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The complete notes · 2026 edition

Data Engineering End to End Notes

Hadoop to Terraform in one flow. Every concept, command, config and interview angle across the modern data engineering stack, with each chapter dressed in its own tool's colours.

Big data and Hadoop, MapReduce and YARN, Hive, HBase, Spark and PySpark, pipelines, Docker, Kafka, Airflow, AWS, Azure and Google Cloud, distributed databases and Cassandra, warehousing with Redshift, BigQuery and Snowflake, Kubernetes, GitLab CI/CD, Terraform, high-performance data engineering and data security. Each chapter opens with an “In this chapter” list and closes with interview questions.

By Durgesh Yadav · Founder · Senior Data Engineer @ 7-Eleven · 2026 edition · 28 chapters · 157 pages

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Data Engineering End to End Notes, by Durgesh Yadav: the cover, with the tools of the stack from Hadoop to Terraform
chapters, Hadoop to Terraform, each in its own tool's colours
28chapters, Hadoop to Terraform, each in its own tool's colours
pages in one PDF, to read here and to download
157pages in one PDF, to read here and to download
pages free to read after one Google sign-in
20pages free to read after one Google sign-in
closes with interview questions, Introduction to Big Data to Data Security
Every chaptercloses with interview questions, Introduction to Big Data to Data Security

2026 edition · 28 chapters · 157 pages

All 28 chapters, in reading order

The book’s contents pages, grouped into ten parts for this page. Each chapter starts on the page shown, opens in its own tool’s colours and closes with interview questions.

  1. Part 01Chapters 1 to 3

    Big data foundations

    1. 01Introduction to Big Datapages 4-7
    2. 02Hadoop & HDFSpages 8-14
    3. 03MapReduce & YARNpages 15-18

  2. Part 02Chapters 4 and 5

    SQL and NoSQL on Hadoop

    1. 04Apache Hivepages 19-24
    2. 05Apache HBasepages 25-29

  3. Part 03Chapters 6 and 7

    Spark and PySpark

    1. 06Apache Sparkpages 30-34
    2. 07PySpark Hands-onpages 35-42

  4. Part 04Chapters 8 to 11

    Pipelines, containers, streaming and orchestration

    1. 08Data Pipelines, ETL & Streamingpages 43-47
    2. 09Docker & Docker Composepages 48-52
    3. 10Apache Kafkapages 53-58
    4. 11Apache Airflowpages 59-64

  5. Part 05Chapters 12 to 15

    AWS for data engineers

    1. 12Cloud, AWS, IAM & EC2pages 65-70
    2. 13Amazon S3, DNS & Route 53pages 71-74
    3. 14AWS Lambda & VPCpages 75-78
    4. 15AWS Glue & Athenapages 79-82

  6. Part 06Chapters 16 and 17

    Azure and Google Cloud

    1. 16Microsoft Azurepages 83-89
    2. 17Google Cloud Platformpages 90-95

  7. Part 07Chapters 18 and 19

    Distributed databases and Cassandra

    1. 18Distributed Databasespages 96-99
    2. 19Apache Cassandra & CQLpages 100-106

  8. Part 08Chapters 20 to 24

    Warehousing, with Kubernetes

    1. 20Data Warehousingpages 107-110
    2. 21Amazon Redshiftpages 111-116
    3. 22Google BigQuerypages 117-121
    4. 23Kubernetes for Data Engineerspages 122-127
    5. 24Snowflakepages 128-134

  9. Part 09Chapters 25 and 26

    CI/CD and infrastructure as code

    1. 25GitLab CI/CDpages 135-141
    2. 26Terraformpages 142-148

  10. Part 10Chapters 27 and 28

    Performance and security

    1. 27High-Performance Data Engineeringpages 149-153
    2. 28Data Securitypages 154-157

How it is written

Every chapter runs the same way

Reference notes, not a story: each chapter is one tool or one idea, written so you can read the book in order or open the one chapter you need before an interview. Every chapter is dressed in its own tool’s colours, as the cover says.

  1. 1In this chapterThe chapter opens with its numbered sections listed on one page, so you see the ground it covers before you read.
  2. 2The conceptEach section explains the idea first: what the tool is for, how it is built and where it sits in the stack, with tables and notes where a comparison helps.
  3. 3Commands and configsWhere a tool has them, the commands and the configuration that run it, in the tool's own syntax.
  4. 4Interview questionsEach chapter closes with numbered interview questions and their answers: the angle an interviewer takes on that tool.

Free sample

Read 20 of the 157 pages free

The cover, the two contents pages and the first page of 17 chapters:

  • p1 Cover
  • p2 Contents
  • p3 Contents (continued)
  • p4 Introduction to Big Data
  • p8 Hadoop & HDFS
  • p19 Apache Hive
  • p30 Apache Spark
  • p35 PySpark Hands-on
  • p53 Apache Kafka
  • p59 Apache Airflow
  • p65 Cloud, AWS, IAM & EC2
  • p83 Microsoft Azure
  • p90 Google Cloud Platform
  • p96 Distributed Databases
  • p107 Data Warehousing
  • p111 Amazon Redshift
  • p128 Snowflake
  • p142 Terraform
  • p149 High-Performance Data Engineering
  • p154 Data Security

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Who it is for

The whole stack, mapped once

  • You are a data engineer preparing for interviews and want every tool's angle in one place
  • You are switching tools, say Hadoop to Spark or Redshift to Snowflake, and want the new one mapped the same way as the old
  • You are a fresher who wants the whole stack mapped once, from HDFS to Terraform, before going deep anywhere
  • You want the commands and configs beside the concept, not in a separate cheat sheet

How to read it

  1. 1. Start from the contents. Pages 2 and 3 list all 28 chapters with their page numbers: read in order, or open the tool you are preparing for.
  2. 2. Read the “In this chapter” list first.It names the chapter’s sections, so you know what it covers before the detail.
  3. 3. Close each chapter with its questions. Answer them out loud before you read the answers: the question is the angle an interviewer takes.
  4. 4. Preparing for a full loop? Pair the notes with the Data Engineering Interview Kit, which works the rounds one deck at a time; these notes are the reference to keep open beside it.

Questions

Before you buy

Is this the free Data Engineering book?

No. The free book is the zero-to-production story in 244 pages. These notes are the tool-by-tool reference: 28 chapters, each with its commands, configs and the interview questions that close it.

What do I get?

The 157-page PDF, to read online here and to download, stamped with your email on every page.

Which pages are free?

The cover, the two contents pages and the first page of 17 chapters, after one Google sign-in. 20 pages in all.

Is it included in the Learn Pass or a plan?

No. Like the other PrepNPlaced books it is sold on its own. Data Engineering cohort learners, past and present, and mentorship members open it free from this page after signing in.

Does every chapter have interview questions?

Yes. Interview questions close every chapter, from Introduction to Big Data to Data Security.

Not for you? Start here