18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine

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

# 18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine

> Data Engineering & Big Data Notes by Rishabh - Topic 18

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### 1. The Cloud Warehouse Giants Compared 🏢

*   **Snowflake (Multi-Cluster Shared Data Architecture):**
    *   **Storage Layer:** Centralized S3/Azure Blob (Micro-partitions).
    *   **Compute Layer:** Independent Virtual Warehouses (T-Shirt sizes XS, S, M, L, XL). Zero contention between ETL and BI queries!
*   **Google BigQuery (Serverless Dremel Execution Tree):**
    *   **Storage Layer:** Capacitor columnar storage on Colossus filesystem.
    *   **Compute:** Dynamic serverless allocation of thousands of query Execution Slots on Jupiter multi-terabit network!

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### 2. Master Comparison Matrix

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

1. **Snowflake Micro-Partitions kya hoti hain?** -> Immutable 50MB-500MB compressed columnar files jinke metadata me har column ke min/max values store hote hain (Enables massive Partition Pruning).
2. **BigQuery Slot Contention kya hoti hai?** -> Complex multi-join queries ke dauran allocated compute slots exhaust ho jana; Reservations aur Bi-Engine memory cache use karke resolve karna.
3. **Zero-Copy Cloning in Snowflake kaise kaam karta hai?** -> Data copy kiye bina sirf metadata pointers clone karta hai; storage cost zero hoti hai jab tak cloned table me naye writes na hon.

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**Next Topic:** 19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing

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