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Data Lake for Enterprises
Data Lake for Enterprises

Data Lake for Enterprises: Lambda Architecture for building enterprise data systems

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Profile Icon Mishra Profile Icon John Profile Icon Pankaj Misra
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.9 (8 Ratings)
Paperback May 2017 596 pages 1st Edition
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Arrow left icon
Profile Icon Mishra Profile Icon John Profile Icon Pankaj Misra
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€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.9 (8 Ratings)
Paperback May 2017 596 pages 1st Edition
eBook
€8.99 €22.99
Paperback
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Data Lake for Enterprises

Comprehensive Concepts of a Data Lake

The concept of a Data Lake in an enterprise was driven by certain challenges that enterprises were facing with the way the data was handled, processed and stored. Initially, all the individual applications in the enterprise, via a natural evolution cycle, started maintaining huge amounts of data themselves with almost no reuse in other applications in the same enterprise. These created information silos across various applications. As the next step of evolution, these individual applications started exposing this data across the organization as a data mart access layer over the central data warehouse. While Data Mart solved one part of the problem, other problems still persisted. These problems were more about data governance, data ownership and data accessibility, which were required to be resolved so as to have better availability of enterprise relevant data. This is where...

What is a Data Lake?

If we need to define the term Data Lake, it can be defined as a vast repository of a variety of enterprise-wide, raw information that can be acquired, processed, analyzed and delivered.

A Data Lake acquires data from multiple sources in an enterprise in its native form and may also have internal, modeled forms of this same data for various purposes. The information thus handled could be any type of information, ranging from structured or semi-structured data to completely unstructured data. A Data Lake is expected to be able to derive enterprise-relevant meanings and insights from this information using various analysis and machine learning algorithms.

Relevance to enterprises

A Data Lake brings a variety of capabilities to the enterprise by...

How does a Data Lake help enterprises?

Organizations have been aspiring for a long time to achieve a unified data model that can represent every entity in an enterprise. This has been a challenge due to various reasons, some of which have been listed here:

  • An entity may have multiple representations across the enterprise. Hence there may not exist a single and complete model for an entity.
  • Different enterprise applications may be processing the entities based on specific business objectives, which may or may not align with expected enterprise processes.
  • Different applications may have different access patterns and storage structures for every entity.

These issues have been bothering enterprises for a long time; limiting standardization of business processes, service definition and their vocabulary.

In Data Lake perspective, we are looking at the problem the other way around. Bringing Data Lake would mean...

How Data Lake works?

In order to realize the benefits of a Data Lake, it is important to know how a Data Lake may be expected to work and what components architecturally may help to build a fully functional Data Lake. Before we pounce on the architectural details, let us understand the life cycle of data in the context of a Data Lake.

At a high level, the life cycle of a data lake may be summarized as shown here:

Figure 01: Data Lake life cycle

These can also be called various stages of data as it lives within the Data Lake. The data thus acquired can be processed and analyzed in various ways. The processing and data analysis could be a batch process or it could even be a near-real-time process. Both of these kinds of processing are expected to be supported by a Data Lake implementation as both of these patterns serve very specific use cases. The choice between the type of processing and analysis (batch/near...

Differences between Data Lake and Data Warehouse

Many a times, Data Lakes are also perceived as Data Warehouses. Both Data Lake and Data Warehouse have different objectives to be achieved in an enterprise. Some of the key difference are shown here:

Data Lake Data Warehouse
Captures all types of data and structures, semi-structured and unstructured in their most natural form from source systems Captures structured information and processes it as it is acquired into a fixed model defined for data warehouse purposes
Possesses enough processing power to process and analyze all kinds of data and have it analyzed for access Processes structured data into a dimensional or reporting model for advanced reporting and analytics
A Data Lake usually contains more relevant information that has good probability of access and can provide operational needs for an enterprise A Data Warehouse usually stores and retains...

What is a Data Lake?


If we need to define the term Data Lake, it can be defined as a vast repository of a variety of enterprise-wide, raw information that can be acquired, processed, analyzed and delivered.

Note

A Data Lake acquires data from multiple sources in an enterprise in its native form and may also have internal, modeled forms of this same data for various purposes. The information thus handled could be any type of information, ranging from structured or semi-structured data to completely unstructured data. A Data Lake is expected to be able to derive enterprise-relevant meanings and insights from this information using various analysis and machine learning algorithms.

Relevance to enterprises

A Data Lake brings a variety of capabilities to the enterprise by centralizing the data. With data being centralized, the enterprise can tap into capabilities that have not yet been explored. This data can help enterprises with a lot more meaningful business insights when compared to any single...

How does a Data Lake help enterprises?


Organizations have been aspiring for a long time to achieve a unified data model that can represent every entity in an enterprise. This has been a challenge due to various reasons, some of which have been listed here:

  • An entity may have multiple representations across the enterprise. Hence there may not exist a single and complete model for an entity.
  • Different enterprise applications may be processing the entities based on specific business objectives, which may or may not align with expected enterprise processes.
  • Different applications may have different access patterns and storage structures for every entity.

These issues have been bothering enterprises for a long time; limiting standardization of business processes, service definition and their vocabulary.

In Data Lake perspective, we are looking at the problem the other way around. Bringing Data Lake would mean implicitly achieving a unified data model to a good extent without really impacting the business...

How Data Lake works?


In order to realize the benefits of a Data Lake, it is important to know how a Data Lake may be expected to work and what components architecturally may help to build a fully functional Data Lake. Before we pounce on the architectural details, let us understand the life cycle of data in the context of a Data Lake.

At a high level, the life cycle of a data lake may be summarized as shown here:

Figure 01: Data Lake life cycle

These can also be called various stages of data as it lives within the Data Lake. The data thus acquired can be processed and analyzed in various ways. The processing and data analysis could be a batch process or it could even be a near-real-time process. Both of these kinds of processing are expected to be supported by a Data Lake implementation as both of these patterns serve very specific use cases. The choice between the type of processing and analysis (batch/near-real-time) may also depend on the amount of processing or analysis to be performed...

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Key benefits

  • Build a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the base
  • Delve into the big data technologies required to meet modern day business strategies
  • A highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-cases

Description

The term "Data Lake" has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake.

Who is this book for?

Java developers and architects who would like to implement a data lake for their enterprise will find this book useful. If you want to get hands-on experience with the Lambda Architecture and big data technologies by implementing a practical solution using these technologies, this book will also help you.

What you will learn

  • Build an enterprise-level data lake using the relevant big data technologies
  • Understand the core of the Lambda architecture and how to apply it in an enterprise
  • Learn the technical details around Sqoop and its functionalities
  • Integrate Kafka with Hadoop components to acquire enterprise data
  • Use flume with streaming technologies for stream-based processing
  • Understand stream- based processing with reference to Apache Spark Streaming
  • Incorporate Hadoop components and know the advantages they provide for enterprise data lakes
  • Build fast, streaming, and high-performance applications using ElasticSearch
  • Make your data ingestion process consistent across various data formats with configurability
  • Process your data to derive intelligence using machine learning algorithms

Product Details

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Publication date : May 31, 2017
Length: 596 pages
Edition : 1st
Language : English
ISBN-13 : 9781787281349
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Publication date : May 31, 2017
Length: 596 pages
Edition : 1st
Language : English
ISBN-13 : 9781787281349
Vendor :
Apache
Category :
Languages :
Tools :

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Table of Contents

12 Chapters
Introduction to Data Chevron down icon Chevron up icon
Comprehensive Concepts of a Data Lake Chevron down icon Chevron up icon
Lambda Architecture as a Pattern for Data Lake Chevron down icon Chevron up icon
Applied Lambda for Data Lake Chevron down icon Chevron up icon
Data Acquisition of Batch Data using Apache Sqoop Chevron down icon Chevron up icon
Data Acquisition of Stream Data using Apache Flume Chevron down icon Chevron up icon
Messaging Layer using Apache Kafka Chevron down icon Chevron up icon
Data Processing using Apache Flink Chevron down icon Chevron up icon
Data Store Using Apache Hadoop Chevron down icon Chevron up icon
Indexed Data Store using Elasticsearch Chevron down icon Chevron up icon
Data Lake Components Working Together Chevron down icon Chevron up icon
Data Lake Use Case Suggestions Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.9
(8 Ratings)
5 star 25%
4 star 0%
3 star 25%
2 star 37.5%
1 star 12.5%
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Sherihan Sheriff Dec 12, 2017
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An excellent guide for both beginners and seasoned professionals that gives a practical insight on building a data lake using Big data technologies. Looking forward to more similar work from the authors in future.
Amazon Verified review Amazon
aussiejim Sep 30, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I like the diagrams that simplified the various conceptsall in all I found this a useful resource
Amazon Verified review Amazon
Anonymous Jun 04, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I am writing a detailed review in hopes that it will help others decide if this book is right for them. More importantly, I hope that the author will see these comments and correct some of the current issues in the next version.I was looking for a book to increase my knowledge of data lake implementation patterns, with technical details on batch vs real time processing, data storage, and data processing strategies. I liked the outline and approach the author chose to discuss these topics (refer to TOC), and it did contain some useful information that I was able to apply to my situation. For anyone using the Apache tools it describes several of the major technologies and when to and when not to use them. I was able to apply these to other technologies as well.The problem is that the book is full of bad grammar, misspelled words (e.g., “willn’t”), wordy/repetitive sentences (see example below), and sections where the pictures don’t match the accompanying text (e.g., the author refers to colors on a B/W picture). I give the book 3 ½ stars out of 5 in its current state. It would be a 4 if they had a tech writer proof read it, fix the grammar issues and rewrite some of the sentences to be easier to understand. With a second pass to fix consistency issues, it would be a solid 5.Detailed example of wordy/repetitive sentences…(Coped from Chapter 8 – Data Processing using Apache Flink)The technology that we have shortlisted to do this very important job of processing data is Apache Flink. I have to say that this selection was quite difficult as we have another technology in mind, namely Apache Spark, which was really strong in this area and more matured. But we decided to go with Flink in the end considering its pros. However, we have also detailed Spark a bit as opposed to other chapters in which we have just named other options and left it, because of its significance in this space.(2 pages later)For covering our use case and to build Data Lake we use Apache Flink in this layer as the technology. Other strong technology choices namely Apache Spark will also be explained a bit as we do feel that this is an equally good choice, in this layer. This chapter dives deep into Flink, though.(next page)The technology choice in this layer was really tough for us. Apache Spark was initially our choice, but Apache Flink had something in it that made us think over and at the time of writing this book, the industry did have some pointers favoring Flink and this made us do the final choice as Flink. However, we could have implemented this layer using Spark and it would have worked well for sure.After 50 pages of Flink related discussions, there is ½ page high level overview of Apache Spark.
Amazon Verified review Amazon
Dimitri Shvorob Apr 04, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
If it is "data lake" that piqued your interest, don't bother - as far as I can tell, it is just the current buzzword for company's data estate. "Data Lake for Enterprises" is a big-data book, starting with a discussion of Nathan Marz's "lambda architecture" and continuing with a tour of a set of big-data technologies which could be used to flesh out elements of that architecture. The Manning-published "Big Data" by Marz and Warren immediately suggests itself as an alternative, and I am sure that others exists - it's too bad that the earlier reviews mention none. Unfortunately, I am not a big-data guy, and cannot offer competent advice. I can say that (a) Stephen Yegge's complaints are overblown - as could be expected from Packt, the book is sloppily written and never proof-read, but it is not difficult to understand, (b) when skimmed, the book has made a decent impression.
Amazon Verified review Amazon
VG Dec 22, 2017
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Expected a lot more. It is lots of small bits and pieces of information trying to touch too many topics, mixing concepts (Very little of it) and implementation products (more of it).
Amazon Verified review Amazon
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