1-Introduction – Why Data Pipelines Matter
Define pipelines as the backbone of modern data-driven organizations.
Show real-world context: e.g., Netflix, Uber, or Swvl using real-time data pipelines.
State the problem: raw data is messy → pipelines transform it into usable insights.
2. Architecture of a Robust Data Pipeline
Batch vs. Streaming Pipelines: Use cases for each.
Layered Approach:
- Ingestion: Kafka, Flume, NiFi.
- Processing: Spark Structured Streaming, Flink, Beam.
- Storage: Data Lakehouse (Delta Lake, Iceberg, Hudi).
- Serving Layer: BI Tools, APIs, ML models.
- Orchestration: Airflow, Dagster, Prefect.
3. Optimization Techniques
Data Modeling & Formats
- Partitioning and bucketing strategies.
- Why Parquet/ORC > CSV/JSON for analytics.
Performance Tuning
- Spark optimizations: Catalyst optimizer, broadcast joins, caching.
- Flink optimizations: windowing strategies, checkpointing.
Scalability & Cost
- Cloud-native elasticity (e.g., autoscaling in AWS EMR/Dataproc).
- Storage tiering (hot, warm, cold).
Data Quality
Implementing Great Expectations or Deequ for validation.
4. Monitoring Pipelines Like a Pro
What to Monitor:
Latency, throughput, error rates, SLA compliance.
Observability Stack:
Logs → ELK Stack / Splunk.
Metrics → Prometheus + Grafana.
Tracing → OpenTelemetry.
Alerting & Automation:
PagerDuty, Slack/Teams integrations.
Self-healing pipelines (retry policies, failover strategies).
Case Study Example: Airflow DAG with monitoring hooks + Grafana dashboard.
5. Real-World Example: Mini Case Study
Scenario:
Retail company processing real-time sales transactions.
Solution: Kafka → Spark Structured Streaming → Delta Lake → Power BI.
Optimizations: partition by date & store ID, use Parquet with Snappy compression.
Monitoring: Grafana dashboard tracking processing latency and throughput.
6. Conclusion & Best Practices
Pipelines should be: scalable, reliable, observable, and cost-efficient.
Key takeaway: A pipeline without monitoring is a black box; optimization without observability is blind.