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Data Lake Development with Big Data电子书

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9.8

作者：Pradeep Pasupuleti

出版社：Packt Publishing

出版时间：2015-11-26

字数：383.2万

所属分类：进口书 > 外文原版书 > 电脑/网络

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Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management, information lifecycle management, data governance, data product design, data engineering, and systems architecture. Also required is experience of Big Data technologies such as Hadoop, Spark, Splunk, and Storm. What You Will Learn Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios Find out the key considerations to be taken into account while building each tier of the Data Lake Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies Enable data discovery on the Data Lake to allow users to discover the data Discover how data is packaged and provisioned for consumption Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. It eliminates the need for up-front modeling and rigid data structures by allowing schema-less writes. Data Lakes make it possible to ask complex far-reaching questions to find out hidden data patterns and relationships. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications such as Spark, Storm, Hive, and so on, to create an environment in which data from different sources can be meaningfully brought together and analyzed. Data Lakes can be viewed as having three capabilities—intake, management, and consumption. This book will take readers through each of these processes of developing a Data Lake and guide them (using best practices) in developing these capabilities. It will also explore often ignored, yet crucial considerations while building Data Lakes, with the focus on how to architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. You will be able to utilize Data Lakes for efficient and easy data processing and analytics. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake.

目录展开

Data Lake Development with Big Data

Table of Contents

Data Lake Development with Big Data

Credits

About the Authors

Acknowledgement

About the Reviewer

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Errata

Piracy

Questions

1. The Need for Data Lake

Before the Data Lake

Need for Data Lake

Defining Data Lake

Key benefits of Data Lake

Challenges in implementing a Data Lake

When to go for a Data Lake implementation

Data Lake architecture

Architectural considerations

Architectural composition

Architectural details

Understanding Data Lake layers

The Data Governance and Security Layer

The Information Lifecycle Management layer

The Metadata Layer

Understanding Data Lake tiers

The Data Intake tier

The Source System Zone

The Transient Zone

The Raw Zone

Batch Raw Storage

The real-time Raw Storage

The Data Management tier

The Integration Zone

The Enrichment Zone

The Data Hub Zone

The Data Consumption tier

The Data Discovery Zone

The Data Provisioning Zone

Summary

2. Data Intake

Understanding Intake tier zones

Source System Zone functionalities

Understanding connectivity processing

Understanding Intake Processing for data variety

Structured data

The need for integrating Structured Data in the Data Lake

Structured data loading approaches

Semi-structured data

The need for integrating semi-structured data in the Data Lake

Semi-structured data loading approaches

Unstructured data

The need for integrating Unstructured data in the Data Lake

Unstructured data loading approaches

Transient Landing Zone functionalities

File validation checks

File duplication checks

File integrity checks

File size checks

File periodicity checks

Data Integrity checks

Checking record counts

Checking for column counts

Schema validation checks

Raw Storage Zone functionalities

Data lineage processes

Watermarking process

Metadata capture

Deep Integrity checks

Bit Level Integrity checks

Periodic checksum checks

Security and governance

Information Lifecycle Management

Practical Data Ingestion scenarios

Architectural guidance

Structured data use cases

Semi-structured and unstructured data use cases

Big Data tools and technologies

Ingestion of structured data

Sqoop

Use case scenarios for Sqoop

WebHDFS

Use case scenarios for WebHDFS

Ingestion of streaming data

Apache Flume

Use case scenarios for Flume

Fluentd

Use case scenarios for Fluentd

Kafka

Use case scenarios for Kafka

Amazon Kinesis

Use case scenarios for Kinesis

Apache Storm

Use case scenarios for Storm

Summary

3. Data Integration, Quality, and Enrichment

Introduction to the Data Management Tier

Understanding Data Integration

Introduction to Data Integration

Prominent features of Data Integration

Loosely coupled Integration

Ease of use

Secure access

High-quality data

Lineage tracking

Practical Data Integration scenarios

The workings of Data Integration

Raw data discovery

Data quality assessment

Profiling the data

Data cleansing

Deletion of missing, null, or invalid values

Imputation of missing, null, or invalid values

Data transformations

Unstructured text transformation techniques

Structured data transformations

Data enrichment

Collect metadata and track data lineage

Traditional Data Integration versus Data Lake

Data pipelines