Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.

Data Lake Architecture: Designing the Data Lake and Avoiding the Garbage Dump

Computers & Technology

Inmon, Bill

Computer Science

Technics Publications

The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today’s complex and ever-changing modern information systems. This book shows you how to construct your data lakehouse as the foundation for your artificial intelligence (AI), machine learning (ML), and data mesh initiatives. Know the pitfalls and techniques for maximizing business value of your data lakehouse.\nIn addition, be able to explain the core characteristics and critical success factors of a data lakehouse. By reviewing entry errors, key incompatibility, and ensuring good documentation, we can improve the data quality and believability of your lakehouse. Evaluate criteria for data quality, including accuracy, completeness, reliability, relevance, and timeliness. Understand the different types of storage for the lakehouse, including the under-utilized yet extremely valuable bulk storage.\nThere are three data types in the data lakehouse (structured, textual, and analog/ IoT), and for each, learn how to build a robust foundation for artificial intelligence (AI), machine learning (ML), and data mesh. Leverage data models for structured data, ontologies and taxonomies for textual data, and distillation algorithms for analog/IoT data. Learn how to abstract these data types to accommodate future requirements and simplify data lineage. Apply Extract, Transform, and Load (ETL) to create a structure that returns the answers to business problems. The end result is a data lakehouse that meets our needs.\nSpeaking of human needs, learn Maslow’s Hierarchy of Data Lakehouse Needs. Next explore data integration geared for Al, ML, and data mesh. Then deep dive with us into all of the varieties of analytics within the lakehouse, including structured, textual, and analog analytics. Witness how descriptive data, data catalog, and metadata can increase the value of the lakehouse.\nWe conclude with a detailed evolution of data architecture, from magnetic tape to the data lakehouse as a bedrock foundation for AI, ML, and data mesh.

The Data Lakehouse: The Bedrock for Artificial Intelligence, Machine Learning, and Data Mesh

Rapien, Dave

Bartelt, Valerie

Software

In our distant past, we attempted to create wealth by turning everyday substances into gold. This was early alchemy, and ultimately it did not work. But the world has changed. Today we have a type of "modern alchemy" that really can create gold. We can transform voluminous text into a wealth of knowledge. Text is a common fabric of society, yet it is still challenging for our technology to make sense of text. This is where taxonomies can help. In this book, legendary Bill Inmon will introduce you to the concept of taxonomies and how they are used to simplify and understand text. We emphasize the practical aspects of taxonomies, and the subsequent usage of taxonomies as a basis for textual analytics. This book is for managers who have to deal with text, students of computer science, programmers who need to understand taxonomies, systems analysts who hope to draw business value out of a body of text, and especially those who are struggling to decode data lakes. Hopefully for those individuals (and many more), this book will serve as both an introduction to taxonomies and a guide to how taxonomies can be used to bring text into the realm of corporate decision-making. This book will introduce you to the world of taxonomies, as well as explore: 
<ul>
<li>Simple and complex taxonomies </li>
<li>Ontologies </li>
<li>Obtaining taxonomies </li>
<li>Changing taxonomies </li>
<li>Taxonomies and data models </li>
<li>Types of textual data </li>
<li>Textual analytics.</li>
</ul> In addition, several case studies are presented from industries as diverse as banking, call centers, and travel.

Turning Text into Gold: Taxonomies and Textual Analytics

For years, business users have leveraged spreadsheets for storing and communicating data. Although spreadsheets may be easy to create and update, making important corporate decisions based on spreadsheets is risky due to the lack of data credibility. Whether you are a manager, developer, end user, or student, this book will help you turn spreadsheet data into credible, useful, reliable data that can be trusted in order to make important decisions. A chapter is dedicated to each of the following topics: 
<ul>
<li>Brief history of spreadsheets </li>
<li>Spreadsheet paradox </li>
<li>Spreadsheet varieties </li>
<li>The PDF spreadsheet </li>
<li>Spreadsheet formatting </li>
<li>Spreadsheet disambiguation </li>
<li>The intermediate database </li>
<li>The ssdef database </li>
<li>The corporate database </li>
<li>The metadata database (mnemonic database) </li>
<li>Political considerations </li>
<li>Data modeling and the spreadsheet </li>
<li>Case study</li>
</ul>

Turning Spreadsheets into Corporate Data

Learn how the data lakehouse is designed and architected to meet today’s complex and ever-changing analytics, machine learning, and data science requirements.\nIn the bestseller, “Building the Data Lakehouse”, you learned about the features of the data lakehouse, along with its powerful analytical infrastructure. This book is the architectural companion to “Building the Data Lakehouse”. Appreciate the strategic approaches and challenges with including structured data, text, and IoT/analog readings within the same analytical environment. Know the steps to create the data lakehouse canonical model, and the dynamic processing necessary to satisfy the most demanding business analysts and data scientists. Understand the modern cloud data storage cost-saving methodology through Data Future-proofing. Experience a new paradigm of Micro Repository in microservices architecture and advanced security to ensure your data lakehouse delivers business value for generations.

The Data Lakehouse Architecture

Srivastava, Ranjeet

Networking & Cloud Computing

Overcome the challenges, appreciate the varieties, and apply the process of data integration.\nLearn all about data integration and become a data integration hero instead of following the masses and running in the opposite direction at the mere mention of the word “integration”. Understand why organizations avoid data integration and often wind up with spider web environments containing siloed applications instead of an enterprise database which excites analysts and data scientists. Distinguish the different types of integration: database, attribute, key, index, encoding, measurement, format, definition, KPI, calculations, summarization, selection criteria, data exclusion, lineage, and timing. Apply identification, equivocation, and physical conversion levels of integration for both structured and textual data. Leverage deidentification, proximity analysis, alternate spelling, stop word resolution, homographic resolution, stemming, taxonomical resolution, inline contextualization, classification, and acronym resolution. Learn how to combine structured and textual data in the context of three levels of interaction. Follow the steps of scope, model, and map in integrating structured data. Follow the steps of scope, connect taxonomies, ingest raw text, and determine analytical processes in integrating textual data. Apply integration best practices, including identifying integration roles, developing a reusable data integration process, and documenting the integration benefits. Compare taxonomies with data models. Know how data integration helps data science.\nTo reinforce all of the concepts within the book, we include a detailed case study on data integration.

Integrating Data

Haines, Patty

Rapien, David

Programming

Increase the awareness of your customer's behavior to survive and excel within your industry. One hundred years ago, the voice of the customer was easily and routinely heard by the shopkeeper. In small towns, the shopkeeper knew everyone. Today's world has gotten much bigger and much more complex. No longer does the store owner personally know everyone who comes into the store. Yet there are three important abilities technologies offer that make it possible to listen to the voice of the customer today: <ul>
<li>The ability to acquire, store, and manage huge amounts of data </li>
<li>The ability to read and understand text in a computerized environment </li>
<li>The ability to visualize data </li>
</ul> This book answers important questions such as: <ul>
<li>Where is the voice of the customer heard? </li>
<li>How does the corporation find and capture the voice of the customer? </li>
<li>How is the voice of the customer actually interpreted and understood? </li>
<li>How do you cope with the volume of messages the customer is sending you? </li>
<li>How do you separate noise from the important messages? </li>
<li>How do you analyze the composite voice of the customer over thousands of customers? </li>
<li>How do you reduce the voice of the customer to a visual format that is understood by management? </li>
<li>How do you know when the message the customer is sending changes? </li>
</ul>After reading this book the reader will be able to manage, build, and operate a corporate infrastructure that listens to the voice of the customer.

Hearing the Voice of the Customer

Business & Money

Marketing & Sales

A Methodology for Building the Data Lakehouse

Data Architecture: Building the Foundation

Databases & Big Data

Technics Publications, LLC

Modernizing Medical Research: AI and Medical Records

Sydow MD, Sylvia

Industries

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Books by Inmon, Bill