In this chapter, we looked at working with data at scale. Working with large datasets requires a paradigm shift in how the data is processed. Traditional methods that work with smaller datasets generally don't work well with large datasets, because these are designed to work on a single computer. These methods need to be re-engineered to work effectively with large datasets. For scalability, we need to turn to distributed computing; however, this introduces significant additional complexity because of the network being involved, where failures are more common. Using good, time-tested frameworks, such as Apache Spark, is the key to addressing these concerns.
Hands-On Data Analysis with Scala
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Hands-On Data Analysis with Scala
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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)
Preface
Free Chapter
Section 1: Scala and Data Analysis Life Cycle
Scala Overview
Data Analysis Life Cycle
Data Ingestion
Data Exploration and Visualization
Applying Statistics and Hypothesis Testing
Section 2: Advanced Data Analysis and Machine Learning
Introduction to Spark for Distributed Data Analysis
Traditional Machine Learning for Data Analysis
Section 3: Real-Time Data Analysis and Scalability
Near Real-Time Data Analysis Using Streaming
Working with Data at Scale
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