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Published By: DATAVERSITY     Published Date: Jul 06, 2015
The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across-the-board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. The time-honored techniques for data modeling are being rewritten, reworked, and modified in a multitude of different ways, often wholly dependent on the NoSQL platform under development. The research report analyzes a 2015 DATAVERSITY® survey titled “Modeling NoSQL.” The survey examined a number of crucial issues within the NoSQL world today, with focus on data modeling in particular.
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DATAVERSITY
Published By: Ted Hills     Published Date: Mar 08, 2017
NoSQL database management systems give us the opportunity to store our data according to more than one data storage model, but our entity-relationship data modeling notations are stuck in SQL land. Is there any need to model schema-less databases, and is it even possible? In this short white paper, Ted Hills examines these questions in light of a recent paper from MarkLogic on the hybrid data model.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Much has been written and debated about the use of SQL NULLs to represent unknown values, and the possible use of three-valued logic. However, there has never been a systematic application of any three-valued logic to use in the logical expressions of computer programs. This paper lays the foundation for a systematic application of three-valued logic to one of the two problems inadequately addressed by SQL NULLs.
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Ted Hills
Published By: DATAVERSITY     Published Date: Dec 27, 2013
There are actually many elements of such a vision that are working together. ACID and NoSQL are not the antagonists they were once thought to be; NoSQL works well under a BASE model, but also some of the innovative NoSQL systems fully conform to ACID requirements. Database engineers have puzzled out how to get non-relational systems to work within an environment that demands high availability, scalability, with differing levels of recovery and partition tolerance. BASE is still a leading innovation that is wedded to the NoSQL model, and the evolution of both together is harmonious. But that doesn’t mean they always have to be in partnership; there are several options. So while the opening anecdote is true in many cases, organizations that need more diverse possibilities can move into the commercial arena and get the specific option that works best for them. This paper is sponsored by: MarkLogic.
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nosql, database, acid v base, white paper
    
DATAVERSITY
Published By: Databricks     Published Date: Sep 13, 2018
Learn how to get started with Apache Spark™ Apache Spark™’s ability to speed analytic applications by orders of magnitude, its versatility, and ease of use are quickly winning the market. With Spark’s appeal to developers, end users, and integrators to solve complex data problems at scale, it is now the most active open source project with the big data community. With rapid adoption by enterprises across a wide range of industries, Spark has been deployed at massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. If you are a developer or data scientist interested in big data, learn how Spark may be the tool for you. Databricks is happy to present this ebook as a practical introduction to Spark. Download this ebook to learn: • Spark’s basic architecture • Why Spark is a popular choice for data analytics • What tools and features are available • How to get started right away through interactive sample code
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Databricks
Published By: Stardog Union     Published Date: Jul 27, 2018
When enterprises consider the benefits of data analysis, what's often overlooked is the challenge of data variety, and that most successful outcomes are driven by it. Yet businesses are still struggling with how to query distributed, heterogeneous data using a unified data model. Fortunately, Knowledge Graphs provide a schema flexible solution based on modular, extensible data models that evolve over time to create a truly unified solution. How is this possible? Download and discover: • Why businesses should organize information using nodes and edges instead of rows, columns and tables • Why schema free and schema rigid solutions eventually prove to be impractical • The three categories of data diversity including semantic and structural variety
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Stardog Union
Published By: Embarcadero     Published Date: Oct 21, 2014
Metadata defines the structure of data in files and databases, providing detailed information about entities and objects. In this white paper, Dr. Robin Bloor and Rebecca Jowiak of The Bloor Group discuss the value of metadata and the importance of organizing it well, which enables you to: - Collaborate on metadata across your organization - Manage disparate data sources and definitions - Establish an enterprise glossary of business definitions and data elements - Improve communication between teams
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data, data management, enterprise data management, enterprise information management, metadata, robin bloor, rebecca jozwiak, embarcadero
    
Embarcadero
Published By: Embarcadero     Published Date: Jul 23, 2015
Whether you’re working with relational data, schema-less (NoSQL) data, or model metadata, you need a data architecture that can actively leverage information assets for business value. The most valuable data has high quality, business context, and visibility across the organization. Check out this must-read eBook for essential insights on important data architecture topics.
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Embarcadero
Published By: CMMI Institute     Published Date: Mar 21, 2016
CMMI Institute®’s Data Management Maturity (DMM)SM framework enables organizations to improve data management practices across the full spectrum of their business. It is a unique, comprehensive reference model that provides organizations with a standard set of best practices to assess their capabilities, strengthen their data management program, and develop a custom roadmap for improvements that align with their business goals.
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CMMI Institute
Published By: Skytree     Published Date: Nov 23, 2014
Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. In this brief, discover how you can use machine learning to analyze both unstructured text data and semi- structured log data, providing you with the insights needed to achieve your business goals.
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log data, machine learning, natural language, nlp, natural language processing, skytree, unstructured data, semi-structured data, data analysis
    
Skytree
Published By: AnalytixDS     Published Date: Feb 28, 2015
With future business intelligence solutions clearly evolving from data that comes from highly efficient and well behaved systems, to data that comes from the extended enterprise where data is not necessarily so well structured and behaved - Organizations are forced into a more collaborative mode of operation with their core infrastructure being adapted from the consumer space, and to the extent possible, conformed to their existing repositories. This whitepaper attempts to address various challenges consumers face while managing enormous data sets within the context of this complex scenario. Further, we’ll try to answer the question: Is Big Data Governance really that different from traditional data governance initiatives? Finally, we’ll see how AnalytiX™ Mapping Manager™ can help organizations accelerate the development and deployment of a successful Big Data/ Business Intelligence platform and accelerate delivery of all sorts of data – structured, semi-structured as well as unstruc
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big data, big data governance, data governance, analytixds
    
AnalytixDS
Published By: Ted Hills     Published Date: Jul 02, 2015
Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. Furthermore, E-R notation has some limitations even for SQL database design. This article shows how a new notation, the Conceptual and Objective Modeling (COM) notation, is able to represent NOSQL designs that are beyond the reach of E-R notation. At the end, it gives a peek into the tutorial workshop to be given at the 2015 NOSQL Conference in San Jose, CA, US, in August, which will provide opportunities to apply COM notation to practical problems.
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nosql, sql, data modeling, data model, er modeling, entity relationship, database, relational, dbms, schema-less, xml, conceptual, logical, physical
    
Ted Hills
Published By: Trillium Software     Published Date: Mar 29, 2016
We are living in a new age in which your business success depends on access to trusted data across more systems and more users faster than ever before. However, your business information is often incomplete, filled with errors, and beyond the reach of people who need it. Whether you’re responsible for technology or information strategy, you need to enable your business to have real-time access to reliable information. Otherwise, your company will be left behind. Download Trillium’s whitepaper, “How to Succeed in the New Age of Data Quality”, to learn how you can create a successful data quality strategy.
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Trillium Software
Published By: Experian     Published Date: May 17, 2016
Every year, Experian Data Quality conducts a study to look at the global trends in data quality. This year, research findings reveal how data practitioners are leveraging and managing data to generate actionable insight, and how proper data management is becoming an organization-wide imperative. This study polled more than 1,400 people across eight countries globally from a variety of roles and departments. Respondents were chosen based on their visibility into their orgazation's customer data management practices. Read through our research report to learn: - The changes in channel usage over the last 12 months - Expected changes in big data and data management initiatives - Multi-industry benchmarks, comparisons, and challenges in data quality - And more! Our annual global benchmark report takes a close look at the data quality and data management initiatives driving today's businesses. See where you line up and where you can improve.
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Experian
Published By: Reltio     Published Date: Feb 12, 2016
Reltio delivers reliable data, relevant insights and recommended actions so companies can be right faster. Reltio Cloud combines data-driven applications with modern data management for better planning, customer engagement and risk management. IT streamlines data management for a complete view across all sources and formats at scale, while sales, marketing and compliance teams use data-driven applications to predict, collaborate and respond to opportunities in real-time. Companies of all sizes, including leading Fortune 500 companies in healthcare and life sciences, distribution and retail rely on Reltio.
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Reltio
Published By: Silwood Technology     Published Date: Mar 02, 2016
Ever since organisations started to implement packaged software solutions to solve business problems and streamline their processes there has been a need to access their data for the purposes of reporting and analytics, integration, governance, master data and more. Information Management projects such as these rely on data professionals being able to understand the underlying data models for these packages in order to be able to answer the critical question “Where’s the data?”. Without this knowledge it is impossible to ensure accuracy of data or timely delivery of projects. In addition the lack of discovery tools designed to meet this challenge has meant that performing this task has commonly been frustrating, time-consuming and fraught with risk. This white paper offers insight into why the traditional methods are not effective and how an innovative software product from Silwood Technology provides a faster and more effective approach to solving the problem.
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Silwood Technology
Published By: Looker     Published Date: Mar 15, 2016
Data centralization merges different data streams into a common source through unified variables. This process can provide context to overly-broad metrics and enable cross-platform analytics to guide better business decisions. Investments in analytics tools are now paying back a 13.01:1 return on investment (ROI), with increased returns when these tools integrate with three or more data sour- ces. While the perks of centralization are obvious in theory, the quantity and variety of data available in today’s landscape make this difficult to achieve. This report provides a roadmap for how to connect systems, data stores, and institutions (both technological and human). Learn: • How data centralization enables better analytics • How to redefine data as a vehicle for change • How the right BI tool eliminates the data analyst bottleneck • How to define single sources of truth for your organization • How to build a data-driven (not just data-rich) organization
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Looker
Published By: IDERA     Published Date: Nov 07, 2017
Data modeling is all about data definition but has a much wider impact on the data of your organization. Quality data definition impacts how data is produced and directly impacts how the data is or will be used throughout an organization. That means that we must proactively govern the process of how we define data, to establish a common understanding across the team. In this whitepaper, Robert Seiner describes how data modeling is a form of data governance and provides insights on the three actions of governing data.
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IDERA
Published By: Infogix     Published Date: Apr 21, 2017
Data Governance and GDPR go Together Like Peanut Butter and Jelly May 2018 might sound far away, but any organization that does business in the EU must be prepared on day one to comply with the new General Data Protection Regulation (GDPR). This regulation carries stiff penalties for non-compliance – first time violators should expect to pay up to the greater of 4% of global annual revenue or 20 million EUR in fines – so it behooves organizations to cross their i's and dot their t's in regards to their GDPR plan. One integral component that is vital is instituting data governance to understand the organization’s data from a business perspective. Learn more about "What is considered Personally Identifiable Information?”, “What are the GDPR compliance obligations?”, and “Why data governance is vital?” in an easy to read white paper titled: General Data Protection Regulation (GDPR) and the Vital Role of Data Governance.
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Infogix
Published By: AtomRain     Published Date: Nov 07, 2017
The world is more connected than ever before, and data relationships only continue to multiply. Yet enterprises still operate largely with an incomplete perspective caused by segmented, non-contextual and disconnected data silos. Connected data is the key to surviving, growing and thriving. However, a transformation across the entire enterprise won’t happen overnight, and each step must be measurable from both a business and technical perspective. Organizations need expert guidance to move more swiftly and avoid costly technical pitfalls in the new paradigm. This paper examines the journey to what we call, “The Connected Enterprise”.
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AtomRain
Published By: Collibra     Published Date: Jul 09, 2018
Data governance can be a game changer when it comes to modern business. But leveraging data for strategic objectives is easier said than done. This new report, developed by the Economist Intelligence Unit, explores the challenges and opportunities for data governance both globally and across industries. Based on a survey of over 500 business executives and complemented by in-depth interviews, this report reveals: • What's working in data governance — and what's not • Why organizations must shift their thinking to data governance on offense • How to overcome barriers to better data governance
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Collibra
Published By: Syncsort     Published Date: Jul 17, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: Syncsort     Published Date: Oct 25, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: R2C     Published Date: Jan 05, 2018
Consistent sharing of data across organizational boundaries is often hampered by a lack of transparency, visibility, and trust in the agreements made between parties who seek to share data assets. How does an organization with cultural barriers to sharing data assets engender trust in the process? Leveraging blockchain technology that “oraclizes” data sharing agreements may provide an answer.
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R2C
Published By: Octopai     Published Date: Sep 01, 2018
For many BI professionals, every task can feel like MISSION IMPOSSIBLE. All the manual mapping required to sort out inconsistencies in data and the lack of tools to simplify and shorten the process of finding and understanding data leaves BI groups frustrated and slows down the business. This whitepaper examines the revolutionary impact of automation on the cumbersome manual processes that have been dragging BI down for so long. • Data correction vs process correction • Root-cause analysis with data lineage: reverse-tracing the data flow • Data quality rules and data controls • Automated data lineage mapping
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Octopai
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