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Build a full ETL pipeline: Airflow orchestrates API ingestion, Docker handles the environment, and Databricks Delta tables store the result.

Get started with Apache Airflow running in Docker — write your first DAGs, understand scheduling, and orchestrate a real data pipeline.

Understand Apache Iceberg's table format, its advantages over Hive, and build hands-on with the definitive guide exercises.

Learn PySpark fundamentals by running distributed transformations locally with Docker, then scaling to a cloud cluster.

Deploy a secured Azure Databricks workspace inside a Virtual Network with No Public IP and connect it to Azure Storage.

Get hands-on with BigQuery — Google's serverless data warehouse. Load data, run analytical queries, and understand cost management.

A 6-week deep dive into multiple data ingestion approaches using the GitHub API as a source and Snowflake as the target.

Deploy Airbyte locally and build connector-based data pipelines to load external data into your warehouse without writing custom code.

Implement automated data quality checks on Snowflake using Soda Core to catch bad data before it reaches dashboards.

Use Databricks DQX to run data quality checks inside notebooks and pipelines — validate schemas, completeness, and custom rules.

Read data from a relational database into Pandas DataFrames and write it to another database — the foundation of every ETL pipeline.

Deploy a full dbt transformation platform from scratch with Snowflake, virtual environments, and Docker packaging.

Learn dbt from scratch using GitHub Codespaces and Docker — models, tests, sources, and documentation in a single project.

Add observability to a dbt project using Elementary Data — get automatic test results, anomaly detection, and a report UI.

Deploy a Dockerized application integrated with PostgreSQL using Docker Compose, automated with GitHub Actions CI/CD.

Multi-week community project: build a full analytics stack with dbt transformations on Azure PostgreSQL and Looker dashboards on top.

Build a complete ETL pipeline on AWS: pull from Adzuna API, transform, and load into a data warehouse with full orchestration.

A structured introduction to the Databricks platform — notebooks, clusters, Delta Lake, and Unity Catalog, built with the community.

Learn Polars — the Rust-based DataFrame library that outperforms Pandas on large datasets with a clean, expressive API.

Discover PyArrow for fast columnar data processing in Python — significantly faster than Pandas for large datasets and Parquet files.

Build modular PySpark ETL code, package it as a Python wheel, deploy to Databricks, and run it as an automated workflow.

Explore the modern data stack through Snowflake for warehousing and Hex for collaborative analytics and visualization.

Explore where Snowflake and Apache Iceberg intersect — build an open-format Lakehouse that combines Snowflake compute with Iceberg storage.

Build a PySpark ETL pipeline structured like a production codebase, with unit tests, a test framework, and CI integration.

Explore SQLMesh as a modern alternative to dbt — automatic change detection, virtual environments, and built-in state management.

Deploy Trino via Docker and standalone mode, then connect it to multiple data sources and use the trino-dbt connector.

Simulate a real company's data warehouse on Azure: ADF orchestration, Azure SQL as source, Snowflake as target, built week by week.

Learn the BI Admin role in Tableau Server/Cloud — user management, content governance, performance monitoring, and security.

Build a Looker dashboard using Snowflake as the data warehouse and dbt-transformed data as the foundation for LookML models.

Deploy Metabase as a free, open-source BI solution — connect to a database, build dashboards, and understand when OSS BI makes sense.

Build a Power BI dashboard end-to-end using Microsoft Fabric: Dataflows Gen2 ingest data into a Lakehouse, pipelines orchestrate it, and Power BI visualizes it.

Learn DevOps concepts applied to data: Git workflows, GitHub Actions CI pipelines, automated testing, and deployment for data projects.

A beginner-friendly introduction to Terraform — provision cloud infrastructure for data projects using code instead of clicking through UIs.

A comprehensive hands-on course from Kafka fundamentals to production-grade real-time pipelines — every module has exercises, solutions, and Docker environments.

Learn the core concepts of real-time data streaming — event producers, brokers, consumers, and stream processing patterns.

A realistic mock interview for Analytics Engineer roles — fintech context, dimensional modeling challenge, and SQL technical assessment.