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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

9 Books Every Data Engineering Aspirant Must Read!

Sneha Jain
Sneha Jain Last Updated : 22 Dec, 2023
7 min read

Did you know that ‘Data Engineer’ is the fastest-growing role in the industry?

Currently, most data science aspirants are still focused on landing the coveted role of a data scientist. That’s understandable – all the hype in the media and the community glorifies the role of a data scientist. But it’s the data engineer that’s emerged as the dark horse.

Which isn’t really surprising, is it? Data science professionals spend close to 60-70% of their time gathering, cleaning, and processing data – that’s right down a data engineer’s alley!

Tech behemoths like Netflix, Facebook, Amazon, Uber, etc. are collecting data at an unprecedented pace – and they’re hiring data engineers like never before. There hasn’t been a better time to get into this field!

data engineering books

Unfortunately, there is no coherent path designed to become a data engineer. Most data science aspirants haven’t even heard of the role – they tend to learn about it on the job.

I’ve put together a list of data engineering books to help you get started with this thriving field and make sure you’re acquainted with the various terms, skills, and other nuances required.

And why books?

A lot of successful people attribute their success to reading books. In fact, Analytics Vidhya’s Founder and CEO Mr. Kunal Jain reads one book every week! There is no substitute for books, it’s still one of the best resources you would want to get your hands on.

Books are a vital way of absorbing information on Data Engineering. So let’s begin!

1. The Data Engineering Cookbook by Andreas Kretz

There is a lot of confusion about how to become a data engineer. I’ve met a lot of data science aspirants who didn’t even know this role existed!

Here is an ebook by Andreas Kertz that has elaborate case studies, codes, podcasts, interviews, case studies, and more. I consider this to be a complete package to enable anyone to become a data engineer.

And the icing on the cake? This ebook is free! Yes, you can instantly get started with it. Learn, practice, and prepare for your data engineering role now!

data engineering cookbook

Click here to access – The Data Engineering Cookbook

2. DW 2.0 – The Architecture for the Next Generation of Data Warehousing by The Father of Data Warehousing W.H. Inmon

This book describes the future of data warehousing that is technologically possible today, at both an architectural level as well as a technology level.

I really like how the book is neatly structured and covers most of the topics related to data architecture and its underlying challenges, how can you use the existing system and build a data warehouse around it, and the best practices to justify the expenses in a very practical manner.

data engineering books, DW 2.0

This book is designed for:

  • Anyone who aspires to become a data engineering professional
  • Organizations that want to induct this capability into their systems
  • Data architects
  • DBA’s
  • System designers, and
  • Data warehouse professionals

DW 2.0. is * Written by the “father of the data warehouse”, Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network.

This one is not to be missed! Here is where you can grab a copy – Amazon.com.

3. Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema by Lawrence Corr

This is a great book. Lawrence Corr provides a comprehensive and step-by-step guide to capturing data warehousing and business intelligence requirements and convert them into high-performance models by using a technique called model storming (model + brainstorming).

Additionally, you’ll come across a concept called BEAM, an agile approach to dimensional modeling for improving communication between data warehouse designers and business intelligence stakeholders.

data engineering books,Agile Data Warehouse Design

Source this book at Amazon.com

4. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball

What do you wish for as a data scientist?

How about getting data that’s clean and reliable? With all the business value captured and presented well in the data, you would definitely wish for accurate and robust data models, high application agility and well-designed models as the final outcome.

How would you feel if someone just granted you these wishes and made your dream to become a champion data engineer come true? So, why wait for that ‘someone’ to grant your wishes when you can find a way to chalk your path and get these wishes granted by yourself by simply reading this book!

Yes, this book is the third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more.

data engineering books,The Data Warehouse Toolkit

You can grab a copy of it here – Amazon.com

5. Learning Spark by Holden Karau

Data is being generated in huge volumes today, a scale we can only imagine. So much data plays a vital role in increasing the complexity of operations and that has sparked new developments in the field of data engineering.

This cracking book by Holden Karau offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.

Dive into the world of innovations in the way you acquire and massage the data, the ultimate goal is to get the best and most classified data for your machine learning model. Spark is the most effective data processing framework in enterprises today.

data engineering books, Learning Spark

Get a copy today!! – Amazon.com

6. Spark: The Definitive Guide: Big Data Processing Made Simple by Bill Chambers 

Data Engineering is a multi-disciplinary field with applications in control, decision theory, and the emerging hot area of bioinformatics. There are no books on the market that make the subject accessible to non-experts.

So, if you are just starting off and need a good book to learn everything about data engineering, then Spark, a fast cluster computing framework that is used for processing, querying and analyzing big data, is the tool that you should learn and this is your book to read.

All the theory and practical concepts are explained in a user-friendly manner and easy to understand language.

data engineering books, Spark The definitive Guide

Grab a copy today at Amazon.com

7. Big Data: Principles and best practices of scalable realtime data systems by Nathan Marz

This book is for managers, advisors, consultants, specialists, professionals, and anyone interested in Data Engineering assessment.

It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they’re built.

So, if you are the CEO/CXO of an organization and want to introduce the Data Engineering practice into your organization, then you should grab this book and access the data engineering pattern of your business.

data engineering books, Big Data

Grab a copy here – Amazon.com

8. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

The concepts of this book revolve around the task of collecting data and distilling useful information from that data. Five discrete sections covered in this book are:

  • Data scalability
  • Consistency
  • Reliability
  • Efficiency, and
  • Maintainability

Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data.

data engineering books,Designing Data-Intensive Applications

Here is where you can source it from – Amazon.com

9. Big Data, Black Book: Covers Hadoop 2, MapReduce, Hive, YARN, Pig, R, and Data Visualization

The objective of this book is to create a new breed of versatile Big Data analysts and developers, who are thoroughly conversant with the basic and advanced analytic techniques for manipulating and analyzing data.

So, if you want to start learning about data engineering tools, then this book is a must-read. It holistically covers all the tools that help you meddle with data and craft strategies to gain a competitive edge.

data engineering books,Big Data , Black Book

Source your copy here – Amazon.com

Conclusion

Becoming a data engineer is not an easy task. It requires a deep understanding of tools, processes, and techniques to be able to extract the best out of any structured/ unstructured data.

You can sketch out a data engineering path for yourself by reading this exhaustive article – Want to Become a Data Engineer? Here’s a Comprehensive List of Resources to get started.

Hope you liked my collection of Data Engineering books! I would definitely like to know if there are any books you would recommend. Please share the names in the comments section below.

big data engineerbookdata engineerdata engineer booksData EngineeringData Science Books
Sneha Jain
Sneha Jain

The heart of every marketing campaign is great content and I love churning just that! I am a Data Science content marketing enthusiast. Exploring the field of applied Artificial Intelligence and Machine Learning and consistently being involved in editing the content at Analytics Vidhya is how I spend my day. I have always been fueled by the passion to do something different. The core of me is always eager to explore and learn more and more each day not only in the field of Data Science but also in the field of Psychology.

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Responses From Readers

Clear

Fabio Valeri
Fabio Valeri

Thank you very much for this list. I suppose that this list is adressed for computer scientist, software engineers, and professional programmers who knows several programming languages. For statisticians and mathematicians who want to become data scientist and implement a DWH I recommend the book of Djoni Darmawikarta: Dimensional Data Warehousing with MySQL. I know the books of Inmon, Kimball and Corr but they are very theoretical, conceptually and often redundant. The book of Darmawikarte shows how to implement a DWH with SQL and shored procedure and does also provide the code to do that and this in a way that it works. You learn DWH-concepts by coding.

123

Pranav Dar
Pranav Dar

Hi Fabio, Thanks for recommending these books! We will be sure to check them out and share them with the community. Thanks, Pranav

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