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

Cleaning and normalizing

Staged data is considered to be immutable. Immutability in this context implies that staged data, once created, never changes. Now, the data cleaning and normalizing process can start. This step could also involve determining the degree of errors in the data received. In particular, it is expected that big data will have a certain amount of errors.

Raw data coming from external sources comes in a variety of formats. These formats are generally designed for data delivery and are not suitable for use by systems consuming data. It is also very common for some of the information to be clubbed together as part of data delivery; however, the consumer of the data needs to have more fine-grained access to the information.

An example of this is the address part of the data. The data producer might provide a free-form address. The contained information, such as...