16. Production Airflow Best Practices: Idempotent Tasks, Dynamic DAG Generation, Backfilling & SLA Alerts • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 16. Production Airflow Best Practices: Idempotent Tasks, Dynamic DAG Generation, Backfilling & SLA Alerts

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Course Syllabus

Module 1: Modern Data Engineering Architecture & The Data Stack

01. The Modern Data Stack: OLTP vs OLAP, ETL vs ELT & Batch vs Stream Processing Architecture 02. Dimensional Modeling: Kimball Methodology, Star Schema, Snowflake Schema & Fact vs Dimension Tables 03. Columnar Storage Formats: Apache Parquet, ORC, Snappy Compression & Dictionary Encoding Internals

Module 2: Distributed Processing with Apache Spark & PySpark

04. Apache Spark Core Architecture: Driver, Executors, RDDs, DAG Scheduler & Spark Catalyst Optimizer 05. High-Performance PySpark DataFrames: Transformations (Narrow vs Wide), Actions & Lazy Evaluation 06. Spark Optimization Masterclass: Partitioning, Coalesce vs Repartition, Broadcast Joins & Skew Data Handling 07. Spark Structured Streaming: Event-Time Processing, Watermarking & Windowed Aggregations

Module 3: Real-Time Event Streaming with Apache Kafka

08. Apache Kafka Architecture: Brokers, Topics, Partitions, Replicas, ISR (In-Sync Replicas) & KRaft Consensus 09. Kafka Producers & Consumers: Partitioning Keys, Consumer Groups, Rebalancing & Offset Commit Management 10. Schema Evolution & Governance: Confluent Schema Registry with Apache Avro and Protobuf Schemas

Module 4: Modern Data Lakehouse Architecture (Delta Lake, Apache Iceberg)

11. Data Lakehouse Architecture: Evolution from Data Lakes (S3/HDFS) to ACID Lakehouses 12. Delta Lake Architecture: Transaction Log (_delta_log), Time Travel, Schema Enforcement & ACID Guarantees 13. Apache Iceberg: Metadata Layer, Hidden Partitioning, Snapshot Isolation & Compaction Optimization 14. The Medallion Architecture: Bronze (Raw Ingestion) -> Silver (Enriched) -> Gold (Business Aggregates)

Module 5: Workflow Orchestration with Apache Airflow & Dagster

15. Apache Airflow Core: DAGs, Operators, Sensors, Task Execution Engine & Celery/Kubernetes Executors 16. Production Airflow Best Practices: Idempotent Tasks, Dynamic DAG Generation, Backfilling & SLA Alerts 17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety

Module 6: Cloud Data Warehousing (Snowflake / BigQuery) & dbt Transformations

18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine 19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing 20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard

Interactive Lesson Studio

# 16. Production Airflow Best Practices: Idempotent Tasks, Dynamic DAG Generation, Backfilling & SLA Alerts

> Data Engineering & Big Data Notes by Rishabh - Topic 16

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### 1. Golden Rules of Production Airflow 👑

1. **Strict Idempotency:** Task ko chahe 1 baar chalao ya 10 baar retry karo, output exact same hona chahiye (Use partition overwrite instead of append!).
2. **Logical Execution Date (`logical_date` / `data_interval_start`):** Hamesha execution date parameters use karo, kabhi hardcoded `datetime.now()` use mat karo!
3. **Dynamic DAG Generation:** Single template Python script se JSON configuration table ke basis par hazaron table ETL DAGs generate karna.

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### 2. Backfilling Historical Data

```bash
# Backfill entire month of January 2026:
airflow dags backfill \
  --start-date 2026-01-01 \
  --end-date 2026-01-31 \
  enterprise_etl_pipeline
```

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### 3. Interview Ke 3 Sawal Is Topic Se

1. **Airflow me `catchup=False` kyu lagaya jata hai?** -> Agar DAG ka `start_date` 1 saal purana ho, toh scheduler saare 365 purane days ke runs instantly trigger na kare.
2. **Top-level code in DAG file scheduler performance ko kaise affect karta hai?** -> Scheduler har 30 seconds me saari DAG files parse karta hai; agar top-level par heavy database calls ya API requests hon toh scheduler freeze ho jata hai.
3. **SLA Miss Callback Airflow me kaise alert karti hai?** -> `sla_miss_callback` configure karke PagerDuty / Slack par notification bhejti hai agar task scheduled execution window me complete na ho.

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**Next Topic:** 17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety

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