Data Science on the Google Cloud Platform

Preface

In my current role at Google, I get to work alongside data scientists and data engineers in a variety of industries as they move their data processing and analysis methods to the public cloud. Some try to do the same things they do on-premises, the same way they do them, just on rented computing resources. The visionary users, though, rethink their systems, transform how they work with data, and thereby are able to innovate faster.

As early as 2011, an article in Harvard Business Review recognized that some of cloud computing’s greatest successes come from allowing groups and communities to work together in ways that were not previously possible. This is now much more widely recognized—an MIT survey in 2017 found that more respondents (45%) cited increased agility rather than cost savings (34%) as the reason to move to the public cloud.

In this book, we walk through an example of this new transformative, more collaborative way of doing data science. You will learn how to implement an end-to-end data pipeline—we will begin with ingesting the data in a serverless way and work our way through data exploration, dashboards, relational databases, and streaming data all the way to training and making operational a machine learning model. I cover all these aspects of data-based services because data engineers will be involved in designing the services, developing the statistical and machine learning models and implementing them in large-scale production and in real time.

Who This Book Is For

If you use computers to work with data, this book is for you. You might go by the title of data analyst, database administrator, data engineer, data scientist, or systems programmer today. Although your role might be narrower today (perhaps you do only data analysis, or only model building, or only DevOps), you want to stretch your wings a bit—you want to learn how to create data science models as well as how to implement them at scale in production systems.

Google Cloud Platform is designed to make you forget about infrastructure. The marquee data services—Google BigQuery, Cloud Dataflow, Cloud Pub/Sub, and Cloud ML Engine—are all serverless and autoscaling. When you submit a query to BigQuery, it is run on thousands of nodes, and you get your result back; you don’t spin up a cluster or install any software. Similarly, in Cloud Dataflow, when you submit a data pipeline, and in Cloud Machine Learning Engine, when you submit a machine learning job, you can process data at scale and train models at scale without worrying about cluster management or failure recovery. Cloud Pub/Sub is a global messaging service that autoscales to the throughput and number of subscribers and publishers without any work on your part. Even when you’re running open source software like Apache Spark that’s designed to operate on a cluster, Google Cloud Platform makes it easy. Leave your data on Google Cloud Storage, not in HDFS, and spin up a job-specific cluster to run the Spark job. After the job completes, you can safely delete the cluster. Because of this job-specific infrastructure, there’s no need to fear overprovisioning hardware or running out of capacity to run a job when you need it. Plus, data is encrypted, both at rest and in transit, and kept secure. As a data scientist, not having to manage infrastructure is incredibly liberating.

The reason that you can afford to forget about virtual machines and clusters when running on Google Cloud Platform comes down to networking. The network bisection bandwidth within a Google Cloud Platform datacenter is 1 PBps, and so sustained reads off Cloud Storage are extremely fast. What this means is that you don’t need to shard your data as you would with traditional MapReduce jobs. Instead, Google Cloud Platform can autoscale your compute jobs by shuffling the data onto new compute nodes as needed. Hence, you’re liberated from cluster management when doing data science on Google Cloud Platform.

These autoscaled, fully managed services make it easier to implement data science models at scale—which is why data scientists no longer need to hand off their models to data engineers. Instead, they can write a data science workload, submit it to the cloud, and have that workload executed automatically in an autoscaled manner. At the same time, data science packages are becoming simpler and simpler. So, it has become extremely easy for an engineer to slurp in data and use a canned model to get an initial (and often very good) model up and running. With well-designed packages and easy-to-consume APIs, you don’t need to know the esoteric details of data science algorithms—only what each algorithm does, and how to link algorithms together to solve realistic problems. This convergence between data science and data engineering is why you can stretch your wings beyond your current role.

Rather than simply read this book cover-to-cover, I strongly encourage you to follow along with me by also trying out the code. The full source code for the end-to-end pipeline I build in this book is on GitHub. Create a Google Cloud Platform project and after reading each chapter, try to repeat what I did by referring to the code and to the README.md¹ file in each folder of the GitHub repository.

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Acknowledgments

When I took the job at Google about a year ago, I had used the public cloud simply as a way to rent infrastructure—so I was spinning up virtual machines, installing the software I needed on those machines, and then running my data processing jobs using my usual workflow. Fortunately, I realized that Google’s big data stack was different, and so I set out to learn how to take full advantage of all the data and machine learning tools on Google Cloud Platform.

The way I learn best is to write code, and so that’s what I did. When a Python meetup group asked me to talk about Google Cloud Platform, I did a show-and-tell of the code that I had written. It turned out that a walk-through of the code to build an end-to-end system while contrasting different approaches to a data science problem was quite educational for the attendees. I wrote up the essence of my talk as a book proposal and sent it to O’Reilly Media.

A book, of course, needs to have a lot more depth than a 60-minute code walk-through. Imagine that you come to work one day to find an email from a new employee at your company, someone who’s been at the company less than six months. Somehow, he’s decided he’s going to write a book on the pretty sophisticated platform that you’ve had a hand in building and is asking for your help. He is not part of your team, helping him is not part of your job, and he is not even located in the same office as you. What is your response? Would you volunteer?

What makes Google such a great place to work is the people who work here. It is a testament to the company’s culture that so many people—engineers, technical leads, product managers, solutions architects, data scientists, legal counsel, directors—across so many different teams happily gave of their expertise to someone they had never met (in fact, I still haven’t met many of these people in person). This book, thus, is immeasurably better because of (in alphabetical order) William Brockman, Mike Dahlin, Tony Diloreto, Bob Evans, Roland Hess, Brett Hesterberg, Dennis Huo, Chad Jennings, Puneith Kaul, Dinesh Kulkarni, Manish Kurse, Reuven Lax, Jonathan Liu, James Malone, Dave Oleson, Mosha Pasumansky, Kevin Peterson, Olivia Puerta, Reza Rokni, Karn Seth, Sergei Sokolenko, and Amy Unruh. In particular, thanks to Mike Dahlin, Manish Kurse, and Olivia Puerta for reviewing every single chapter. When the book was in early access, I received valuable error reports from Anthonios Partheniou and David Schwantner. Needless to say, I am responsible for any errors that remain.

A few times during the writing of the book, I found myself completely stuck. Sometimes, the problems were technical. Thanks to (in alphabetical order) Ahmet Altay, Eli Bixby, Ben Chambers, Slava Chernyak, Marian Dvorsky, Robbie Haertel, Felipe Hoffa, Amir Hormati, Qi-ming (Bradley) Jiang, Kenneth Knowles, Nikhil Kothari, and Chris Meyers for showing me the way forward. At other times, the problems were related to figuring out company policy or getting access to the right team, document, or statistic. This book would have been a lot poorer had these colleagues not unblocked me at critical points (again in alphabetical order): Louise Byrne, Apurva Desai, Rochana Golani, Fausto Ibarra, Jason Martin, Neal Mueller, Philippe Poutonnet, Brad Svee, Jordan Tigani, William Vampenebe, and Miles Ward. Thank you all for your help and encouragement.

Thanks also to the O’Reilly team—Marie Beaugureau, Kristen Brown, Ben Lorica, Tim McGovern, Rachel Roumeliotis, and Heather Scherer for believing in me and making the process of moving from draft to published book painless.

Finally, and most important, thanks to Abirami, Sidharth, and Sarada for your understanding and patience even as I became engrossed in writing and coding. You make it all worthwhile.

¹ For example, see https://github.com/GoogleCloudPlatform/data-science-on-gcp/blob/master/06_dataproc/README.md.