Implementation: Streaming CDC, SCD Modeling, Query Guardrails, and Observability

Implementing reliable data movement from OLTP to OLAP starts with change data capture from write ahead logs. Configure CDC connectors to tail the log with at least once delivery guarantees, then ensure idempotency at the warehouse sink using deterministic primary keys derived from source keys plus version or timestamp fields. For example, derive a surrogate key as hash(order_id, updated_at) so that replaying an update event is idempotent: the merge or upsert operation matches the existing row and applies the update once. Validate completeness by reconciling row counts and aggregate sums over sliding time windows (example: compare order count and total revenue between source OLTP and target OLAP every 5 minutes, alert on drift over 0.1 percent). Automate backfills using snapshot plus delta replay from a bookmark, with safeguards to detect and skip already applied ranges to avoid duplication.

Model slowly changing dimensions correctly to maintain historical accuracy. For dimensions that change rarely but matter for time travel (customer region, product category), use Type 2 SCD: add effective_from and effective_to timestamp columns and a surrogate key. When a customer moves from region US to region EU, insert a new dimension row with a new surrogate key, close the old row's effective_to, and update all subsequent fact inserts to reference the new surrogate key. Fact to dimension joins now include a time predicate (fact.order_date BETWEEN dim.effective_from AND dim.effective_to) to correctly attribute historical orders. For high velocity dimensions that change too frequently to version (example: real time inventory levels), denormalize a snapshot into facts at event time or use Type 1 updates and accept that historical queries will reflect current state.

Enforce query guardrails to prevent cost and performance runaway. Implement query cost estimation based on bytes to scan and reject interactive queries exceeding thresholds (example: block queries scanning more than 1 TB without explicit approval). Set per query memory limits and timeouts (example: 10 GB RAM, 5 minute timeout for interactive; 100 GB and 60 minutes for batch). Use adaptive join strategies to handle skew: broadcast small dimension tables to all workers, apply salting (add random prefix to skewed keys to split hot partitions), and configure dynamic partition pruning to push filters into scans. Monitor and enforce admission control: limit concurrent queries per user or team, queue excess queries, and prioritize by Service Level Objective (SLO) tier (interactive over batch).

Observability is critical for both OLTP and OLAP. For OLTP, track p50, p95, p99 transaction latency, lock wait times, deadlock rates per minute, replication lag in seconds, and maintain error budgets (example: 99.9 percent of transactions must complete successfully within 100 ms). Capacity plan with headroom: provision for 2 to 3 times daily peak Queries Per Second (QPS) to handle traffic spikes. For OLAP, track end to end data freshness (event time to query time lag), bytes scanned per query, average and 95th percentile query duration, and cache hit rates. Set Service Level Indicators (SLIs) for freshness (example: operational dashboards must have data fresher than 2 minutes; alert if lag exceeds 5 minutes) and enforce spending alerts (example: notify when daily scan volume exceeds budget by 20 percent).

💡 Key Takeaways

•Implement idempotent CDC sinks using deterministic keys like hash(order_id, updated_at); reconcile counts and sums between OLTP and OLAP every 5 minutes, alert on drift over 0.1 percent to catch duplication or loss

•Use Type 2 slowly changing dimensions with effective_from, effective_to, and surrogate keys for time travel accuracy; fact to dimension joins include time predicates (fact.date BETWEEN dim.effective_from AND effective_to)

•Enforce query guardrails: reject interactive queries scanning over 1 TB without approval, set memory limits (10 GB interactive, 100 GB batch) and timeouts (5 min vs 60 min), use salting and broadcast joins to handle skew

•OLTP observability: track p99 latency, lock waits, deadlocks/min, replication lag; maintain error budgets (99.9 percent under 100 ms); capacity plan for 2 to 3x daily peak QPS to absorb spikes

•OLAP observability: track freshness SLI (event time to query time), bytes scanned, p95 query duration, cache hit rate; alert when operational dashboard freshness exceeds 5 minutes or daily scan cost exceeds budget by 20 percent

📌 Examples

Amazon order CDC: tails write ahead log with at least once delivery, uses upsert with (order_id, version) key; reconciles order count and revenue sum every 5 min, caught 0.3 percent drift indicating connector lag

Google customer dimension: Type 2 SCD with surrogate key; when customer changes region, inserts new row with new key and closes old effective_to; historical revenue queries join on fact.order_date BETWEEN dim.effective_from AND effective_to

Uber analytics: enforces 500 GB scan limit for interactive queries; analyst attempted 2 TB full history scan, query rejected with message to use batch queue or add date filter; saved estimated $400 in compute cost

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