Design a pipeline capable of processing 1TB of data per day.

Section 1 — The Context (The 'Why')
Processing 1TB/day requires partitioning, parallelism, and cost optimization. The primary challenge is avoiding hotspots, managing storage growth, and completing daily batches within SLA. A single large partition creates a straggler that blocks the entire job.

Section 2 — The Diagram

[S3 Sources] --> [Glue/Spark] --> [Partitioned Output]
     |                    |              |
     v                    v              v
[Incremental]    [Dynamic Part]   [S3/Delta]
[Checkpoint]    [Skew Handling]

Section 3 — Component Logic
Data in S3; Glue or Spark reads with partition pruning. Dynamic partitioning by date, region. Skew mitigation: salting for hot keys. Checkpointing enables resume. Idempotency: overwrite by partition. TTL and lifecycle policies tier cold data to Glacier. In production, monitor consumer lag, checkpoint success rate, and sink write latency as primary SLOs. Partitioning strategies should align with query patterns; bucketing within partitions mitigates join skew. TTL policies on raw and intermediate data control storage cost while preserving replay capability for debugging and backfill. Data skew mitigation via salting or secondary hashing prevents single partitions from becoming bottlenecks. Exactly-once semantics require transactional commits at the sink; at-least-once delivery demands idempotent write logic to avoid duplicates. Fan-out patterns allow one source topic to feed multiple downstream consumers without re-ingestion. Backpressure handling ensures that slow processors do not cause unbounded buffer growth; Kafka consumer lag is a key metric. Schema evolution should follow additive-only rules where possible to avoid breaking consumer compatibility. The CAP trade-off should be documented per component: analytics typically favors AP, while financial reconciliation requires CP. Blast radius from component failure is bounded by replication and checkpointing; design for graceful degradation during partial outages. Cost optimization: use Spot instances for batch workloads and tier cold data to lower storage classes. Dead-letter queues preserve failed records for replay rather than dropping them.

Section 4 — The Trade-offs (The 'Senior' part)

Section 5 — Pro-Tip