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Data Lake Development with Big Data
Data Lake Development with Big Data

Data Lake Development with Big Data: Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies

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Profile Icon Pasupuleti Profile Icon Beulah Salome Purra
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€18.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (8 Ratings)
Paperback Nov 2015 164 pages 1st Edition
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Arrow left icon
Profile Icon Pasupuleti Profile Icon Beulah Salome Purra
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€18.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (8 Ratings)
Paperback Nov 2015 164 pages 1st Edition
eBook
€8.99 €23.99
Paperback
€29.99
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Free Trial
Renews at €18.99p/m
eBook
€8.99 €23.99
Paperback
€29.99
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Renews at €18.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

Data Lake Development with Big Data

Chapter 2. Data Intake

In the preceding chapter, you understood the need for Data Lake and gained a high-level understanding of the key components that can comprise a Data Lake and how important each of these components are for building it. We have seen how the changing business landscape is provoking data growth and how organizations are adopting a newer paradigm such as Data Lake to ingest this data and extract analytical value.

In this chapter, you will understand in detail, the Intake Tier that was introduced in Chapter 1, The Need for Data Lake. The following topics will be covered in this chapter:

  • The process of obtaining data into the Data Lake's Intake Tier
  • An high-level overview of the various External Data Sources from which the data can be acquired, and the variety of data that can be ingested
  • The key functionalities that can be implemented as part of the Data Intake Tier
  • The various data intake modes
  • Big Data tools and technologies that can be used to acquire a variety...

Understanding Intake tier zones

Enterprises sit on vast reserves of diverse, potentially invaluable data such as databases, social media, logs, and sensor data locked in data silos. The Data Lake is schema-less and stores data of any type and format. Not only storing, but offering the ability to integrate data from disparate sources and ingest high-velocity, multi-structured and massive datasets. This key feature empowers enterprises to perform exploratory and advanced data analysis on all the data and quickly gain actionable insights.

Before the Data Lake is utilized to its hilt to perform data analysis, there should be mechanisms implemented to seamlessly connect the Data Lake to various external data sources and acquire data from them. The Intake Tier in the Data Lake architecture implements the functionalities needed to address this. In the following subsection, let us study the Intake Tier in detail.

Let us start by understanding the data flow from the External Data Sources to the...

Architectural guidance

This section attempts to answer one key question: which tool should be used for which use case and how do we decide the best option?

As evidenced in the previous sections, there are a plethora of options available for establishing connectivity to External Data Sources and Ingesting data into the Data Lake's Intake Tier.

Choosing a tool for ingestion depends primarily on the use case you are attempting to implement using the Data Lake. Many implementations of Data Lake end up using multiple tools together to acquire and process data. We also see that the market is flooded with umpteen numbers of tools that make decision making very difficult.

In this section, we will try to provide a crisp overview of the things to be considered while selecting a tool. This is by no means an exhaustive coverage, but it tries to give you enough depth so that you will be in a decent position to extrapolate using this knowledge and make better decisions.

The choice of the tool invariably...

Summary

This chapter explained the Data Intake layer in detail; we started with understanding the various zones in the Intake tier and the external sources from which the data can be acquired as per your use case. We then took a deep dive into the functionalities of the Source System Zone, Transient landing Zone, and the Raw Zone and also comprehended the best practices that can be considered while architecting the Data Intake tier.

In the subsequent sections, we took a look at the various Big Data tools and technologies that can be used to acquire different types of data from various sources. The architectural guidance section helped you in decision making in order to arrive at the set of technologies that can be used for specific use cases.

In the next chapter, you will understand the Data Integration, Quality, and Enrichment zones; it will take you through the key functionalities of these zones and provides architectural guidance on how to go about implementing these zones.

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Key benefits

  • 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

Description

A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. 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. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on 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.

Who is this book 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 and information lifecycle management, and experience of Big Data technologies.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 26, 2015
Length: 164 pages
Edition : 1st
Language : English
ISBN-13 : 9781785888083
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Languages :
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Publication date : Nov 26, 2015
Length: 164 pages
Edition : 1st
Language : English
ISBN-13 : 9781785888083
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Languages :
Concepts :
Tools :

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Table of Contents

6 Chapters
1. The Need for Data Lake Chevron down icon Chevron up icon
2. Data Intake Chevron down icon Chevron up icon
3. Data Integration, Quality, and Enrichment Chevron down icon Chevron up icon
4. Data Discovery and Consumption Chevron down icon Chevron up icon
5. Data Governance Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6
(8 Ratings)
5 star 12.5%
4 star 62.5%
3 star 12.5%
2 star 0%
1 star 12.5%
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Mayank Arora Nov 19, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good to read..
Amazon Verified review Amazon
Ravi Sep 17, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book is easy to read and is useful for beginners.
Amazon Verified review Amazon
Mukesh Rao Oct 24, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good introductory book on Data Lakes.
Amazon Verified review Amazon
Vikram Koka Sep 28, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Well written summary of emerging concept. The narration explains theoretical concepts in detail.The book would have gained more value, had the authors focused on the actual tool set used at various stages.
Amazon Verified review Amazon
Abhishek shukla Sep 18, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Nice book to understand data lake with new ideas
Amazon Verified review Amazon
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