team already writes compressed Parquet files to object storage and queries them with a SQL engine. An engineer proposes moving the same files to Iceberg. The bytes on disk barely change, so what does the table format actually add?

    2 min answer icebergparquettable-formatmetadata
  9. Reverse ETL intermediate

    A company wants warehouse data pushed back into operational systems. What are the design considerations, and what should not go through this path?

    2 min answer udaanreverse-etloperationalfreshness
  10. Reverse ETL intermediate

    A platform pushes warehouse-derived data back into operational systems. What are the risks, and what must be designed?

    2 min answer reverse-etloperational-datafreshnessidempotency
  11. Reverse ETL advanced

    A warehouse table now feeds the CRM through reverse ETL. It breaks, and the analytics team and the CRM team each say it is the other's problem. How do you resolve it?

    1 min answer data-platformreverse-etlownershipoperations
  12. Reverse ETL advanced

    At 01:50 an upstream system renames a column and the nightly customer model's join silently yields 4,000 rows instead of 1.2 million. Every test on that model is a not-null check on columns that survived, so the model passes. At 02:30 reverse ETL syncs it to the CRM. By 09:15 sales reports that lifecycle stage and account owner are blank on more than a million accounts and overnight campaigns fired against the wrong segment. Nothing errored. What failed, and which design decision made it possible?

    3 min answer reverse etlfull syncblast radiusvolume tests