11. Data Lakehouse Architecture: Evolution from Data Lakes (S3/HDFS) to ACID Lakehouses • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 11. Data Lakehouse Architecture: Evolution from Data Lakes (S3/HDFS) to ACID Lakehouses

45 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

# 11. Data Lakehouse Architecture: Evolution from Data Lakes (S3/HDFS) to ACID Lakehouses

> Data Engineering & Big Data Notes by Rishabh - Topic 11

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### 1. The Architectural Evolution 🏗️

```
[ Data Warehouse ] (Fast SQL, ACID, Structured only, Expensive storage)
        ⬇
[ Data Lake ]      (Cheap S3/HDFS Object Storage, Unstructured, but NO ACID, Data Swamps!)
        ⬇
[ DATA LAKEHOUSE ] (Best of Both Worlds: Cheap Cloud Object Storage + ACID Transactions & Time Travel!)
```

---

### 2. The 3 Modern Open Lakehouse Table Formats

1. **Delta Lake (Linux Foundation / Databricks 👑):** JSON Transaction Log + Parquet files.
2. **Apache Iceberg (Netflix / Apple Standard):** Metadata Tree (Manifests + Snapshots) + Parquet/ORC.
3. **Apache Hudi (Uber):** Incremental upserts & Change Data Capture (CDC).

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

1. **Data Lake me "Data Swamp" problem kyu hoti hai?** -> Bina schema validation aur catalog governance ke Parquet/CSV files dump hone se duplicate, corrupted aur unsearchable data accumulate ho jata hai.
2. **Lakehouse direct Cloud Object Storage (S3/GCS/R2) par ACID transactions kaise provide karta hai?** -> Metadata transaction log (`_delta_log` / Iceberg snapshots) use karke optimistic concurrency control enforce karna.
3. **Copy-on-Write (CoW) vs Merge-on-Read (MoR) Lakehouse formats me kya tradeoff hai?** -> CoW update par modified Parquet file rewrite karta hai (Fast reads, slower writes); MoR delta log files me changes append karta hai aur read time par merge karta hai (Fast writes for CDC).

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**Next Topic:** 12. Delta Lake Architecture: Transaction Log (_delta_log), Time Travel, Schema Enforcement & ACID Guarantees

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