Design and Implement Scalable Data Pipelines

Data Pipeline Architecture helps design scalable, reliable, and cost-aware batch or streaming data workflows. It starts by assessing source systems, data volume, latency needs, and targets, then selects an ETL, ELT, Lambda, Kappa, or Lakehouse pattern. The suggested flow covers sources, ingestion, processing, storage, serving, and observability.

Use it for data pipeline architecture and implementation planning. It is not intended for work unrelated to data pipeline architecture or for a different technical domain. It its proposed success rates and savings as criteria to target, not guarantees; validate them against the system being built.

Inputs include sources, data volume, latency and throughput goals, targets, schema behavior, and operational needs. Batch ingestion guidance covers incremental loading with watermarks, exponential-backoff retries, schema validation, dead-letter queues, and metadata such as extraction time and source. Streaming guidance covers Kafka consumers, transactional offset commits, time windows, error handling, and replay.

For orchestration, Guidance covers Airflow task groups, XCom, SLA monitoring, email alerts, execution dates, and retries; Prefect examples include task caching, parallel submission, artifacts, and configurable retries. Transformation guidance uses dbt staging and marts, deduplication, late-arriving data handling, tests, source freshness, and incremental merge or delete-and-insert strategies. Spark is also named for jobs.

Data quality options include Great Expectations table and column checks, checkpoints, data docs, and failure notifications, alongside dbt schema and custom tests. Storage choices include Delta Lake transactions, merge modes, time travel, compaction, Z-order clustering, and vacuum; Apache Iceberg guidance covers partitioning, merge operations, snapshot isolation, compaction, and snapshot expiration.

The skill names Airflow, Prefect, dbt, Spark, Great Expectations, Delta Lake, Apache Iceberg, Kafka, CloudWatch, Prometheus, Grafana, and SNS. Monitoring should track processed and failed records, data size, runtime, success rates, freshness, and trends. Cost guidance covers partitioning, Parquet file sizing, lifecycle tiers, compute choices, partition pruning, clustering, and predicate pushdown.

An example batch-pipeline validation line from the source is:

df = ingester.validate_and_clean(df, schema)

Data engineers and platform teams can use the material to plan architecture, ingestion, transformations, storage, quality checks, monitoring, and operations. Expected deliverables include architecture documentation, implementation code, configuration, observability, and an operations guide with deployment, rollback, troubleshooting, scaling, cost, and disaster-recovery procedures. Listed success criteria include meeting defined latency and throughput SLAs, passing data-quality checks at over 99%, automatic retries and alerts, schema evolution without downtime, and end-to-end lineage. The source also sets a 30-50% infrastructure cost-reduction target; these are project goals to measure, not promised results.