DCubes

Roadmap

Foundations, then the craft

Start with Linux, then Docker and Compose, then Python, SQL, and Git. Data Engineering starts with Apache Airflow, then a local lakehouse lab. Foundations, Airflow, and the lakehouse on-ramp are ready to take today.

Foundations

The environment and languages you will use before pipelines and warehouses.

  1. 01

    ~7 hours

    Linux fundamentals

    The terminal, files, logs, pipes, and the skills you will use on every data machine.

  2. 02

    ~8 hours

    Docker and Compose

    Images, containers, volumes, and Compose files for local data stacks.

  3. 03

    ~11 hours

    Python

    Virtualenvs, files as data, pandas, and loading Postgres from scripts.

  4. 04

    ~10 hours

    SQL

    Query Postgres: joins, CTEs, window functions, and habits for warehouse work.

  5. 05

    ~8 hours

    Git

    Version scripts and SQL — branches, ignores, remotes, and pull requests.

Data Engineering

Move data, keep it correct, and run it on a schedule. Airflow is first, then a local lakehouse lab; warehouses and Spark follow.

  1. 01

    ~4 hours

    Apache Airflow

    DAGs, schedules, and a local Airflow stack in Docker.

  2. 02

    ~2 hours

    Data lakehouse lab

    Clone dcubes-lakehouse: RustFS, Iceberg, Spark writes, Trino reads. Then object storage and Iceberg concepts.

  3. 03

    Coming soon

    Warehouses and modeling

    Tables, grain, and how analytics databases think.

  4. 04

    Coming soon

    Distributed compute

    Spark and friends — when one machine is not enough.

  5. 05

    Coming soon

    Cloud data platforms

    Object storage, managed warehouses, and identity.