Ultimate Python, SQL & Spark for Data Engineering Bootcamp

Join a six-phase master class covering production setup and runtime, core modeling with dbt, testing and quality enforcement, yaml specs and documentation, observability, performance engineering, and production strategies.

Explore how Python anchors data engineering by linking data sources—APIs, files, Kafka, and databases—to storage, analytics, and business intelligence, with extraction, ingestion, validation, transformation, and orchestration.

Create a structured project skeleton by setting up a venv, a root repository, and folders for data, data/raw, and src. Document the setup with readme.md, lock dependencies with requirements.txt, and organize code and data to support medallion bronze, silver, and gold layers.

Improve Python project structure by enforcing four-space indentation, avoiding tabs, and adding comments, while using Visual Studio Code and the CLI to explore data types and core data structures.

Explore how Python data structures work, comparing lists, tuples, sets, and dictionaries, including mutability, ordering, indexing, and deduplication for data engineering workflows.

Master raw API response handling in Python and apply SQL and Spark approaches for data engineering in this bootcamp module.

Diagnose dag failures by inspecting task logs and error lines; use chat gpt for fixes. See a dag file creation in VS Code, with a start date and airflow decorators.

Group by country in pandas data frame to perform aggregations, counting order ids and summing amounts, then compute a running total with a cumulative sum for data transformations.

Learn to use try, except, and finally to handle runtime errors, prevent pipeline breaks, and perform cleanup, with exception flow examples like file not found and type errors.

Learn how Python logging levels control output, master debugging and defensive error handling, and connect to Snowflake via Python drivers with reproducible environments.

Learn the Python to Snowflake flow with the Snowflake connector, translating Python commands to Snowflake SQL; configure credentials, warehouse, database, and schema, then execute queries via a cursor.

Build and optimize a data pipeline in data engineering, applying Python, SQL, and Spark to ingest, transform, and move data efficiently.

Build a three-step data pipeline that extracts data from dirty orders.csv, transforms it into cleaned data, and loads it into destination.csv, using a class-based orchestrator and a config.json file.