learn by doing it · 15 min · beginner · English

GitHub Tutorial for Beginners | How to Raise a Pull Request Step by Step | data engineer

Soumil Shah · 17 min · advanced · English

Bring Data from Source using Debezium with CDC into Kafka&S3Sink &Build Hudi Datalake | Hands on lab

Data Engineering · 20 min · beginner · English

Data Engineer Roadmap 2026: 3 Levels to Get Hired

Optional job-search angle: how to position skills and portfolio across hireable/amazing/master levels for a targeted 2026 search.

Indeed · 8 min · beginner · English

Behavioral Interview Questions and Answers: Use the STAR Technique | Indeed Career Tips

Clean, reputable breakdown of the STAR method for structuring impact-driven behavioral answers.

Data with Zach · 24 min · intermediate · English

Data Engineering System Design Made Easy

Optional framework angle: a repeatable way to structure batch vs streaming pipeline designs from a former Meta engineer.

Exponent · 47 min · intermediate · English

Spotify Data Engineer - System Design Interview

Realistic full data engineering system design mock (streaming event pipeline) showing how to drive the round and defend trade-offs.

Ankit Bansal · 39 min · intermediate · English

Python Coding Interview Questions Series for Data Analysts and Data Engineers

Python coding-interview problems aimed at data analysts/engineers — solve under timed conditions.

techTFQ · 26 min · intermediate · English

Top 25 SQL Interview Questions and Answers(The BEST SQL Interview Questions)

Most-asked SQL interview questions worked through as live queries under interview framing.

Andreas Kretz · 16 min · intermediate · English

5 Ways To Monitor Data Pipelines - Every engineer needs to know this!

Fills the observability gap: 5 practical ways to monitor/observe production data pipelines.

Data with Baraa · 60 min · intermediate · English

Code along - build an ELT Pipeline in 1 Hour (dbt, Snowflake, Airflow)

Optional tighter angle: a fast code-along orchestrating a dbt + Snowflake ELT pipeline with Airflow as production-grade scheduled code.

SleekData · 7 min · intermediate · English

DBT Tests | Data Build Tool | Singular | Generic | Custom | Prebuild | Source Freshness

dbt tests end-to-end: generic, singular, custom tests and source freshness to ship tested models.

Kahan Data Solutions · 17 min · intermediate · English

Data Modeling Tutorial: Star Schema (aka Kimball Approach)

Dimensional modeling foundations (Kimball star schema: facts, dimensions, grain) for the warehouse layer.

BI Insights Inc · 14 min · intermediate · English

dbt build Star Schema using dimensional modeling | data modeling with dbt | build dims & fact | P4

Hands-on: build star-schema dim & fact models in dbt — the silver/gold transformation/marts layer.

Data with Zach · 8 min · intermediate · English

Data Pipelines in 8 minutes: Streaming, Batch, and on-demand

Ingestion patterns: streaming vs batch vs on-demand — choosing the right approach for the source layer.

ByteByteGo · 7 min · intermediate · English

What is a Data Lakehouse?

Lakehouse architecture explained — how ingestion/storage/query layers fit together (design foundation).

Data Engineering · 17 min · intermediate · English

Build a Data Lakehouse with AWS S3 & Athena!

Hands-on build of the ingestion layer: land raw data in an S3 lakehouse and query it with Athena.

TechWorld with Nana · 18 min · beginner · English

Terraform explained in 15 mins | Terraform Tutorial for Beginners

Terraform IaC essentials: providers, state and the plan/apply workflow to provision infrastructure.

TechWorld with Nana · 33 min · beginner · English

GitHub Actions Tutorial - Basic Concepts and CI/CD Pipeline with Docker

CI/CD with GitHub Actions that builds and pushes Docker images — covers containerizing pipelines and deploying.

Dynatrace · 34 min · intermediate · English

Anomaly Detection on 5 Pillars of Data Observability with Dynatrace Davis AI

Observability angle: freshness, volume, schema, distribution and lineage monitoring

BiteSize Academy · 17 min · intermediate · English

Implementing Data Quality in Python w/ Great Expectations

Hands-on Great Expectations: expectation suites and validating data in a pipeline

Confluent · 11 min · intermediate · English

Intro to Stream Processing with Apache Flink | Apache Flink 101

Different angle: stream processing fundamentals, event time, windowing and exactly-once with Flink

Stacksimplify · 18 min · beginner · English

Kafka Topics, Partitions & Consumer Groups | Explained with Real Examples

Core Kafka data model: topics, partitions, consumer groups, offsets with real examples

Kahan Data Solutions · 20 min · intermediate · English

Your First DBT CI/CD Pipeline With GitHub Actions

Runs dbt tests and deploys via GitHub Actions CI/CD with SQLFluff linting

Data Engineering · 25 min · intermediate · English

Dbt Materializations - Incremental & Snapshot | Slowly Changing Dimension SCD Type 2

Incremental materialization plus snapshots for SCD Type 2 history

Kahan Data Solutions · 15 min · beginner · English

Intro to Data Build Tool (dbt) // Create your first project!

Create your first dbt project: sources, models, ref() and the DAG — the layered-project fundamentals.

Data with Marc · 60 min · intermediate · English

[Data Build Tool] DBT - The Ultimate Guide | With CI/CD

Optional alternate angle: end-to-end dbt project including staging/marts structure

Dagster · 10 min · beginner · English

Dagster Crash Course: develop data assets in under ten minutes

Quick official intro: build your first software-defined data assets fast.

Dagster · 26 min · intermediate · English

Converting an ETL script to Software-Defined Assets

Refactor an imperative ETL script into Dagster software-defined assets vs task-based DAGs.

codebasics · 9 min · beginner · English

What is Apache Airflow?

Concise conceptual intro to Airflow: DAGs, scheduler and operators.

Data with Marc · 40 min · intermediate · English

Apache Airflow Tutorial for Data Engineers

Different angle: data-engineer-focused first DAG (S3 to Postgres) plus writing custom operators.

Darshil Parmar · 11 min · beginner · English

Learn Apache Spark in 10 Minutes | Step by Step Guide

Spark architecture intro (driver/executors/partitions) to start reasoning about distribution and shuffles.

coder2j · 48 min · beginner · English

PySpark Tutorial for Beginners

Hands-on PySpark transformations: DataFrame ops, filter/join/groupBy and Spark SQL.

Data Engineering Things · 14 min · intermediate · English

Apache Iceberg vs Delta Lake - Which Open Table Format Should You Choose in 2025?

Optional comparison angle: Delta Lake vs Iceberg trade-offs for lakehouse ACID.

Confluent · 16 min · intermediate · English

Understanding Open Table Formats: Iceberg, Delta Lake, & Apache Hudi Explained

ACID table formats overview — how Iceberg/Delta add transactions, schema evolution, and time travel on a lake.

Data Mozart · 9 min · beginner · English

Parquet File Format - Explained to a 5 Year Old!

Optional: focused explainer on why columnar Parquet is the de-facto data lake file format.

EcZachly Data Engineering (Zach Wilson) · 60 min · intermediate · English

Data Lake Fundamentals, Apache Iceberg and Parquet in 60 minutes on DataExpert.io

Object storage layout, partitioning, and columnar Parquet that underpin the lakehouse.

Data With Dominic · 18 min · intermediate · English

BigQuery Partitioning & Clustering | Performance Optimization + Live Demo!

Optional BigQuery angle: hands-on partitioning + clustering to cut cost and speed up queries in a serverless warehouse.

codebasics · 31 min · intermediate · English

What is Snowflake? Learn Snowflake in 30 Minutes

Snowflake essentials: architecture, virtual warehouses, loading data and querying a serverless warehouse.

Seattle Data Guy · 14 min · advanced · English

OLTP vs OLAP | Normalization vs Denormalization | Finding the Right Balance

Trade-offs of normalization vs denormalization across OLTP and columnar OLAP warehouses, framing wide-table/OBT choices.

BiInsightS · 10 min · intermediate · English

Slowly Changing Dimensions (SCD) Explained: Type 1, 2, & 3

Focused walkthrough of Type 1, 2, and 3 with effective-date logic for history tracking.

Learn with Whiteboard · 12 min · intermediate · English

Slowly Changing Dimensions (SCD) | Types 0, 1, 2, 3, 4 Explained with Real Examples

Clear example-driven explanation of all SCD types, covering overwrite (Type 1) vs versioned rows (Type 2).

Bryan Cafferky · 38 min · intermediate · English

Dimensional Data Model Tutorial - A Kimball Style Data Model

Hands-on walkthrough of building a Kimball-style star schema with fact and dimension tables and grain.

ArjanCodes · 22 min · intermediate · English

My 2025 uv-based Python Project Layout for Production Apps

Packaging/project-structure angle: uv-managed, reproducible Python project layout

BiInsights Inc · 20 min · intermediate · English

How to test your Python ETL pipelines | Data pipeline | Pytest

Writing pytest unit tests for ETL/data-pipeline code

Data Engineering · 12 min · beginner · English

Big Data File Formats Explained | CSV, JSON, Parquet, ORC, Avro for Data Engineers

File-format angle: when to use CSV/JSON vs columnar Parquet/ORC/Avro for ingestion

Karolina Sowinska · 25 min · intermediate · English

Building A Data Engineering Project - Extracting Data From An API

Robustly ingesting data from a REST API with Python requests in a real DE project

Rob Mulla · 24 min · beginner · English

Polars Tutorial: Blazingly Fast Exploratory Data Analysis in Python

Hands-on Polars DataFrame intro (Arrow-backed, lazy/eager) as a fast pandas alternative

BiInsights Inc · 18 min · intermediate · English

Pandas vs PySpark vs Polars: When to Use Each for Data Engineering

Decision framework for when to reach beyond pandas to Polars/Spark in DE workloads

Ankit Bansal · 24 min · intermediate · English

SQL Incremental data load | 3 ways to perform Upsert | Update else Insert | EXISTS | ROWCOUNT | MERGE

Hands-on MERGE/upsert logic (EXISTS, ROWCOUNT, MERGE) for re-runnable incremental loads without duplicating data.

Tech Coach · 18 min · intermediate · English

How to Tune SQL Query | Oracle Performance Tuning with Explain Plan

Second angle: reading the EXPLAIN plan and reasoning about how the engine executes joins for optimization.