Chapter 1. Big Data Analytics at a 10,000-Foot View
The goal of this book is to familiarize you with tools and techniques using Apache Spark, with a focus on Hadoop deployments and tools used on the Hadoop platform. Most production implementations of Spark use Hadoop clusters and users are experiencing many integration challenges with a wide variety of tools used with Spark and Hadoop. This book will address the integration challenges faced with Hadoop Distributed File System (HDFS) and Yet Another Resource Negotiator (YARN) and explain the various tools used with Spark and Hadoop. This will also discuss all the Spark components—Spark Core, Spark SQL, DataFrames, Datasets, Spark Streaming, Structured Streaming, MLlib, GraphX, and SparkR and integration with analytics components such as Jupyter, Zeppelin, Hive, HBase, and dataflow tools such as NiFi. A real-time example of a recommendation system using MLlib will help us understand data science techniques.
In this chapter, we will approach Big Data analytics from a broad perspective and try to understand what tools and techniques are used on the Apache Hadoop and Apache Spark platforms.
Big Data analytics is the process of analyzing Big Data to provide past, current, and future statistics and useful insights that can be used to make better business decisions.
Big Data analytics is broadly classified into two major categories, data analytics and data science, which are interconnected disciplines. This chapter will explain the differences between data analytics and data science. Current industry definitions for data analytics and data science vary according to their use cases, but let's try to understand what they accomplish.
Data analytics focuses on the collection and interpretation of data, typically with a focus on past and present statistics. Data science, on the other hand, focuses on the future by performing explorative analytics to provide recommendations based on models identified by past and present data.
Figure 1.1 explains the difference between data analytics and data science with respect to time and value achieved. It also shows typical questions asked and tools and techniques used. Data analytics has mainly two types of analytics, descriptive analytics and diagnostic analytics. Data science has two types of analytics, predictive analytics and prescriptive analytics. The following diagram explains data science and data analytics:
Figure 1.1: Data analytics versus data science
The following table explains the differences with respect to processes, tools, techniques, skill sets, and outputs:
|
Data analytics |
Data science | |
|---|---|---|
|
Perspective |
Looking backward |
Looking forward |
|
Nature of work |
Report and optimize |
Explore, discover, investigate, and visualize |
|
Output |
Reports and dashboards |
Data product |
|
Typical tools used |
Hive, Impala, Spark SQL, and HBase |
MLlib and Mahout |
|
Typical techniques used |
ETL and exploratory analytics |
Predictive analytics and sentiment analytics |
|
Typical skill set necessary |
Data engineering, SQL, and programming |
Statistics, machine learning, and programming |
This chapter will cover the following topics:
- Big Data analytics and the role of Hadoop and Spark
- Big Data science and the role of Hadoop and Spark
- Tools and techniques
- Real-life use cases
