# TeamDataWorks

> A research laboratory exploring data intelligence and agentic AI — sharing hands-on data engineering and ML work openly with the global data community.

TeamDataWorks publishes research, tutorials, and a long-form learning book on Databricks, PySpark, Delta Lake, and modern data platforms. Recognized with an Honorable Mention in the 2025 Databricks Free Edition Hackathon (FMD — Future of Movie Discovery).

## Pages

- [Home](/): Overview of the lab and current focus areas.
- [About](/about): Brahma Reddy, founder and principal investigator.
- [Projects](/projects): FMD, Data Academy, and other research projects.
- [Research](/research): Research focus areas and ongoing work.
- [Data Resources](/data-resources): Curated public datasets for data engineering practice.
- [Tutorials](/tutorials): Step-by-step tutorials on data engineering and ML.
- [Docs](/docs): Reference documentation.
- [Data Agent Lab](/data-agent-lab): Live specialized AI agents that turn business questions into decisions with evidence, backed by a real cloud database.
- [Retail Expansion Agent](/data-agent-lab/retail-expansion): Recommends the next NovaMart store locations by analyzing ZIP-level demand, income, growth and coverage gaps.
- [Patient Continuity Agent](/data-agent-lab/patient-continuity): Builds a longitudinal, ontology-aware view of a patient across fragmented healthcare data. Fully synthetic demo, no real PHI.

## Blog

- [Research Blog](/blog): Experiments, lessons, and essays on data engineering and AI.

## Learn Databricks — Book

- [Learn Databricks (book home)](/learn-databricks): Table of contents for the long-form learning book.

## Principles of Agentic Data Systems — Book

- [Principles of Agentic Data Systems](/principles-of-agentic-data-systems): Free field guide (Early Access v0.1) by Brahma Reddy — the data engineer's operating manual for the agentic era. Covers tools, memory, RAG, evals, observability and governance. Includes PDF and EPUB downloads and bonus templates.

- [Start Here](/learn-databricks/start-here): How to read this book and why it exists.
- [DataFrames](/learn-databricks/dataframes): Your first mental model for Spark.
- [Spark SQL](/learn-databricks/spark-sql): Asking questions of your data with SQL.
- [Transformations](/learn-databricks/transformations): Shaping data with PySpark.
- [Joins & Aggregations](/learn-databricks/joins-aggregations): Combining and summarizing data.
- [Delta Lake](/learn-databricks/delta-lake): ACID transactions on the lake.
- [Partitioning & Performance](/learn-databricks/partitioning-performance): Making jobs fast.
- [Streaming Fundamentals](/learn-databricks/streaming-fundamentals): Structured Streaming.
- [Data Quality](/learn-databricks/data-quality): Expectations and validation.
- [Unit Testing](/learn-databricks/unit-testing): Testing PySpark code.
- [Medallion Architecture](/learn-databricks/medallion-architecture): Bronze, silver, gold.
- [Workflows & Orchestration](/learn-databricks/workflows-orchestration): Production deployment.
- [Unity Catalog](/learn-databricks/unity-catalog): Governance and lineage.
- [File Formats & Optimization](/learn-databricks/file-formats-optimization): Parquet, Z-ordering, compaction.
- [Debugging & Monitoring](/learn-databricks/debugging-monitoring): Spark UI, query plans, common errors.

## Newsletter

- [Newsletter Archive](/newsletter-archive): Past issues of the TeamDataWorks newsletter.

## Optional

- [Terms](/terms): Terms of service.
- [Privacy](/privacy): Privacy policy.