Trade-off Summary: ETL (Extract-Transform-Load) transforms data before loading into the warehouse, keeping raw data separate and enabling heavy transformation infrastructure. ELT (Extract-Load-Transform) loads raw data first then transforms in-database using warehouse compute — faster to set up and leverages warehouse power but requires careful permission management. Modern cloud data stacks increasingly favor ELT with dbt for transformations, but ETL remains valuable when transformation logic is complex or compliance requires separation.

Trade-off Summary: Batch processing handles large volumes efficiently with lower infrastructure cost per record and natural fault-tolerance through recomputation. Stream processing delivers sub-second latency but requires managing state, exactly-once semantics, and handling backpressure. Lambda architecture (both batch and stream) provides freshness at the cost of doubled complexity — most practical systems pick one based on their freshness requirements and add the other only when genuinely needed.

Trade-off Summary: Star schema denormalizes dimension tables into the fact table for query simplicity and performance — business users can navigate it easily and BI tools generate faster SQL. Snowflake schema normalizes dimensions further into sub-dimensions, reducing storage and handles complex hierarchical relationships better, but increases join complexity. Most data warehouses start with star for speed and normalize to snowflake only when dimension complexity genuinely demands it.

Trade-off Summary: Data lakes provide cheap, scalable object storage for any data format, making them ideal for raw data and ML workloads. However, they lack ACID transactions and reliable concurrent access, leading to “data swamp” problems. Lakehouse architecture (Delta Lake, Iceberg) adds transactional guarantees and reliable query performance while maintaining lake economics — worth the added complexity when you need both raw data storage and performant analytics on the same platform.

Trade-off Summary: Apache Kafka is the industry standard for distributed streaming with mature ecosystem, exactly-once delivery, and maximum flexibility. Cloud services (Kinesis, Pub/Sub, Event Hubs) offer operational simplicity with pay-per-use pricing but create vendor lock-in and limit tuning options. For startups or teams with limited ops capacity, cloud-native is pragmatic. For enterprises with multi-cloud strategy or complex streaming requirements, Kafka provides the control and consistency needed across environments.

Trade-off Summary: dbt brings software engineering practices (version control, testing, documentation, CI/CD) to SQL transformations with analytics engineer accessibility. Stored procedures keep logic in-database but lack versioning, testing, and documentation standards — leading to transformation logic that becomes incomprehensible over time. dbt’s modular approach (models, seeds, macros) makes transformation logic maintainable and auditable, making it the clear choice for teams wanting sustainable transformation architecture.

Trade-off Summary: Data Vault provides auditability, scalability, and decoupled integration at the cost of query complexity — business users find it harder to navigate. Kimball delivers intuitive, performant dimensional models that business users love but requires careful upfront design and becomes fragile when sources change. Data Vault is enterprise-scale for audit-heavy industries; Kimball is analytics-focused for BI teams. Many modern architectures use Data Vault for raw integration layers then denormalize to dimensional models for consumption.