Posts
All the articles I've posted.
- 9 MIN READ•Oct 21, 2024
Parquet Metadata Explained: Footer, Row Groups, Statistics
Parquet metadata lives in the file footer: the schema, row group layout and per-column min/max statistics that let query engines skip data they do not need.
data lakehousedata engineeringapache parquet - 7 MIN READ•Oct 21, 2024
Read and Write Parquet Files in Python (PyArrow, Pandas)
How to read and write Parquet files in Python with PyArrow, FastParquet, and Pandas, including column selection, compression, and partitioned datasets.
data lakehousedata engineeringapache parquet - 9 MIN READ•Oct 21, 2024
Parquet Performance Tuning: 8 Best Practices
Tune Apache Parquet for speed and cost: row group and file sizes, partitioning, Snappy vs ZSTD compression, encodings, sorting, and predicate pushdown.
data lakehousedata engineeringapache parquet - 43 MIN READ•Sep 23, 2025
Data Lakehouse Guide 2026: Architecture and Ecosystem
What a data lakehouse is and how it works: Iceberg, Delta, Hudi, and Paimon table formats, the five core layers, catalogs, ingestion, and optimization.
Data LakehouseData EngineeringAgentic AI - 23 MIN READ•Sep 24, 2025
Open Table Formats Compared: Iceberg, Delta, Hudi, Paimon
Apache Iceberg, Delta Lake, Hudi, Paimon and DuckLake compared: metadata design, row-level updates, engine support, and when to choose each table format.
Data LakehouseData EngineeringAgentic AI - 9 MIN READ•Jan 10, 2026
Recursive Language Models (RLMs): How They Work
Recursive Language Models keep a long prompt in a Python REPL and let the model call itself on pieces of it, handling inputs 100x beyond its context window.
artificial intelligencelanguage modelsmachine learning - 5 MIN READ•Feb 18, 2026
Idempotent Data Pipelines: Patterns, SQL, and Pitfalls
An idempotent pipeline gives the same result no matter how often it runs. Use partition overwrite, MERGE upserts, and dedup to make retries and backfills safe.
data engineeringbest practicesidempotency - 6 MIN READ•Feb 18, 2026
What Is Data Modeling? Definition, Types, and Examples
Data modeling defines the entities, attributes, and relationships in your data. Learn the conceptual, logical, and physical levels and the main techniques.
data modelingdata engineeringdata lakehouse - 6 MIN READ•Feb 18, 2026
Conceptual vs Logical vs Physical Data Models (Examples)
Conceptual, logical, and physical data models explained with one e-commerce example: what each level contains, who owns it, and how one feeds the next.
data modelingdata engineeringconceptual model - 6 MIN READ•Feb 18, 2026
Star Schema vs Snowflake Schema: Differences and When to Use
A star schema keeps each dimension in one flat table; a snowflake schema splits dimensions into normalized sub-tables. Compare joins, speed, and use cases.
data modelingdata engineeringstar schema - 6 MIN READ•Feb 18, 2026
Dimensional Modeling Explained: Facts, Dimensions, Grain
Dimensional modeling organizes analytics data into fact tables and dimension tables. Learn how to declare the grain, design both table types, and conform them.
data modelingdata engineeringdimensional modeling - 6 MIN READ•Feb 18, 2026
Denormalization: When and Why to Denormalize Your Data
Denormalization adds deliberate redundancy to cut joins and speed up reads. Learn when it helps analytics, when to avoid it, and how views do it virtually.
data modelingdata engineeringdenormalization