Beginner level Data Engineering

SQL, Python and Your First ETL Pipelines

Learn how data engineering works from the ground up: SQL for analytics, Python and pandas for wrangling, file formats, ETL pipeline design, dimensional modelling, data quality checks, scheduling and Git, using only free local tools (SQLite, DuckDB and PostgreSQL), and build a warehouse with a dashboard.

  • 41hours of work
  • 13parts
  • Checkedcapstone
  • Lifetimeaccess
  • SQL
  • Python and pandas
  • ETL design
  • Dimensional modelling
  • Data quality
  • DuckDB and PostgreSQL

At a glance

Who it is for

Beginners, students and analysts who want to become data engineers or work with data pipelines, with no previous data engineering experience.

Before you start

Basic Python (variables, loops, functions, lists and dictionaries) and a computer where you can install Python 3.10 or newer. No SQL knowledge is assumed; the programme teaches it. Docker is optional (used for PostgreSQL from part 10).

How you finish

Pass the quizzes, complete the labs and submit your capstone. Everything is checked as you go and your certificate is issued the moment you finish.

What you will be able to do

  1. 01Write analytical SQL with joins, aggregation, CTEs and window functions
  2. 02Clean and reshape data with Python and pandas and work with CSV, JSON and Parquet files
  3. 03Design and build a tested, repeatable ETL pipeline that can run safely more than once
  4. 04Model data in a star schema and load it into a database or DuckDB warehouse
  5. 05Add data quality checks, logging and scheduling, and publish a documented project on GitHub

Curriculum 41 hours in 13 parts

Select a kind to highlight its parts.

Stage 1A tested, idempotent ETL pipeline from files and an API into a database6 parts · 16 h
  1. 1
    What data engineers do, and how data movesWorkshop · Follow along and build, with the steps and the traps written out.
    2 hFree
  2. 2
    SQL for analytics: queries, joins, aggregation and window functionsWorkshop · Follow along and build, with the steps and the traps written out.
    3 h
  3. 3
    Investigate an e-commerce dataset with SQLWorkshop · Follow along and build, with the steps and the traps written out.
    2 h
  4. 4
    Check yourself: data engineering concepts and SQLQuiz · Twelve questions, marked at once, with the reason behind each answer.
    1 h
  5. 5
    Python and pandas for data wrangling, and file formatsWorkshop · Follow along and build, with the steps and the traps written out.
    3 h
  6. 6
    A tested, idempotent ETL pipeline from files and an API into a databaseLab · Hands-on work. Many labs are checked automatically against what must exist.
    5 h
Stage 2A star-schema warehouse in DuckDB or PostgreSQL with quality gates and a dashboard4 parts · 10 h
  1. 7
    Data modelling for analytics: normalisation, star schemas and keysWorkshop · Follow along and build, with the steps and the traps written out.
    2 h
  2. 8
    Data quality, pipeline testing, scheduling and operationsWorkshop · Follow along and build, with the steps and the traps written out.
    2 h
  3. 9
    Check yourself: pandas, file formats, modelling and qualityQuiz · Twelve questions, marked at once, with the reason behind each answer.
    1 h
  4. 10
    A star-schema warehouse in DuckDB or PostgreSQL with quality gates and a dashboardLab · Hands-on work. Many labs are checked automatically against what must exist.
    5 h
Stage 3Git, CI, Docker and cloud for data work2 parts · 3 h
  1. 11
    Git, Docker and cloud basics for data engineersWorkshop · Follow along and build, with the steps and the traps written out.
    2 h
  2. 12
    Check yourself: Git, CI, Docker and cloud for data workQuiz · Twelve questions, marked at once, with the reason behind each answer.
    1 h
FinishCapstone project1 part · 12 h
  1. 13
    An end-to-end batch data pipeline and analytics productCapstone · A full project for your portfolio.
    12 h
Free lessonWhat data engineers do, and how data moves
What you take away

An internship certificate with your skills on it.

Made for you automatically when you finish the last part, it names the internship programme, the level and the 41 hours, lists what you showed, and carries a number and a QR code. Anyone can check it on the NIBVEDA verification page, and it shows as withdrawn there if it is ever revoked.

  • Proof you completed all 41 hours of this internship programme
  • An Internship badge for your profile, CV or portfolio
  • Not a job, a stipend or an academic degree, and it says so

See how verification works

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See all 3 programmes in Data Engineering Take the placement test

Before you enrol

Quick answers.

The policies have the full detail.

Refund policy Terms of use

Is this a real internship?

It is a virtual internship programme: a structured, self-paced course built around a real project, with checked assessments and a certificate. It is not employment, it does not carry a stipend and it is not an academic or AICTE-approved qualification. What it gives you is verified, project-based evidence of skills.

How does self-paced work?

You start when you like and move at your own speed. Each part unlocks when you finish the one before it. There are no live classes and no deadlines. Your access is shown on each programme page.

How is my work checked?

Quizzes are marked as soon as you submit them. Labs use a tick-list of tasks. Intermediate and Expert programmes finish with a capstone that the NIBVEDA team reviews and either accepts or sends back with comments.

Can I get a refund?

Please read the refund policy before you buy. It explains when a refund is possible.

Ready to start?

Rs. 352.82 including GST. Lifetime access. Read the first lesson free before you decide.

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