Posts - Page 32
All the articles I've posted, page 32.
- 8 MIN READ•Apr 29, 2026
Partition Evolution: Change Your Partitioning Without Rewriting Data
Iceberg lets you change partition schemes without rewriting data. Here is how partition evolution works internally and why Hive-style partitioning could.
Iceberg partition evolutionpartition spechidden partitioning - 8 MIN READ•Apr 29, 2026
Performance and Apache Iceberg's Metadata
Iceberg's three-layer metadata tree eliminates directory listing and enables multi-level data skipping. Here is how scan planning actually works.
Apache Iceberg metadata performanceIceberg scan planningmanifest pruning - 8 MIN READ•Apr 29, 2026
How Databases Organize Data on Disk: Pages, Blocks, and File Formats
Databases structure data on disk as heap files, sorted files, or LSM trees, then wrap it in formats like Parquet with metadata that lets engines skip.
data file formatsParquet file formatdatabase storage internals - 8 MIN READ•Apr 29, 2026
The Metadata Structure of Modern Table Formats
Iceberg uses a metadata tree, Delta Lake uses a transaction log, Hudi uses a timeline. Here is exactly how each format organizes metadata and why it matters.
table format metadataApache Iceberg metadataDelta Lake transaction log - 8 MIN READ•Apr 29, 2026
Row vs. Column: How Storage Layout Shapes Everything
Row stores suit transactions, column stores suit analytics. See the I/O math, why columnar compression wins, late materialization, and when to pick each.
columnar vs row storagecolumn-oriented databaserow store performance - 9 MIN READ•Apr 29, 2026
What Are Table Formats and Why Were They Needed?
Table formats like Apache Iceberg solved the ACID, schema, and performance problems that turned data lakes into data swamps. Here is how each one works.
data lake table formatsApache IcebergDelta Lake - 9 MIN READ•Apr 29, 2026
How Query Engines Think: The Tradeoffs Behind Every Data System
Every database is a collection of engineering tradeoffs. Learn the 9 design decisions that shape how query engines store, index, and process your data.
query engine designdatabase tradeoffsquery optimization - 4 MIN READ•Mar 7, 2026
Agentic Analytics on the Apache Lakehouse
Why LLMs fail on raw Parquet in S3, and how a semantic layer, autonomous reflections, text-to-SQL, and AI SQL functions enable agentic analytics on a lakehouse.
data lakehouseapache icebergapache parquet - 4 MIN READ•Mar 7, 2026
What is Apache Arrow? Erasing the Serialization Tax
Apache Arrow removes the serialization tax with a standard in-memory columnar format, zero-copy sharing, and Arrow Flight for fast transfer across the network.
data lakehouseapache icebergapache parquet - 5 MIN READ•Mar 7, 2026
What is Apache Iceberg? The Table Format Revolution
How Apache Iceberg turns piles of Parquet files into reliable tables: its metadata tree, schema and partition evolution, time travel, and atomic snapshots.
data lakehouseapache icebergapache parquet - 4 MIN READ•Mar 7, 2026
What is Apache Parquet? Columns, Encoding, and Performance
How Apache Parquet speeds up analytics: columnar layout, dictionary encoding and compression, predicate pushdown with row group skipping, and its role today.
data lakehouseapache icebergapache parquet - 21 MIN READ•Mar 7, 2026
What is Apache Polaris? Unifying the Iceberg Ecosystem
Treating thousands of Parquet files as a unified database table requires a brain. Apache Iceberg provides the metadata structure to do this, but.
data lakehouseapache icebergapache parquet