Snowflake: From Foundations to Advanced Engineering

Welcome to The ETL Dispatch — exploring data engineering, dev workflows, and the chaos of self-hosting, one query at a time.

This guided series walks through Snowflake’s core concepts, practical use cases, and advanced features — designed for data engineers, analysts, and architects who want to go beyond the basics.

Bookmark this page — each section will be updated with links as posts go live.

📘 Part 1: Foundations & Architecture

Post 1: What Makes Snowflake Different
A high-level look at Snowflake’s unique architecture: separation of storage and compute, multi-cluster warehouses, and why it matters.
Post 2: Getting Started with Warehouses
How warehouses work, scaling up vs. out, and tips for auto-suspend/resume to keep costs low.
Post 3: Stages & Data Loading 101
The basics of internal vs. external stages and loading data with COPY INTO.

🗂 Part 2: Working with Data

Post 4: Loading at Scale — Snowpipe & Beyond
Continuous ingestion, automation, and when to use Snowpipe.
Post 5: Handling Semi-Structured Data
How to work with JSON/VARIANT and flatten nested data structures.
Post 6: Modeling for Analytics
Dimensional modeling in Snowflake: fact/dim design, schema layouts, and best practices.

⚡ Part 3: Performance & Optimization

Post 7: Micro-Partitions Under the Hood
How Snowflake organizes data and why pruning is your best friend.
Post 8: Clustering Keys Explained
When (and when not) to define clustering keys.
Post 9: Query Performance Tuning
Layers of caching, query profiling, and tuning tips for common workloads.

🔒 Part 4: Governance & Reliability

Post 10: Role-Based Access Control (RBAC)
Building a secure and scalable permission model.
Post 11: Time Travel & Fail-Safe
How Snowflake handles data recovery, auditing, and compliance requirements.
Post 12: Resource Monitors & Cost Control
Keeping your cloud data warehouse spend under control.

🚀 Part 5: Advanced Features & Integrations

Post 13: Zero-Copy Cloning in Action
Spinning up dev/test environments without extra storage.
Post 14: Streams & Tasks
Building ELT pipelines natively inside Snowflake.
Post 15: Snowpark for Data Engineers
Extending ELT with Python/Scala for complex transformations.
Post 16: Data Sharing & Marketplace
Sharing data across orgs and tapping into Snowflake’s ecosystem.

🛠 Part 6: Real-World Applications

Post 17: CI/CD for Snowflake
Using dbt + Azure DevOps/GitHub Actions to version-control and deploy your warehouse.
Post 18: End-to-End Project Walkthrough
From raw ingestion to curated analytics: building a small pipeline project with Snowflake.
Post 19: Hybrid Architecture
How Snowflake fits into a broader data platform with ADF, Synapse, or Power BI.

👉 Stay tuned — posts will go live here as the series unfolds.