Book Image

Hands-On Data Analysis with Scala

By : Rajesh Gupta
Book Image

Hands-On Data Analysis with Scala

By: Rajesh Gupta

Overview of this book

Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights
Table of Contents (14 chapters)
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Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Data staging

Once the data is extracted or delivered, it is generally staged into temporary storage for further processing. It is generally a good idea to keep data extraction/delivery storage separate from staging storage, although there are instances where this won't be necessary.

The staging area cleanly separates the following two aspects of the data ingestion process:

  • Data that has been extracted or delivered
  • New data that has to be processed

Once the data is staged completely, when we reach the further processing steps, such as cleaning, we do not have to be concerned about new data arriving. We can think of staged data as something that, once created, never changes and is immutable. This means that, to an already staged piece of data, no more data can be added, deleted, or modified. This is a very important data property that is necessary for reliable data processing...