Immerse yourself in the pivotal Order to Cash (OTC) process in SAP with this comprehensive guide! By leveraging the functionalities of SAP CRM, SAP APO, SAP TMS, and SAP LES, integrated with SAP ECC, this book provides a detailed walkthrough to enhance your business operations and system understanding. What this Book will help me do Understand master data management across different SAP modules to ensure integrated operations. Explore and implement the key functions of sales processes and customer relationship management in SAP CRM. Master the concepts of order fulfillment, including ATP checks, leveraging SAP APO. Dive deep into transportation planning and freight management processes using SAP TMS. Gain insights into logistics execution and customer invoicing using SAP ECC. Author(s) None Agarwal is an experienced SAP consultant specializing in enterprise integration and process optimization. With an extensive background in SAP modules such as CRM, APO, TMS, and LES, Agarwal brings real-world experience into this work. Passionate about helping others leverage SAP software to its fullest, Agarwal writes accessible and actionable guides. Who is it for? This book is tailored for SAP consultants, solution architects, and managers tasked with process optimization in SAP environments. If you're seeking to integrate SAP CRM, TMS, or APO modules effectively into your operations, this book has been designed for you. Readers are expected to have a foundational understanding of SAP ECC and its core principles. Ideal for individuals aiming to enhance their enterprise's OTC processes.

As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

The vast majority of businesses today already have a documented data strategy. But only a third of these forward-thinking companies have evolved into data-driven organizations or even begun to move toward a data culture. Most have yet to treat data as a business asset, much less use data and analytics to compete in the marketplace. What’s the solution? This insightful report demonstrates the importance of creating a holistic data infrastructure approach. You’ll learn how data virtualization (DV), master data management (MDM), and metadata-management capabilities can help your organization meet business objectives. Chief data officers, enterprise architects, analytics leaders, and line-of-business executives will understand the benefits of combining these capabilities into a unified data platform. Explore three separate business contexts that depend on data: operations, analytics, and governance Learn a pragmatic and holistic approach to building a unified data infrastructure Understand the critical capabilities of this approach, including the ability to work with existing technology Apply six best practices for combining data management capabilities

Master Data Management is no shiny object. But like many traditional IT practices, MDM is being severely tested – and rendered all the more strategic – by digitalization and rising data volumes.

Originally published at https://www.eckerson.com/articles/five-master-data-management-best-practices-for-enterprises

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it?

How does it compare to the other available platforms for data warehousing? How does it differ from traditional data warehouses?

How does the performance and flexibility affect the data modeling requirements?

Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces? Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity?

What are some of the current limitations that you are struggling with?

For someone getting started with Snowflake what is involved with loading data into the platform?

What is their workflow for allocating and scaling compute capacity and running anlyses?

One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen? What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about? When is SnowflakeDB the wrong choice? What are some of the plans for the future of SnowflakeDB?

Contact Info

LinkedIn Website @KentGraziano on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

SnowflakeDB

Free Trial Stack Overflow

Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto

Podcast Episode

SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal Form Data Vault Modeling Dimensional Modeling JSON AVRO Parquet SnowflakeDB Virtual Warehouses CRM == Customer Relationship Management Master Data Management

Podcast Episode

FoundationDB

Podcast Episode

Apache Spark

Podcast Episode

SSIS == SQL Server Integration Services Talend Informatica Fivetran

Podcast Episode

Matillion Apache Kafka Snowpipe Snowflake Data Exchange OLTP == Online Transaction Processing GeoJSON Snowflake Documentation SnowAlert Splunk Data Catalog

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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Summary Data integration is one of the most challenging aspects of any data platform, especially as the variety of data sources and formats grow. Enterprise organizations feel this acutely due to the silos that occur naturally across business units. The CluedIn team experienced this issue first-hand in their previous roles, leading them to build a business aimed at building a managed data fabric for the enterprise. In this episode Tim Ward, CEO of CluedIn, joins me to explain how their platform is architected, how they manage the task of integrating with third-party platforms, automating entity extraction and master data management, and the work of providing multiple views of the same data for different use cases. I highly recommend listening closely to his explanation of how they manage consistency of the data that they process across different storage backends.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Tim Ward about CluedIn, an integration platform for implementing your companies data fabric

Interview

Introduction

How did you get involved in t

Why is Data Quality still an issue after all these years? To get an answer to the prevalent question, Wayne Eckerson and Jason Beard engage in a dynamic exchange of questions which lead us to the root cause of data quality and data governance problems. Using examples from his past projects, Jason shows the value of business process mapping and how it exposes the hidden problems which go undetected under the standard IT lens.

In his most recent role as Vice President of Process & Data Management at Wiley, a book publisher, he was responsible for master data setup and governance, process optimization, business continuity planning, and change management for new and emerging business models. Jason has led business intelligence, data governance, master data management, Process Improvement, Business Transformation, and ERP projects in a variety of industries, including Scientific and Trade publishing, Educational Technology, Consumer Goods, Banking, Investments, and Insurance.

Send us a text Happy holidays from the Making Data Simple team! Enjoy a rebroadcast of a conversation with Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics, as he and Al explore the strategies and people your company needs to disrupt and succeed in the year ahead. Do you or your team members need new credentials to work in data? Seth also discusses what you need in your toolkit to be a data scientist at IBM.

Show Notes 00.30 Connect with Al Martin on Twitter and LinkedIn. 01.00 Connect with Seth Dobrin on Twitter and LinkedIn. 01.40 Read "What IBM looks for in a Data Scientist" by Seth Dobrin and Jean-Francois Puget. 06.00 Learn more about GDPR.  13.00 Learn more about master data management. 13.05 Learn more about unified governance and integration.  13.25 Learn more about machine learning.  14.00 Connect and learn more about Ginni Rometty.  14.40 Learn more about cognitive computing. 19.35 Connect with Rob Thomas on Twitter and LinkedIn. 21.00 Connect with Jean-Francois Puget on Twitter and LinkedIn. Follow @IBMAnalytics Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary

With the proliferation of data sources to give a more comprehensive view of the information critical to your business it is even more important to have a canonical view of the entities that you care about. Is customer number 342 in your ERP the same as Bob Smith on Twitter? Using master data management to build a data catalog helps you answer these questions reliably and simplify the process of building your business intelligence reports. In this episode the head of product at Tamr, Mark Marinelli, discusses the challenges of building a master data set, why you should have one, and some of the techniques that modern platforms and systems provide for maintaining it.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Mark Marinelli about data mastering for modern platforms

Interview

Introduction How did you get involved in the area of data management? Can you start by establishing a definition of data mastering that we can work from?

How does the master data set get used within the overall analytical and processing systems of an organization?

What is the traditional workflow for creating a master data set?

What has changed in the current landscape of businesses and technology platforms that makes that approach impractical? What are the steps that an organization can take to evolve toward an agile approach to data mastering?

At what scale of company or project does it makes sense to start building a master data set? What are the limitations of using ML/AI to merge data sets? What are the limitations of a golden master data set in practice?

Are there particular formats of data or types of entities that pose a greater challenge when creating a canonical format for them? Are there specific problem domains that are more likely to benefit from a master data set?

Once a golden master has been established, how are changes to that information handled in practice? (e.g. versioning of the data) What storage mechanisms are typically used for managing a master data set?

Are there particular security, auditing, or access concerns that engineers should be considering when managing their golden master that goes beyond the rest of their data infrastructure? How do you manage latency issues when trying to reference the same entities from multiple disparate systems?

What have you found to be the most common stumbling blocks for a group that is implementing a master data platform?

What suggestions do you have to help prevent such a project from being derailed?

What resources do you recommend for someone looking to learn more about the theoretical and practical aspects of

Master data management, visualization, and spatial analysis techniques in QGIS and become a GIS power user About This Book Learn how to work with various types of data and create beautiful maps using this easy-to-follow guide Give a touch of professionalism to your maps, both for functionality and look and feel, with the help of this practical guide This progressive, hands-on guide builds on a geo-spatial data and adds more reactive maps using geometry tools. Who This Book Is For If you are a user, developer, or consultant and want to know how to use QGIS to achieve the results you are used to from other types of GIS, then this learning path is for you. You are expected to be comfortable with core GIS concepts. This Learning Path will make you an expert with QGIS by showing you how to develop more complex, layered map applications. It will launch you to the next level of GIS users. What You Will Learn Create your first map by styling both vector and raster layers from different data sources Use parameters such as precipitation, relative humidity, and temperature to predict the vulnerability of fields and crops to mildew Re-project vector and raster data and see how to convert between different style formats Use a mix of web services to provide a collaborative data system Use raster analysis and a model automation tool to model the physical conditions for hydrological analysis Get the most out of the cartographic tools to in QGIS to reveal the advanced tips and tricks of cartography In Detail The first module Learning QGIS, Third edition covers the installation and configuration of QGIS. You'll become a master in data creation and editing, and creating great maps. By the end of this module, you'll be able to extend QGIS with Python, getting in-depth with developing custom tools for the Processing Toolbox. The second module QGIS Blueprints gives you an overview of the application types and the technical aspects along with few examples from the digital humanities. After estimating unknown values using interpolation methods and demonstrating visualization and analytical techniques, the module ends by creating an editable and data-rich map for the discovery of community information. The third module QGIS 2 Cookbook covers data input and output with special instructions for trickier formats. Later, we dive into exploring data, data management, and preprocessing steps to cut your data to just the important areas. At the end of this module, you will dive into the methods for analyzing routes and networks, and learn how to take QGIS beyond the out-of-the-box features with plug-ins, customization, and add-on tools. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning QGIS, Third Edition by Anita Graser QGIS Blueprints by Ben Mearns QGIS 2 Cookbook by Alex Mandel, Víctor Olaya Ferrero, Anita Graser, Alexander Bruy Style and approach This Learning Path will get you up and running with QGIS. We start off with an introduction to QGIS and create maps and plugins. Then, we will guide you through Blueprints for geographic web applications, each of which will teach you a different feature by boiling down a complex workflow into steps you can follow. Finally, you'll turn your attention to becoming a QGIS power user and master data management, visualization, and spatial analysis techniques of QGIS. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

HBase is a remarkable tool for indexing mass volumes of data, but getting started with this distributed database and its ecosystem can be daunting. With this hands-on guide, you’ll learn how to architect, design, and deploy your own HBase applications by examining real-world solutions. Along with HBase principles and cluster deployment guidelines, this book includes in-depth case studies that demonstrate how large companies solved specific use cases with HBase. Authors Jean-Marc Spaggiari and Kevin O’Dell also provide draft solutions and code examples to help you implement your own versions of those use cases, from master data management (MDM) and document storage to near real-time event processing. You’ll also learn troubleshooting techniques to help you avoid common deployment mistakes. Learn exactly what HBase does, what its ecosystem includes, and how to set up your environment Explore how real-world HBase instances were deployed and put into production Examine documented use cases for tracking healthcare claims, digital advertising, data management, and product quality Understand how HBase works with tools and techniques such as Spark, Kafka, MapReduce, and the Java API Learn how to identify the causes and understand the consequences of the most common HBase issues

In "Data Lake Development with Big Data," you will explore the fundamental principles and techniques for constructing and managing a Data Lake tailored for your organization's big data challenges. This book provides practical advice and architectural strategies for ingesting, managing, and analyzing large-scale data efficiently and effectively. What this Book will help me do Learn how to architect a Data Lake from scratch tailored to your organizational needs. Master techniques for ingesting data using real-time and batch processing frameworks efficiently. Understand data governance, quality, and security considerations essential for scalable Data Lakes. Discover strategies for enabling users to explore data within the Data Lake effectively. Gain insights into integrating Data Lakes with Big Data analytic applications for high performance. Author(s) None Pasupuleti and Beulah Salome Purra bring their extensive expertise in big data and enterprise data management to this book. With years of hands-on experience designing and managing large-scale data architectures, their insights are rooted in practical knowledge and proven techniques. Who is it for? This book is ideal for data architects and senior managers tasked with adapting or creating scalable data solutions in enterprise contexts. Readers should have foundational knowledge of master data management and be familiar with Big Data technologies to derive maximum value from the content presented.

SAP is a market leader in enterprise business application software. SAP solutions provide a rich set of composable application modules, and configurable functional capabilities that are expected from a comprehensive enterprise business application software suite. In most cases, companies that adopt SAP software remain heterogeneous enterprises running both SAP and non-SAP systems to support their business processes. Regardless of the specific scenario, in heterogeneous enterprises most SAP implementations must be integrated with a variety of non-SAP enterprise systems: Portals Messaging infrastructure Business process management (BPM) tools Enterprise Content Management (ECM) methods and tools Business analytics (BA) and business intelligence (BI) technologies Security Systems of record Systems of engagement The tooling included with SAP software addresses many needs for creating SAP-centric environments. However, the classic approach to implementing SAP functionality generally leaves the business with a rigid solution that is difficult and expensive to change and enhance. When SAP software is used in a large, heterogeneous enterprise environment, SAP clients face the dilemma of selecting the correct set of tools and platforms to implement SAP functionality, and to integrate the SAP solutions with non-SAP systems. This IBM® Redbooks® publication explains the value of integrating IBM software with SAP solutions. It describes how to enhance and extend pre-built capabilities in SAP software with best-in-class IBM enterprise software, enabling clients to maximize return on investment (ROI) in their SAP investment and achieve a balanced enterprise architecture approach. This book describes IBM Reference Architecture for SAP, a prescriptive blueprint for using IBM software in SAP solutions. The reference architecture is focused on defining the use of IBM software with SAP, and is not intended to address the internal aspects of SAP components. The chapters of this book provide a specific reference architecture for many of the architectural domains that are each important for a large enterprise to establish common strategy, efficiency, and balance. The majority of the most important architectural domain topics, such as integration, process optimization, master data management, mobile access, Enterprise Content Management, business intelligence, DevOps, security, systems monitoring, and so on, are covered in the book. However, there are several other architectural domains which are not included in the book. This is not to imply that these other architectural domains are not important or are less important, or that IBM does not offer a solution to address them. It is only reflective of time constraints, available resources, and the complexity of assembling a book on an extremely broad topic. Although more content could have been added, the authors feel confident that the scope of architectural material that has been included should provide organizations with a fantastic head start in defining their own enterprise reference architecture for many of the important architectural domains, and it is hoped that this book provides great value to those reading it. This IBM Redbooks publication is targeted to the following audiences: Client decision makers and solution architects leading enterprise transformation projects and wanting to gain further insight so that they can benefit from the integration of IBM software in large-scale SAP projects. IT architects and consultants integrating IBM technology with SAP solutions.

SAP is a market leader in enterprise business application software. SAP solutions provide a rich set of composable application modules, and configurable functional capabilities that are expected from a comprehensive enterprise business application software suite. In most cases, companies that adopt SAP software remain heterogeneous enterprises running both SAP and non-SAP systems to support their business processes. Regardless of the specific scenario, in heterogeneous enterprises most SAP implementations must be integrated with a variety of non-SAP enterprise systems: Portals Messaging infrastructure Business process management (BPM) tools Enterprise Content Management (ECM) methods and tools Business analytics (BA) and business intelligence (BI) technologies Security Systems of record Systems of engagement When SAP software is used in a large, heterogeneous enterprise environment, SAP clients face the dilemma of selecting the correct set of tools and platforms to implement SAP functionality, and to integrate the SAP solutions with non-SAP systems. This IBM® Redbooks® publication explains the value of integrating IBM software with SAP solutions. It describes how to enhance and extend pre-built capabilities in SAP software with best-in-class IBM enterprise software, enabling clients to maximize return on investment (ROI) in their SAP investment and achieve a balanced enterprise architecture approach. This book describes IBM Reference Architecture for SAP, a prescriptive blueprint for using IBM software in SAP solutions. The reference architecture is focused on defining the use of IBM software with SAP, and is not intended to address the internal aspects of SAP components. The chapters of this book provide a specific reference architecture for many of the architectural domains that are each important for a large enterprise to establish common strategy, efficiency, and balance. The majority of the most important architectural domain topics, such as integration, process optimization, master data management, mobile access, Enterprise Content Management, business intelligence, DevOps, security, systems monitoring, and so on, are covered in the book. However, there are several other architectural domains which are not included in the book. This is not to imply that these other architectural domains are not important or are less important, or that IBM does not offer a solution to address them. It is only reflective of time constraints, available resources, and the complexity of assembling a book on an extremely broad topic. Although more content could have been added, the authors feel confident that the scope of architectural material that has been included should provide organizations with a fantastic head start in defining their own enterprise reference architecture for many of the important architectural domains, and it is hoped that this book provides great value to those reading it. This IBM Redbooks publication is targeted to the following audiences: Client decision makers and solution architects leading enterprise transformation projects and wanting to gain further insight so that they can benefit from the integration of IBM software in large-scale SAP projects. IT architects and consultants integrating IBM technology with SAP solutions.

Today's businesses, applications, social media, and online transactions generate more data than ever before. This data can be explored and analyzed to provide tremendous business value. IBM® Watson™ Explorer and IBM InfoSphere® Master Data Management (InfoSphere MDM) enable organizations to simultaneously explore and derive insights from enterprise data that was traditionally stored in "silos" in enterprise applications, different data repositories, and in different data formats. This IBM Redbooks® publication provides information about Watson Explorer 9.0, InfoSphere MDM, and IBM InfoSphere MDM Probabilistic Matching Engine for InfoSphere BigInsights™ (PME for BigInsights). It gives you an overview, describes the architecture, and presents use cases that you can use to accomplish the following tasks: Understand the core capabilities of Watson Explorer, InfoSphere MDM, and PME for BigInsights. Realize the full potential of Watson Explorer applications. Describe the integration and value of the combination of Watson Explorer and InfoSphere MDM. Build a 360-degree information application. Learn by example by following hands-on lab scenarios.

Today's businesses, applications, social media, and online transactions generate more data than ever before. This data can be explored and analyzed to provide tremendous business value. IBM® InfoSphere® Data Explorer (Data Explorer) enables organizations to simultaneously explore and derive insights from enterprise data that was traditionally stored in "silos" in enterprise applications, different data repositories, and in different data formats. Applications developed using Data Explorer identify data relationships across these silos, unlocking the business value that is inherent in a unified, 360-degree view of the information related to business entities, such as application users, customers, products, and so on. This IBM Redbooks® publication provides information about InfoSphere Data Explorer 9.0. It gives an overview, describes the architecture, and presents use cases that you can use to accomplish the following tasks: Understand the core capabilities of Data Explorer Engine and Data Explorer Application Builder. Realize the full potential of Data Explorer applications. Describe the integration and value of the combination of Data Explorer and InfoSphere Master Data Management. Build a 360-degree information application. Learn by example by following a hands-on lab scenario.

IBM® InfoSphere® Master Data Management Reference Data Management Hub (InfoSphere MDM Ref DM Hub) is designed as a ready-to-run application that provides the governance, process, security, and audit control for managing reference data as an enterprise standard, resulting in fewer errors, reduced business risk and cost savings. This IBM Redbooks® publication describes where InfoSphere MDM Ref DM Hub fits into information management reference architecture. It explains the end-to-end process of an InfoSphere MDM Ref DM Hub implementation including the considerations of planning a reference data management project, requirements gathering and analysis, model design in detail, and integration considerations and scenarios. It then shows implementation examples and the ongoing administration tasks. This publication can help IT professionals who are interested or have a need to manage reference data efficiently and implement an InfoSphere MDM Ref DM Hub solution with ease.

Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You’ll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtualization really works. Data Virtualization for Business Intelligence Systems outlines the advantages and disadvantages of data virtualization and illustrates how data virtualization should be applied in data warehouse environments. You’ll come away with a comprehensive understanding of how data virtualization will make data warehouse environments more flexible and how it make developing operational BI applications easier. Van der Lans also describes the relationship between data virtualization and related topics, such as master data management, governance, and information management, so you come away with a big-picture understanding as well as all the practical know-how you need to virtualize your data. First independent book on data virtualization that explains in a product-independent way how data virtualization technology works. Illustrates concepts using examples developed with commercially available products. Shows you how to solve common data integration challenges such as data quality, system interference, and overall performance by following practical guidelines on using data virtualization. Apply data virtualization right away with three chapters full of practical implementation guidance. Understand the big picture of data virtualization and its relationship with data governance and information management.

To make better informed business decisions, better serve clients, and increase operational efficiencies, you must be aware of changes to key data as they occur. In addition, you must enable the immediate delivery of this information to the people and processes that need to act upon it. This ability to sense and respond to data changes is fundamental to dynamic warehousing, master data management, and many other key initiatives. A major challenge in providing this type of environment is determining how to tie all the independent systems together and process the immense data flow requirements. IBM® InfoSphere® Change Data Capture (InfoSphere CDC) can respond to that challenge, providing programming-free data integration, and eliminating redundant data transfer, to minimize the impact on production systems. In this IBM Redbooks® publication, we show you examples of how InfoSphere CDC can be used to implement integrated systems, to keep those systems updated immediately as changes occur, and to use your existing infrastructure and scale up as your workload grows. InfoSphere CDC can also enhance your investment in other software, such as IBM DataStage® and IBM QualityStage®, IBM InfoSphere Warehouse, and IBM InfoSphere Master Data Management Server, enabling real-time and event-driven processes. Enable the integration of your critical data and make it immediately available as your business needs it.

SAP NetWeaver MDM 7.1 Administrator's Guide acts as a complete resource for mastering the administration and configuration of SAP's Master Data Management solution: NetWeaver MDM 7.1. With a hands-on and practical approach, this book connects theoretical understanding with real-world application, tailored specifically for MDM administrators. What this Book will help me do Understand the core concepts and business scenarios associated with SAP NetWeaver MDM. Master the configuration of MDM Console, Servers, repositories, and the underlying database. Learn to maintain repository integrity through backup, restore, and management techniques. Automate data operations like importing and syndicating through MDM tools. Grasp the integration aspects of MDM with other SAP NetWeaver components. Author(s) Uday Rao is an experienced administrator and consultant in SAP systems, specializing in Master Data Management. With years of field experience, Uday brings deep technical insights combined with an approach that simplifies complex administration tasks. His guide emphasizes practical scenarios with step-by-step instructions that empower SAP professionals. Who is it for? This book is ideal for SAP administrators aiming to specialize in Master Data Management with NetWeaver MDM. It targets professionals with foundational knowledge in SAP who are looking to gain expertise in configuring and managing MDM systems. Novices in SAP MDM can still benefit from the guide's structured approach. Whether you're managing corporate data systems or overseeing MDM projects, this guide aligns with your goals.