Data Structures & Formats

Row vs columnar, Parquet vs ORC vs Avro, encoding, compression and predicate pushdown. This path sequences the category's 12 resources by roadmap stage and composite score — work top-down inside each step.

Foundations

1. Hold the row-versus-column mental model 34.15 — Hold the row-versus-column mental model
2. Open the Parquet file — row groups, pages, encodings 34.15 — Open the Parquet file — row groups, pages, encodings
3. Arrow — columnar memory and zero-copy interchange 34.15 — Arrow — columnar memory and zero-copy interchange
4. Avro and schema evolution for event payloads 34.15 — Avro and schema evolution for event payloads
5. Choosing file formats — Parquet, Avro, and Arrow's role 34.15 — Choosing file formats — Parquet, Avro, and Arrow's role
6. Compression and encodings — the tradeoffs behind the file 34.15 — Compression and encodings — the tradeoffs behind the file

6 more on the category page.

• Say why analytics engines read columnar files — columnar files scan less data
• Place file-format decisions on the storage stage of the data engineering lifecycle
• Point at the sources that make columnar claims exact — layout, statistics, memory
• Name the parts of a Parquet file — row groups, column chunks, pages, statistics
• Explain what per-row-group statistics buy a reader through predicate pushdown
• Reach the authoritative wording instead of paraphrasing the format
• Explain Arrow's role as the columnar in-memory format and interchange substrate
• Name the pieces — language bindings, IPC and Flight protocols, zero-copy interchange

Verification on these records: 12 community. Nothing here is independently audited — the trust scan and your own review are the controls.

Phase 1 of the roadmap. Stage context: Foundations · side-by-side comparison · all learning topics