Data Architecture

Data Architecture
Author: Charles Tupper
Publsiher: Elsevier
Total Pages: 448
Release: 2011-05-09
ISBN 10: 9780123851277
ISBN 13: 0123851270
Language: EN, FR, DE, ES & NL

Data Architecture Book Review:

Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. Using a holistic approach to the field of data architecture, the book describes proven methods and technologies to solve the complex issues dealing with data. It covers the various applied areas of data, including data modelling and data model management, data quality, data governance, enterprise information management, database design, data warehousing, and warehouse design. This text is a core resource for anyone customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality. The book presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios. It teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions. It includes the detail needed to illustrate how the fundamental principles are used in current business practice. The book is divided into five sections, one of which addresses the software-application development process, defining tools, techniques, and methods that ensure repeatable results. Data Architecture is intended for people in business management involved with corporate data issues and information technology decisions, ranging from data architects to IT consultants, IT auditors, and data administrators. It is also an ideal reference tool for those in a higher-level education process involved in data or information technology management. Presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios Teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions Includes the detail needed to illustrate how the fundamental principles are used in current business practice

Scalable Big Data Architecture

Scalable Big Data Architecture
Author: Bahaaldine Azarmi
Publsiher: Apress
Total Pages: 141
Release: 2015-12-31
ISBN 10: 1484213262
ISBN 13: 9781484213261
Language: EN, FR, DE, ES & NL

Scalable Big Data Architecture Book Review:

This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.

Data Architecture A Primer for the Data Scientist

Data Architecture  A Primer for the Data Scientist
Author: W.H. Inmon,Daniel Linstedt,Mary Levins
Publsiher: Academic Press
Total Pages: 431
Release: 2019-04-30
ISBN 10: 0128169176
ISBN 13: 9780128169179
Language: EN, FR, DE, ES & NL

Data Architecture A Primer for the Data Scientist Book Review:

Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture

Data Architecture A Primer for the Data Scientist

Data Architecture  A Primer for the Data Scientist
Author: W.H. Inmon,Daniel Linstedt
Publsiher: Morgan Kaufmann
Total Pages: 378
Release: 2014-11-26
ISBN 10: 0128020911
ISBN 13: 9780128020913
Language: EN, FR, DE, ES & NL

Data Architecture A Primer for the Data Scientist Book Review:

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data

Data Architecture and the Experience of Place

Data  Architecture and the Experience of Place
Author: Anastasia Karandinou
Publsiher: Routledge
Total Pages: 236
Release: 2018-11-12
ISBN 10: 1351139312
ISBN 13: 9781351139311
Language: EN, FR, DE, ES & NL

Data Architecture and the Experience of Place Book Review:

The notion of data is increasingly encountered in spatial, creative and cultural studies. Big data and artificial intelligence are significantly influencing a number of disciplines. Processes, methods and vocabularies from sciences, architecture, arts are borrowed, discussed and tweaked, and new cross-disciplinary fields emerge. More and more, artists and designers are drawing on hard data to interpret the world and to create meaningful, sensuous environments. Architects are using neurophysiological data to improve their understanding of people’s experiences in built spaces. Different disciplines collaborate with scientists to visualise data in different and creative ways, revealing new connections, interpretations and readings. This often demonstrates a genuine desire to comprehend human behaviour and experience and to – possibly – inform design processes accordingly. At the same time, this opens up questions as to why this desire and curiosity is emerging now, how it relates to recent technological advances and how it converses with the cultural, philosophical and methodological context of the disciplines with which it engages. Questions are also raised as to how the use of data and data-informed methods may serve, support, promote and/or challenge political agendas. Data, Architecture and the Experience of Place provides an overview of new approaches on this significant subject and is ideal for students and researchers in digital architecture, architectural theory, design, digital media, sensory studies and related fields.

The DAMA Guide to the Data Management Body of Knowledge

The DAMA Guide to the Data Management Body of Knowledge
Author: Susan Earley
Publsiher: Technics Publications Llc
Total Pages: 406
Release: 2010-01-01
ISBN 10: 9781935504023
ISBN 13: 1935504029
Language: EN, FR, DE, ES & NL

The DAMA Guide to the Data Management Body of Knowledge Book Review:

Written by over 120 data management practitioners, this is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: Data Governance; Data Architecture Management; Data Development; Database Operations Management; Data Security Management; Reference & Master Data Management; Data Warehousing & Business Intelligence Management; Document & Content Management; Meta Data Management; Data Quality Management; Professional Development. As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: To build consensus for a generally applicable view of data management functions; To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology; To document guiding principles for data management; To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches; To clarify the scope and boundaries of data management; To act as a reference which guides readers to additional resources for further understanding.

Salesforce Data Architecture and Management

Salesforce Data Architecture and Management
Author: Ahsan Zafar
Publsiher: Packt Publishing Ltd
Total Pages: 376
Release: 2021-07-30
ISBN 10: 1801076901
ISBN 13: 9781801076906
Language: EN, FR, DE, ES & NL

Salesforce Data Architecture and Management Book Review:

Learn everything you need to become a successful data architect on the Salesforce platform Key Features Adopt best practices relating to data governance and learn how to implement them Learn how to work with data in Salesforce while maintaining scalability and security of an instance Gain insights into managing large data volumes in Salesforce Book Description As Salesforce orgs mature over time, data management and integrations are becoming more challenging than ever. Salesforce Data Architecture and Management follows a hands-on approach to managing data and tracking the performance of your Salesforce org. You'll start by understanding the role and skills required to become a successful data architect. The book focuses on data modeling concepts, how to apply them in Salesforce, and how they relate to objects and fields in Salesforce. You'll learn the intricacies of managing data in Salesforce, starting from understanding why Salesforce has chosen to optimize for read rather than write operations. After developing a solid foundation, you'll explore examples and best practices for managing your data. You'll understand how to manage your master data and discover what the Golden Record is and why it is important for organizations. Next, you'll learn how to align your MDM and CRM strategy with a discussion on Salesforce's Customer 360 and its key components. You'll also cover data governance, its multiple facets, and how GDPR compliance can be achieved with Salesforce. Finally, you'll discover Large Data Volumes (LDVs) and best practices for migrating data using APIs. By the end of this book, you'll be well-versed with data management, data backup, storage, and archiving in Salesforce. What you will learn Understand the Salesforce data architecture Explore various data backup and archival strategies Understand how the Salesforce platform is designed and how it is different from other relational databases Uncover tools that can help in data management that minimize data trust issues in your Salesforce org Focus on the Salesforce Customer 360 platform, its key components, and how it can help organizations in connecting with customers Discover how Salesforce can be used for GDPR compliance Measure and monitor the performance of your Salesforce org Who this book is for This book is for aspiring architects, Salesforce admins, and developers. You will also find the book useful if you're preparing for the Salesforce Data Architecture and Management exam. A basic understanding of Salesforce is assumed.

Guidance for Developing a Freight Transportation Data Architecture

Guidance for Developing a Freight Transportation Data Architecture
Author: César Augusto Quiroga
Publsiher: Transportation Research Board
Total Pages: 97
Release: 2011
ISBN 10: 0309155231
ISBN 13: 9780309155236
Language: EN, FR, DE, ES & NL

Guidance for Developing a Freight Transportation Data Architecture Book Review:

TRB's National Freight Cooperative Research Program (NCFRP) Report 9: Guidance for Developing a Freight Transportation Data Architecture explores the requirements and specifications for a national freight data architecture to link myriad existing data sets, identifies the value and challenges of the potential architecture, and highlights institutional strategies to develop and maintain the architecture. The report also includes an analysis of the strengths and weaknesses of a wide range of data sources; provides information on the development of a national freight data architecture definition that is scalable at the national, state, regional, and local levels; and offers readers a better understanding of the challenges that might block the implementation of a national freight data architecture as well as candidate strategies for developing, adopting, and maintaining it--

Architecting Modern Data Platforms

Architecting Modern Data Platforms
Author: Jan Kunigk,Ian Buss,Paul Wilkinson,Lars George
Publsiher: "O'Reilly Media, Inc."
Total Pages: 636
Release: 2018-12-05
ISBN 10: 1491969229
ISBN 13: 9781491969229
Language: EN, FR, DE, ES & NL

Architecting Modern Data Platforms Book Review:

There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

Big Data Application Architecture Q A

Big Data Application Architecture Q A
Author: Nitin Sawant,Himanshu Shah
Publsiher: Apress
Total Pages: 172
Release: 2014-01-24
ISBN 10: 1430262931
ISBN 13: 9781430262930
Language: EN, FR, DE, ES & NL

Big Data Application Architecture Q A Book Review:

Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real–time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution.

Enterprise Data Architecture How to navigate its landscape

Enterprise Data Architecture  How to navigate its landscape
Author: Dave Knifton
Publsiher: Paragon Publishing
Total Pages: 318
Release: 2014-10-16
ISBN 10: 1782223266
ISBN 13: 9781782223269
Language: EN, FR, DE, ES & NL

Enterprise Data Architecture How to navigate its landscape Book Review:

Are you looking to make better use of data captured within your organisation or want to learn more about how Data Architecture can transform your operations? Answering these questions is at the very heart of Navigating the Data Architecture Landscape. By reading this book you will learn how to: Introduce or improve the Data Architecture function of your organisation Enhance your skills in this domain to deliver more from your data. You may be wondering how a book can do this if it knows nothing about where you are now, or where you want to be? It can, because by leveraging its principles you will discover how to create optimised potential routes to achieve your own Data Architectural objectives. Basic building blocks, concepts and models are defined, enabling you to create new or adapt existing frameworks appropriate for any data landscape. Practical tips and suggestions are also detailed throughout, helping you gain immediate improvements from the way you work and enhance the benefits your organisation can derive from its data. So if you are a Data Architect or deal with data in your organisation and want to learn how to transform the positive yield from its data, then this book is a must read for you! “David has been there and dealt with the issues, which is why this book is an outstanding resource for Data Architects and indeed anyone dealing with the serious challenges of an enterprise data landscape.” – Richard Rendell, Technical Services Director, AgeSmart “An essential read for anyone wishing to practically achieve more benefit from data for their organisation within today’s constraints.” – Reem Zahran - Director, Offering Development, IMS Health “This book provides a comprehensive set of tools enabling you to improve the business outcomes from your organisation’s use of data.” – Andrew Rowland, Global Head Database Engineering, UBS This book is an essential read for Data Architects or indeed anyone wanting to improve the benefit that their organisation can derive from its data usage. It does this by providing principles and models that are appropriate to use within any framework, or even the absence of one. The book is designed to be practical and contains many tips and suggestions as well as examples that can be used as the basis for the reader's own Data Architectural definitions. The breadth of the book covers contemporary themes for Data Architecture and the chapters include; Data Modelling, Enterprise Data Models, Data Governance, Master Data Management and Big Data

Latest Salesforce Certified Data Architecture and Management Designer Exam Questions Answers

Latest Salesforce Certified Data Architecture and Management Designer Exam Questions   Answers
Author: Pass Exam
Publsiher: Pass Exam
Total Pages: 329
Release: 2021
ISBN 10:
ISBN 13:
Language: EN, FR, DE, ES & NL

Latest Salesforce Certified Data Architecture and Management Designer Exam Questions Answers Book Review:

- This is the latest practice test to pass the Salesforce Certified Data Architecture and Management Designer Exam. - It contains 120 Questions and Answers. - All the questions are 100% valid and stable. - You can reply on this practice test to pass the exam with a good mark and in the first attempt.

Expert Oracle Database Architecture

Expert Oracle Database Architecture
Author: Thomas Kyte,Darl Kuhn
Publsiher: Apress
Total Pages: 824
Release: 2014-11-10
ISBN 10: 1430262990
ISBN 13: 9781430262992
Language: EN, FR, DE, ES & NL

Expert Oracle Database Architecture Book Review:

Now in its third edition, this best-selling book continues to bring you some of the best thinking on how to apply Oracle Database to produce scalable applications that perform well and deliver correct results. Tom Kyte and Darl Kuhn share a simple philosophy: "you can treat Oracle as a black box and just stick data into it, or you can understand how it works and exploit it as a powerful computing environment." If you choose the latter, then you’ll find that there are few information management problems that you cannot solve quickly and elegantly. This fully revised third edition covers the developments up to Oracle Database 12c. Significant new content is included surrounding Oracle's new cloud feature set, and especially the use of pluggable databases. Each feature is taught in a proof-by-example manner, not only discussing what it is, but also how it works, how to implement software using it, and the common pitfalls associated with it. Don’t treat Oracle Database as a black-box. Get this book. Get under the hood. Turbo-charge your career. Revised to cover Oracle Database 12c Proof-by-example approach: Let the evidence be your guide Dives deeply into Oracle Database’s most powerful features

Big Data Architect s Handbook

Big Data Architect   s Handbook
Author: Syed Muhammad Fahad Akhtar
Publsiher: Packt Publishing Ltd
Total Pages: 486
Release: 2018-06-21
ISBN 10: 1788836383
ISBN 13: 9781788836388
Language: EN, FR, DE, ES & NL

Big Data Architect s Handbook Book Review:

A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architects Who this book is for Big Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect.

Architecture of a Database System

Architecture of a Database System
Author: Joseph M. Hellerstein,Michael Stonebraker,James Hamilton
Publsiher: Now Publishers Inc
Total Pages: 123
Release: 2007
ISBN 10: 1601980787
ISBN 13: 9781601980786
Language: EN, FR, DE, ES & NL

Architecture of a Database System Book Review:

Architecture of a Database System presents an architectural discussion of DBMS design principles, including process models, parallel architecture, storage system design, transaction system implementation, query processor and optimizer architectures, and typical shared components and utilities.

Data Architecture

Data Architecture
Author: William H. Inmon
Publsiher: Unknown
Total Pages: 259
Release: 1992
ISBN 10:
ISBN 13: UCAL:B3678312
Language: EN, FR, DE, ES & NL

Data Architecture Book Review:

XML for Data Architects

XML for Data Architects
Author: James Bean
Publsiher: Elsevier
Total Pages: 270
Release: 2003-07-09
ISBN 10: 9780080521435
ISBN 13: 0080521436
Language: EN, FR, DE, ES & NL

XML for Data Architects Book Review:

"The book addresses a sorely missing set of considerations in the real world... This is a very timely book." -Peter Herzum, author of Business Component Factory and CEO of Herzum Software XML is a tremendous enabler for platform agnostic data and metadata exchanges. However, there are no clear processes and techniques specifically focused on the engineering of XML structures to support reuse and integration simplicity, which are of particular importance in the age of application integration and Web services. This book describes the challenges of using XML in a manner that promotes simplification of integration, and a high degree of schema reuse. It also describes the syntactical capabilities of XML and XML Schemas, and the similarities (and in some cases limitations) of XML DTDs. This book presents combinations of architectural and design approaches to using XML as well as numerous syntactical and working examples. * Designed to be read three different ways: skim the margin notes for quick information, or use tables in the appendix to locate sections relevant the to a particular issue, or read cover-to-cover for the in-depth treatment. * Contains numerous tables that describe datatypes supported by the most common DBMSs and map to XML Schema supported data types. * Unique focus on the value added role and processes of the data architect as they apply to enterprise use of XML.

Documentation of South Dakota s ITS CVO Data Architecture

Documentation of South Dakota s ITS CVO Data Architecture
Author: Edward S. Fekpe
Publsiher: Unknown
Total Pages: 329
Release: 1999
ISBN 10:
ISBN 13: UCBK:C101218508
Language: EN, FR, DE, ES & NL

Documentation of South Dakota s ITS CVO Data Architecture Book Review:

Software Architecture for Big Data and the Cloud

Software Architecture for Big Data and the Cloud
Author: Ivan Mistrik,Rami Bahsoon,Nour Ali,Maritta Heisel,Bruce Maxim
Publsiher: Morgan Kaufmann
Total Pages: 470
Release: 2017-06-12
ISBN 10: 0128093382
ISBN 13: 9780128093382
Language: EN, FR, DE, ES & NL

Software Architecture for Big Data and the Cloud Book Review:

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques Presents case studies involving enterprise, business, and government service deployment of big data applications Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

Database Architecture Design Memory to Storage using DB2

Database Architecture Design     Memory to Storage using DB2
Author: Lawrence Dunn
Publsiher: Dominion Publishing
Total Pages: 32
Release: 2016-04-14
ISBN 10:
ISBN 13:
Language: EN, FR, DE, ES & NL

Database Architecture Design Memory to Storage using DB2 Book Review:

It continues to amaze me on the number of corporations running data-based applications on DB2 who view it as a black-box that simply houses their application data. This disconnect with the importance of properly designed and more importantly…configured…data bases leads to application error, lowered customer satisfaction, drastically decreased potential being proactive with KPI’s and increased hardware and support costs. This disconnect is so prevalent, that many organizations assume that the solution to a badly performing database is adding a score of new indexes, buying more memory or worse going through a massive conversion/upgrade effort with a new product. Capital expenditure on additional memory and storage for a database that simply needs to be properly configured for its’ workload is something that I see various companies do every year and it’s a massive waste of shareholder equity. In a previous life, I was responsible for capacity planning with Procter & Gamble’s technical infrastructure. Following the Pareto Principle (i.e., 80 /20 rule), the first thing that I did was identify which portion of the infrastructure was responsible for the highest costs in support, licensing and new expenditures. Three IBM 3090 mainframes jumped out with the highest costs so I initially placed my focus there. Looking at daily workloads of each environment what became apparent was that the utilization between each machine ran a little over 40%. This was a massive waste of resources. So I simply rebalanced the workload, dividing processing between two machines and eliminating the third. This simple act of effectively spreading workload utilization saved P&G $2M/year. The workload within a DB2 database can also be effectively spread with an effective buffer pool and storage strategy. This will drastically improve overall performance, eliminating the need to acquire additional memory and lowering support costs. I’ve been in numerous pre-sales situations were a client will sign a contract to purchase a data based application and as the ink is drying they will then ask architecture questions that they should have addressed beforehand: · What type of DB2 license should we buy? · How much memory will we need? · How much storage? · What kind of storage? · How many LUNs will we need to configure? · How many CPU’s will we need? These are all very relevant questions that should be addressed…before…making the decision to purchase a shiny new data based application. However, in my experience the key after the fact question that is rarely asked is, “What type of performance should I expect?” In my experience performance is seldom discussed during project startup, but it is…always…brought up once the application is in production. The issue as I stated earlier, is that many technical professionals view DB2 as a black-box in that they are only concerned with…space. With this fixated view they look at the potential size of the database to figure out how much storage to purchase. Then the storage size is used to guestimate the amount of memory and CPU that should be required. The assumption is made that adequately estimating hardware based on database size will also equate into having adequate performance. This is a woefully inadequate assumption that will lead to subpar performance in your production environment. This white paper will illustrate via a case study the benefits of completing a database architecture design as one of the first project deliverables that contains the database configuration parameters, bufferpool design, tablespace design, LUN design, and device/file design. This will ensure optimal database performance and low capital expenditures for your project.