Lakshmanan - Data Science on the Google Cloud Platform

• 2017-12-12: First Release

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 bityou 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 servicesGoogle BigQuery, Cloud Dataflow, Cloud Pub/Sub, and Cloud ML Engineare 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 dont 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 youre running open source software like Apache Spark thats 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, theres 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 dont 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, youre 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 scalewhich 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 dont need to know the esoteric details of data science algorithmsonly 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 file in each folder of the GitHub repository .