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Book details
Paperback: 490 pages
Format: epub
Size: 12.94 MB
Publisher: Packt Publishing (July 31, 2018)
Language: English
ISBN-10: 178883996X
ISBN-13: 978-1788839969
“The book contains two logical parts of roughly equal length. In the first half, I lay down the theme of the book which is the need to bridge the gap between data science and engineering, including in-depth details about the Jupyter + PixieDust solution I’m proposing. The second half is dedicated to applying what we learned in the first half, to four industry cases.
Chapter 1, Perspectives on Data Science from a Developer, I attempt to provide a definition of data science through the prism of my own experience, building a data pipeline that performs sentiment analysis on Twitter posts. I defend the idea that it is a team sport and that most often, silos exist between the data science and engineering teams that cause unnecessary friction, inefficiencies and, ultimately, a failure to realize its full potential. I also argue the point of view that data science is here to stay and that eventually, it will become an integral part of what is known today as computer science (I like to think that someday new terms will emerge, such as computer data science that better capture this duality).
“The book contains two logical parts of roughly equal length. In the first half, I lay down the theme of the book which is the need to bridge the gap between data science and engineering, including in-depth details about the Jupyter + PixieDust solution I’m proposing. The second half is dedicated to applying what we learned in the first half, to four industry cases.
Chapter 2, Data Science at Scale with Jupyter Notebooks and PixieDust, I start diving into popular data science tools such as Python and its ecosystem of open-source libraries dedicated to data science, and of course Jupyter Notebooks. I explain why I think Jupyter Notebooks will become the big winner in the next few years. I also introduce the PixieDust open-source library capabilities starting from the simple display() method that lets the user visually explore data in an interactive user interface by building compelling charts. With this API, the user can choose from multiple rendering engines such as Matplotlib, Bokeh, Seaborn, and Mapbox. The display() capability was the only feature in the PixieDust MVP (minimum viable product) but, over time, as I was interacting with a lot of data science practitioners, “I added new features to what would quickly become the PixieDust toolbox:
sampleData(): A simple API for easily loading data into pandas and Apache Spark DataFrames
wrangle_data(): A simple API for cleaning and massaging datasets. This capability includes the ability to destructure columns into new columns using regular expressions to extract content from unstructured text. The wrangle_data() API can also make recommendations based on predefined patterns.
PackageManager: Lets the user install third-party Apache Spark packages inside a Python Notebook.
Scala Bridge: Enables the user to run the Scala code inside a Python Notebook. Variables defined in the Python side are accessible in Scala and vice-versa.
Spark Job Progress Monitor: Lets you track the status of your Spark Job with a real-time progress bar that displays directly in the output cell of the code being executed.
PixieApp: Provides a programming model centered around HTML/CSS that lets developers build sophisticated dashboards to operationalize the analytics built in the Notebook. PixieApps can run directly in the Jupyter Notebook or be deployed as analytic web applications using the PixieGateway microservice. PixieGateway is an open-source companion project to PixieDust.
