Notebooks
Notes you would
actually keep.
Handwritten-style notebooks on SQL, Python, PySpark, cloud, Airflow, dbt, Kafka and interview prep. Pick one and scroll through it right here.
- 20+ notebooks
- 5 tracks
Handwritten-style notes
Diagrams, tables and worked examples laid out like the notebook you wish you had kept.
Scroll and read
Open a notebook and keep scrolling, like a page in front of you. Your place is saved.
Linked to practice
Every notebook points to the drills and lessons on LakeBench that match it.
Start here
Three notebooks to open first.
Foundations
SQL Simplified
SELECT, JOINs, GROUP BY and window functions explained one small table at a time.
Foundations
Python Simplified
Python from the first variable to files, functions and the habits data work needs.
Foundations
Fundamentals of Data Engineering
What data engineers build and why: storage, pipelines, batch, streaming and the lifecycle.
Foundations
Start here. Python, SQL, Spark, modeling and the core of data engineering.
Foundations
Python Simplified
Python from the first variable to files, functions and the habits data work needs.
Foundations
SQL Simplified
SELECT, JOINs, GROUP BY and window functions explained one small table at a time.
Foundations
PySpark End-to-End Master Study Notebook
Spark architecture, DataFrames, joins and tuning, from first job to a working pipeline.
Foundations
Fundamentals of Data Engineering
What data engineers build and why: storage, pipelines, batch, streaming and the lifecycle.
Foundations
Data Modeling: The Visual Notebook
Facts, dimensions, star schemas and slowly changing data, drawn rather than described.
Interview
Worked problems and playbooks for the rounds you will actually sit.
Interview
Python Round 1: 60 Core Interview Coding Problems
60 Python coding problems that first-round interviews keep asking, worked step by step.
Interview
SQL Scenarios: Real-World SQL & LeetCode Patterns
Real-world SQL scenarios and the LeetCode patterns behind them, with the reasoning shown.
Interview
DSA: 100 FAANG Master Interview Problems
100 data-structure and algorithm problems, grouped by pattern, with clean solutions.
Interview
Data Engineer Interview Playbook
How data engineering interviews run, what each round tests, and how to prepare.
Cloud
AWS, Azure and GCP data stacks, with the questions interviews ask.
Cloud
AWS Cloud Data Engineer
S3, Glue, Redshift, Athena, IAM and the AWS data stack, with the questions interviews ask.
Cloud
Azure Cloud Data Engineer
ADLS, Data Factory, Synapse, Databricks on Azure and the interview questions around them.
Cloud
Google Cloud (GCP) Data Engineer
BigQuery, Cloud Storage, Dataflow, Pub/Sub and Composer, with interview questions.
Modern stack
Snowflake, Airflow, dbt, Kafka and Databricks, the tools teams hire for.
Modern stack
Snowflake for Data Engineers
Warehouses, stages, streams, tasks and cost control in Snowflake.
Modern stack
Apache Airflow for Data Engineers
DAGs, operators, scheduling, retries and backfills in Apache Airflow.
Modern stack
dbt for Data & Analytics Engineers
Models, tests, sources and docs in dbt, and how a project is structured.
Modern stack
Apache Kafka & Stream Processing for Data Engineers
Topics, partitions, consumer groups and stream processing patterns in Kafka.
Modern stack
Databricks Lakehouse for Data Engineers
Delta Lake, notebooks, jobs and the medallion layout on Databricks.
DevOps and quality
Git, Docker, Linux, Terraform and the habits that keep pipelines reliable.
DevOps and quality
Git, Docker & CI/CD for Data Engineers
The Git, container and CI/CD workflow a data team uses to ship pipelines safely.
DevOps and quality
Linux, Bash & Shell Wrangling for Data Engineers
The shell commands, pipes and scripts that save hours when you wrangle data on a server.
DevOps and quality
Terraform, Helm & Infrastructure as Code for Data Platforms
Describe data infrastructure as code with Terraform and Helm, and change it safely.
DevOps and quality
Data Quality, Testing & Observability
Tests, contracts and monitoring that catch bad data before your users do.