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2908 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 areas2908 Architecture Fundamentals91 Distributed Systems101 Data Architecture90 Cloud Architecture87 Networking86 API & Integration Architecture87 Reliability & Resilience99 Observability92 Performance & Capacity Engineering100 Security Architecture95 Cost Architecture & FinOps102 Business Architecture103 Architecture Communication102 Enterprise Architecture100 Legacy Modernization92 AI-Era Architecture96 Software Architecture & Engineering93 Architecture Patterns94 Architecture Decision-Making101 The Architect's Meta-Skills102 Delivery & Release Engineering103 Platform Engineering & Developer Experience102 Testing & Quality Architecture102 Data Platform Architecture98 Streaming & Real-Time Data103 Data Governance & Semantics91 Frontend & Experience Architecture101 Edge, Mobile & IoT98 Regulatory & Data Protection Architecture99 Assurance, Audit & Model Risk98
Difficulty Any beginner intermediate advanced

103 questions in Streaming & Real-Time Data.

  1. Feature Freshness advanced

    ByteDance's Monolith paper (RecSys workshop 2022) describes training recommendation models on user feedback as it arrives rather than in nightly batches, and states that system reliability was deliberately traded for real-time learning. What does that trade actually look like in the pipeline, and when is a nightly batch the better engineering decision?

    2 min answer bytedanceonline trainingembeddingsfreshness
  2. Feature Freshness advanced

    Delivery ETAs are computed by a model using live traffic and restaurant load. The model's features are computed in batch overnight. What is wrong?

    2 min answer doordashmlfeaturesskew
  3. Feature Freshness advanced

    How should feature freshness requirements be decided, and what does getting them wrong cost?

    2 min answer feature-freshnesslatencycostskew
  4. Kappa vs Lambda advanced

    A finance-reporting platform runs a nightly batch job and a streaming job that compute the same daily revenue figure, and the batch number is the one the controller signs. You are asked to retire the batch path. Sequence it so every step is reversible and say where the point of no return is.

    3 min answer lambdareconciliationauditparallel run
  5. Kappa vs Lambda advanced

    A platform maintains separate batch and streaming implementations of the same logic. What does that cost, and what are the alternatives?

    2 min answer lambdakappaduplicationskew
  6. Kappa vs Lambda advanced

    A publisher keeps every piece of published content in one ordered Kafka log and rebuilds every downstream store by replaying it, as The New York Times described in 2017. What does that buy, what does the ordering requirement cost, and where would copying it be a mistake?

    3 min answer new york timeskafkalogreplay
  7. Kappa vs Lambda advanced

    A team replays 60 days of order events through the current job to correct a tax calculation. Side effects are already handled - the replay writes to a shadow table and emits no notifications. The job enriches each order by calling the pricing service for the item's list price. What does the shadow table contain when the replay finishes?

    3 min answer replaydeterminismenrichmenttemporal-join
  8. Kappa vs Lambda advanced

    Walk me through how you would decide whether a new analytics platform needs a batch processing path at all. The team already runs a stream processor, and nobody has asked for historical reprocessing yet.

    3 min answer kappareplayretentionidempotency
  9. Partition Keys & Ordering advanced

    A location pipeline must choose a partition key. What are the candidates, and what does each optimise for?

    2 min answer olapartitioningorderingskew
  10. Partition Keys & Ordering advanced

    A marketplace in the mould of Flipkart has an order-events topic with 24 partitions keyed by buyer id. One consumer per partition is now the throughput ceiling and the team wants 96 partitions. Per-buyer ordering is a hard requirement and six downstream jobs rebuild their state by replaying the topic. Sequence the change so each step is reversible and say where the point of no return is.

    3 min answer kafkapartitioningorderingmigration
  11. Partition Keys & Ordering advanced

    A stream requires ordering per user but not globally. How should partitioning be designed, and what breaks it?

    2 min answer partitioningorderinghot-keysrebalancing
  12. Partition Keys & Ordering intermediate

    A team must choose partition keys for an event stream. What does the choice determine, and what goes wrong when it is chosen carelessly?

    2 min answer partitioningorderinghot-keyparallelism