Data engineers are the unsung heroes of the data world.
data scientists and analysts soak up the glitz and glamor while engineers are in the belly of the beast, soldering pipes and making sure the data pumped through the system is clean and of high quality.
You are reading: Best data engineering books
here is a quote that captures the data engineer utility command:
“data engineers are the plumbers who build a data pipeline, while data scientists are the painters and storytellers who make sense of a static entity”: david bianco of urthecast.
It’s easy to get sidetracked from data science and analytics, but quality data engineering is absolutely essential.
data engineers, never fear your time is up, because it has only just begun! In fact, the Tech Job Says 2020 report found that the demand for data engineers had increased by 50%, faster than any other job in the tech industry.
With the demand for data professionals seemingly constantly on the rise, there’s no better team to get involved in a career in data, and if you’re interested in the data engineering side of things, then you’re in luck.
To get you started, here are 15 of the best data engineering books. Founder and CEO of Vidhya Analytics, Mr. Kunal Jain says that he reads a book a week. successful people read and read a lot, not just online, but from the humble book.
Check out this post on 15 of the best data science and analytics books for even more books on data!
data intensive application design – martin kleppman
With a colossal number of reviews on Amazon for a seemingly specialized book, Designing Data-Intensive Applications provides a fundamental overview of data engineering in a modern big data context. Many data tools, methods and processes are covered, detailing everything from data collection and storage to data cleansing and transformation for use in a number of modern tools and platforms. The book covers key topics such as data storage and storage, structures, distributed systems, batch and stream processing, coding, replication, partitioning, and much more. the goal is to break down terminology and buzzwords and provide an in-context view of data tools in action. is probably the best selling book on data engineering and data science today.
data genres:
- data engineering
- data warehousing
- big data
- data cleansing and transformation
suitable for:
- Anyone interested in the engineering or practical side of data.
spark: the ultimate guide: big data processing made easy – bill chambers, matei zaharia
apache spark is a machine learning and big data analytics engine. spark provides apis in java, scala, python, and r, and this book teaches a lot about how to use spark in various business or organizational contexts.
Although it has a fairly specific focus, this is an excellent book for anyone interested in a data engineering or data science job that involves working with spark. It covers everything from clustering to debugging, as well as Spark’s excellent stream processing engine. it is a high level book for those who use spark or intend to in the future. published in 2018, it promises to remain relevant for a long time yet.
data genres:
- data engineering
- big data
- machine learning
- apache spark
suitable for:
- anyone who is already using apache spark or intends to use it in the future.
snowflake cookbook – hamid mahmood qureshi and hammad sharif
snowflake, the unique cloud-based all-in-one data storage platform, is extremely popular. Snowflake has raised a colossal amount of money since forming him a few years ago, mainly because he was simply outselling himself. it has become an option for SMEs and enterprises looking to centralize and scale their data strategies.
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This book delves into the snowflake and is a great introduction for anyone wanting to work with the tool. takes readers through snowflake’s advanced scalable virtual storage capabilities, processes queries and sql statements, and leverages its internal and external integrations to perform nearly limitless analytics. The book provides a solid foundation for working on Snowflake in a professional capacity.
data genres:
- data engineering
- snowflake
- cloud data storage
- data pipelines
suitable for:
- Anyone looking for an introduction to snowflake, or anyone looking to consolidate their knowledge on the data platform
the data warehouse toolkit: the definitive guide to dimensional modeling – ralph kimball, margy ross
Dimensional data models were developed by Ralph Kimball and this book has become basic reading on the subject. the key components of dimensional modeling are facts, dimensions, and attributes; this modeling technique for data warehousing has become deeply embedded in the fabric of modern data modeling culture.
This book is on the reading list of many data-related courses and curricula. covers a multitude of topics related to data modeling techniques, storage, transformation, cleaning and etl. there are 12 case studies of varying complexity. Tons of real-world examples are given, covering everything from inventory management and procurement to accounting, CRM, and big data analytics.
data genres:
- big data engineering
- data modeling
- elt pipelines
- business intelligence
suitable for:
- anyone looking for a contextualized guide to data modeling and warehousing
a great book for anyone applying for a position involving snowflake, or looking to add snowflake experience to their resume.
data engineering with python – paul crickard
python is the main programming language for data, and of that, there is no doubt! this book fills a huge niche then, as it teaches how to do data engineering with python, which is what a lot of people want or need to learn. Released in 2020, it is fully up to date and covers everything from etl using python to programming, automating and monitoring complex data pipelines. provides guidelines on data architecture construction, using real-world case studies to guide the reader.
This is a real data engineering oriented book that focuses on building strong foundations for data science and data analysis. there is tons of useful practical information in there that promises to be of almost immediate use to anyone working in data engineering now or in the future. no prior knowledge of data engineering is required, but there is also some high-level stuff.
data genres
- data engineering
- etl pipelines
- python for data
- data cleansing and enrichment
suitable for:
- those looking for a solid basic understanding of how to design data engineering using python
data pipelines pocket reference: moving and processing data for analytics – james densmore
A super little book on data pipelines, this pocket-sized reference is packed with information on how to build and implement successful data pipelines in real-world contexts. covers some useful conundrums for data engineers to solve, e.g. lot vs. ingest streaming data and build vs. buy. focuses on modern data tools and platforms, e.g. using data pipelines to extract, transform, and load data into cloud-based platforms.
This compact and well-designed back is packed with excellent diagrams and contextualized examples. It covers key areas like ensuring data quality, testing pipelines before deployment, and a few other overlooked areas. A must have for any established or budding data engineer.
data genres:
- data pipelines
- data storage
- data cleansing and transformation
- etl and elt
suitable for:
- anything and everything that works with data pipelines
97 things every data engineer should know: collective wisdom from the experts – tobias macey
Edited by respected data engineer Tobias Macey, this book was published very recently when this article was written (published June 2021). is full of subtopics, each of which acts as a kind of mini-lecture or seminar on data engineering:
- the importance of data lineage – julien le dem
- data security for data engineers – katharine jarmul
- the two types of data engineers and data engineers data – jesse anderson
- six dimensions for choosing an analytical data warehouse – gleb mezhanskiy
- the end of etl as we know it – paul singman
- building an career as a data engineer – vijay kiran
- modern metadata for the modern data stack: prukalpa sankar
- your data tests failed! now what? – sam bail
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Think of it as a wisdom book of facts and anecdotes. it’s very current and up-to-date, so it really should be packed with immediately useful and applicable concepts, ideas, strategies, and processes. a very interesting looking book that has a wide professional scope.
data genres:
- it seems to cover almost everything!
suitable for:
- anyone looking for pearly wisdom explained by some of the world’s top data professionals
python data cleanup cookbook – michael walker
One of the first things that pops into your head when working with data is garbage in = garbage out. cleaning and enriching data is incredibly important and can really make or break an entire project, or worse, a career. That’s partly why Michael Walker wrote this book: to explore the myriad of data cleaning techniques available with Python.
Some of the specific data cleaning techniques it covers are deduplication, handling of missing values, monitoring of particularly high volumes of data, error validation, handling of outliers, and handling of errors. invalid dates. It also explores how to discover unexpected values and classification errors through visualization and exploratory data analysis. This is the perfect book for anyone dealing with large volumes of messy or dirty data.
data genres:
- data cleaning
- data wrangling
- data engineering with python
- exploratory data analysis (EDA)
suitable for:
- those looking to learn about data cleansing with python
data governance disruption: a call to action – laura madsen
Here’s something slightly different and possibly tangential to data engineering itself, but still very relevant to the field. Data governance, the act and method of managing the flow of data in and out of a business or organization with respect to key stakeholders and regulations, is a rapidly changing topic.
Organizations are now moving towards universal data literacy, that is, training large numbers of employees on how to work and manage data independently of monolithic IT departments. gdpr and other regulations have added a new layer of complexity to data governance. This book is an exploration of this complex topic, likely aimed at C-level executives. its goal is to educate on how to govern data in the modern age.
data genres:
- data governance
- data democratization
- data management
- cybersecurity and privacy
suitable for:
- C-Level Data Executives and Other Senior Managers
data-driven science and engineering – steven l. brunton and nathan kutz
With a focus on scientific computing and how data has revolutionized our technological approach to understanding everything from turbulence and the brain to weather, environmental epidemiology, finance and robotics, this book is a comprehensive guide to the forefront of data.
The book covers many areas within data science and engineering, such as data mining, dimensional reduction, applied optimization, machine learning, and artificial intelligence, and is likely to be aimed at researchers and senior practitioners. level.
There are extensive technical diagrams in this fairly large book, it is an excellent fusion between theory and practice that is not afraid to pay attention to the limits of data science and data engineering.
data genres:
- machine learning
- applied optimization
- scientific computing
- data science and engineering
suitable for:
- anyone with a strong academic or intellectual interest in cutting-edge data and scientific computing
summary: top 10 data engineering books
here are some excellent books on data engineering, some of which inevitably cross the spheres of data science and data analysis, among other disciplines. While we take the internet for granted, books will forever remain an invaluable source of information.
The tactile nature of a book can’t be changed by any other means, but combining the subject matter of the data with the format of a book really works. !
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