Book Image

Hands-On Data Analysis with Scala

By : Gupta
Book Image

Hands-On Data Analysis with Scala

By: 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)
Free Chapter
1
Section 1: Scala and Data Analysis Life Cycle
7
Section 2: Advanced Data Analysis and Machine Learning
10
Section 3: Real-Time Data Analysis and Scalability

Reliability considerations

Processing large datasets requires reliability to be looked at from a slightly different point of view. It is quite common to have a small percentage of errors in such large datasets. An acceptable error tolerance level can only be defined by business rules. Large datasets are generally processed by a network of computers, where failures are more common compared to processing on a single computer. In this section, we will look at the following aspects of error handling:

  • Input data errors
  • Processing failures

Input data errors

As a general guideline, it is crucial to measure and monitor the number of errors in the input data over time. If the quality of the input data is bad, then any analysis performed...