Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Table of Contents (12 chapters)

Data masking

Businesses that deal with customer data have to make sure that the PII (personally identifiable information) of these customers is not moving freely around the entire data pipeline. This criterion is applicable not only to customer data but also to any other type of data that is considered classified, as per standards such as GDPR, SOX, and so on. In order to make sure that we protect the privacy of customers, employees, contractors, and vendors, we need to take the necessary precautions to ensure that when the data goes through several pipelines, users of the data see only anonymized data. The level of anonymization we do depends upon the standards the company adheres to and also the prevailing country standards.

So, data masking can be called the process of hiding/transforming portions of original data with other data without losing the meaning or context.

In this...