About

Built on curiosity,
powered by data

DataCraft is an independent technical blog for data professionals who care about doing the work well — not just getting it done.

Why DataCraft?

The data ecosystem moves fast — new tools, new patterns, new acronyms every quarter. But foundational thinking doesn't change as quickly. DataCraft exists to explore both: the timeless principles of good data architecture and engineering, alongside honest reviews and practical guides for the modern data stack.

We don't believe in hype. We believe in trade-offs, first principles, and code that actually runs in production.

What We Cover

⚙️
Data Engineering
Pipeline design, orchestration (Airflow, Prefect, Dagster), batch vs. streaming, Spark, Kafka, Flink, Python patterns, and production best practices.
🏗️
Data Architecture
Lakehouse vs. warehouse, Data Mesh, Data Fabric, Medallion architecture, table formats (Delta, Iceberg, Hudi), and system design for scale.
📊
Data Analysis
SQL patterns, analytics engineering, dbt, exploratory analysis, metrics frameworks, BI tools, and translating data into decisions.
🛡️
Data Management
Data quality, cataloging, governance, data contracts, lineage tracking, metadata management, and building data cultures that last.

The Authors

DataCraft is written by a small team of data practitioners with backgrounds across cloud data platforms, financial services, e-commerce, and SaaS.

MR
Marcus Reyes
Senior Data Engineer

Marcus has 8+ years building large-scale data systems, with deep experience in Apache Spark, distributed systems, and cloud data platforms on AWS and GCP. He writes about engineering craft, performance, and the hard lessons from production systems.

SP
Sasha Park
Data Architect

Sasha designs data platforms for scale, with a focus on cloud-native architectures and the organizational dimensions of data strategy. She's particularly interested in Data Mesh and how data teams evolve as companies grow.

JL
Jordan Liu
Analytics Engineer

Jordan lives in the space between raw data and business decisions — SQL, dbt, semantic layers, and data quality. They write about analytics engineering, data modeling, and making data accessible to the teams who need it.


Get in Touch

Have a topic you'd like us to cover? A correction? A guest post idea? We'd love to hear from you. Reach out via email or connect on LinkedIn.

If you'd like to be notified when new articles are published, subscribe to our newsletter — we publish twice a month, no spam.