Book
Databricks & Snowflake
From first principles to production: the problem both solve, columnar storage, how Delta and Iceberg turn files into tables, how each engine executes a query, each platform in practice, performance, cost control, governance, and choosing between them.
Built because 66% of enterprise data postings name one of these by name and 30% name both. Assumes no prior knowledge of either.
CHAPTER 1
The problem both are solving
Warehouse, lake, lakehouse — the three architectures, why each appeared, and what was actually wrong with the previous one.
CHAPTER 2
Files, columns and compression
Why analytics runs on Parquet. The single idea that makes everything above it possible.
CHAPTER 3
Delta and Iceberg — how files become a table
The mechanism that makes a lakehouse possible. Understand this and both products stop being magic.
CHAPTER 4
How a query actually executes
Spark's model and Snowflake's, side by side. The mental model that makes performance work intelligible.
CHAPTER 5
Databricks in practice
Workspaces, clusters, jobs, Delta Live Tables and Unity Catalog — what each is and when it bites.
CHAPTER 6
Snowflake in practice
Virtual warehouses, micro-partitions, clustering and caching — and the three ways people waste money.
CHAPTER 7
Making queries fast on each
The specific moves that matter, and the diagnostic that tells you which one you need.
CHAPTER 8
Cost control, which is the skill people get hired for
Half of data-platform postings name cost. This chapter is the practical part.
CHAPTER 9
Governance and security on both
Named in 68% of postings. What each platform gives you and where the gaps are.
CHAPTER 10
Choosing, migrating, and the interview
How to answer the question you will be asked, and what to actually learn.
CHAPTER 11
Table format internals: the log, the tree, and the timeline
What Delta, Iceberg and Hudi actually write to storage — and why the three designs behave differently under concurrency and at scale.
CHAPTER 12
Query execution: Photon, micro-partitions and the profile
What the engines actually do, and the three questions that diagnose any slow query on either platform.
CHAPTER 13
Performance engineering with the actual numbers
File sizing, the three layout strategies and why only one is reversible, clustering economics, and the caching layer that silently controls your bill.
CHAPTER 14
The cost model, from the primary documents
How credits and DBUs are actually computed, the minimums that catch people, and the six things that produce a surprise invoice on each platform.
CHAPTER 15
Governance and sharing, mechanically
How each platform actually enforces policy, what lineage does and does not capture, and the two sharing protocols that make opposite trades.
CHAPTER 16
Streaming, and what 'exactly once' actually guarantees
Checkpoints, the key-value store that makes file ingestion idempotent, channels and offset tokens — and the preconditions every exactly-once claim quietly depends on.