13. Apache Iceberg: Metadata Layer, Hidden Partitioning, Snapshot Isolation & Compaction Optimization • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 13. Apache Iceberg: Metadata Layer, Hidden Partitioning, Snapshot Isolation & Compaction Optimization

50 mins

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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

# 13. Apache Iceberg: Metadata Layer, Hidden Partitioning, Snapshot Isolation & Compaction Optimization

> Data Engineering & Big Data Notes by Rishabh - Topic 13

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### 1. Iceberg Metadata Hierarchy (The Tree Architecture 🌲)

```
[ Iceberg Catalog (Glue / Nessie / REST) ] ➔ Points to Latest Metadata File
                      ⬇
[ Table Metadata (v3.metadata.json) ] ➔ Snapshots History & Schema
                      ⬇
[ Manifest List (snap-101.avro) ] ➔ List of Manifest Files
                      ⬇
[ Manifest File (manifest-a.avro) ] ➔ Exact Parquet Data Files + Min/Max Column Stats!
                      ⬇
[ Physical Parquet Data Files in S3 ]
```

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### 2. Hidden Partitioning (Eliminating Partition Drift 👑)

Traditional Hive me queries me partition column exact match karna padta tha (`WHERE year=2024 AND month=08`).  
**Iceberg Hidden Partitioning:** User query `WHERE event_time >= '2026-08-21'` likhta hai aur Iceberg internally partition pruning execute karta hai bina physical folder path structure par depend kiye!

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

1. **Snapshot Isolation Iceberg me concurrent reads/writes ko kaise protect karti hai?** -> Readers hamesha consistent committed snapshot view karte hain jabki background me writer naya snapshot manifest prepare kar raha hota hai.
2. **Small Files Problem ko Iceberg Compaction kaise solve karta hai?** -> `rewrite_data_files` procedure small 5MB Parquet chunks ko 512MB optimized blocks me merge karta hai.
3. **Iceberg Catalog REST API standard kyu ban raha hai?** -> Cross-engine compatibility (Snowflake, Spark, Trino, DuckDB sab single shared Iceberg catalog access kar sakte hain).

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**Next Topic:** 14. The Medallion Architecture: Bronze (Raw Ingestion) -> Silver (Enriched) -> Gold (Business Aggregates)

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