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 Ingestion

In this chapter, we will look into the data ingestion aspects of the data life cycle. Data ingestion is a very broad term; however, we will concentrate on some of the most important aspects of it. Data is generally considered to be ingested when it is ready for usage in enterprise systems. In reality, a significant amount of effort is required to perform data ingestion effectively and efficiently.

Data ingestion typically involves the following key tasks, which are also the topics that will be covered in this chapter:

  • Data extraction
  • Data staging
  • Data cleaning
  • Data normalization
  • Data enrichment
  • Data organization and storage

At times, there are more tasks involved as part of data ingestion. There are also situations where some of these tasks might not be necessary and two or more could be combined into a single task.