Book Image

Python Social Media Analytics

By : Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk
Book Image

Python Social Media Analytics

By: Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

Overview of this book

Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Different scaling methods and platforms

Let us look at some scaling methods on different platforms in detail in the following sections.

Parallel computing

Before the arrival of advanced systems, such as Hadoop or Spark, developers had to handle the problem of horizontal scaling. What are the methods they used?

The most basic form of horizontal scaling is multi-threading or multi-processing. These two approaches are similar, since both use multiple threads on a single machine to break the data into chunks and then execute the computation in parallel. The typical difference between a thread and a process is that threads (of the same process) run in a shared memory space, while processes run in separate memory spaces.

Parallel computation has one fundamental limitation: it is restricted by the resources of the single machine.

Let's see a hands-on example of parallel computation. For this we will use Python's native multiprocessing library.

from multiprocessing import Pool 
def f(x):