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Published By: CData     Published Date: Jan 04, 2019
The growth of NoSQL continues to accelerate as the industry is increasingly forced to develop new and more specialized data structures to deal with the explosion of application and device data. At the same time, new data products for BI, Analytics, Reporting, Data Warehousing, AI, and Machine Learning continue along a similar growth trajectory. Enabling interoperability between applications and data sources, each with a unique interface and value proposition, is a tremendous challenge. This paper discusses a variety of mapping and flattening techniques, and continues with examples that highlight performance and usability differences between approaches.
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data architecture, data, data management, business intelligence, data warehousing
    
CData
Published By: MEGA International     Published Date: Mar 07, 2019
The effectiveness of agile processes is often jeopardized because the architecture and organizational pre-requisites of agility are neglected. This White Paper proposes a new architecture framework, the Agile Architecture Framework (AAF), that meets the needs of the digital enterprise. It develops a vision that uniquely combines: • Methods for decomposing the system into loosely-coupled services and autonomous teams • Alignment mechanisms rooted in business strategy • Architecture patterns that leverage the latest software innovations • Results from large enterprises that started their agile-at-scale journey several ago This proposed architecture approach will enable organizations at all scales to better realize the Boundaryless Information Flow™ vision achieved through global interoperability in a secure, reliable, and timely manner.
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MEGA International
Published By: DATAVERSITY     Published Date: Nov 11, 2013
This report investigates the level of Information Architecture (IA) implementation and usage at the enterprise level. The primary support for the report is an analysis of a 2013 DATAVERSITY™ survey on Data and Information Architecture. This paper is sponsored by: HP, Vertica, Denodo, Embarcadero and CA Technologies.
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information architecture, data architecture, white paper, mdm, master data management, data, data management, enterprise information management, enterprise data management, data virtualization, metadata, data modeling, research paper, survey, data integration
    
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: Trillium Software     Published Date: Oct 26, 2015
Acting Quickly – Or Not at All The pace of business is accelerating. Enterprises must do more things, do them more quickly – and then adjust to market and competitive forces and do them differently. They must adapt in order to remain differentiated, and with that differentiation, hopefully build and sustain competitive advantage.
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Trillium Software
Published By: DATAVERSITY     Published Date: Oct 12, 2017
The foundation of this report is a survey conducted by DATAVERSITY® that included a range of different question types and topics on the current state of Data Architecture. The report evaluates the topic through a discussion and analysis of each presented survey question, as well as a deeper examination of the present and future trends.
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data architecture, data, data management
    
DATAVERSITY
Published By: SAP     Published Date: May 19, 2016
SAP® solutions for enterprise information management (EIM) support the critical abilities to architect, integrate, improve, manage, associate, and archive all information. By effectively managing enterprise information, your organization can improve its business outcomes. You can better understand and retain customers, work better with suppliers, achieve compliance while controlling risk, and provide internal transparency to drive operational and strategic decisions.
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SAP
Published By: Couchbase     Published Date: Dec 04, 2014
Interactive applications have changed dramatically over the last 15 years. In the late ‘90s, large web companies emerged with dramatic increases in scale on many dimensions: · The number of concurrent users skyrocketed as applications increasingly became accessible · via the web (and later on mobile devices). · The amount of data collected and processed soared as it became easier and increasingly · valuable to capture all kinds of data. · The amount of unstructured or semi-structured data exploded and its use became integral · to the value and richness of applications. Dealing with these issues was more and more difficult using relational database technology. The key reason is that relational databases are essentially architected to run a single machine and use a rigid, schema-based approach to modeling data. Google, Amazon, Facebook, and LinkedIn were among the first companies to discover the serious limitations of relational database technology for supporting these new application requirements. Commercial alternatives didn’t exist, so they invented new data management approaches themselves. Their pioneering work generated tremendous interest because a growing number of companies faced similar problems. Open source NoSQL database projects formed to leverage the work of the pioneers, and commercial companies associated with these projects soon followed. Today, the use of NoSQL technology is rising rapidly among Internet companies and the enterprise. It’s increasingly considered a viable alternative to relational databases, especially as more organizations recognize that operating at scale is more effectively achieved running on clusters of standard, commodity servers, and a schema-less data model is often a better approach for handling the variety and type of data most often captured and processed today.
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database, nosql, data, data management, white paper, why nosql, couchbase
    
Couchbase
Published By: Embarcadero     Published Date: Apr 29, 2015
Everything about data has changed, but that only means that data models are even more essential to understanding that data so that businesses can know what it means. As development methodologies change to incorporate Agile workflows, data architects must adapt to ensure models stay relevant and accurate. This whitepaper describes key requirements for Agile data modeling and shows how ER/Studio supports this methodology.
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data, data management, data modeling, agile, agile data modeling, it management
    
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: CA Technologies     Published Date: Dec 03, 2015
This 2nd paper in a 3-part series by David Loshin explores some challenges in bootstrapping a data governance program, and then considers key methods for using metadata to establish the starting point for data governance. The paper will focus on how metadata management facilitates progress along three facets of the data governance program including assessment, collaboration and operationalization.
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CA Technologies
Published By: MapR Technologies     Published Date: Mar 29, 2016
Add Big Data Technologies to Get More Value from Your Stack Taking advantage of big data starts with understanding how to optimize and augment your existing infrastructure. Relational databases have endured for a reason – they fit well with the types of data that organizations use to run their business. These types of data in business applications such as ERP, CRM, EPM, etc., are not fundamentally changing, which suggests that relational databases will continue to play a foundational role in enterprise architectures for the foreseeable future. One area where emerging technologies can complement relational database technologies is big data. With the rapidly growing volumes of data, along with the many new sources of data, organizations look for ways to relieve pressure from their existing systems. That’s where Hadoop and NoSQL come in.
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MapR Technologies
Published By: MapR Technologies     Published Date: Aug 01, 2018
How do you get a machine learning system to deliver value from big data? Turns out that 90% of the effort required for success in machine learning is not the algorithm or the model or the learning - it's the logistics. Ted Dunning and Ellen Friedman identify what matters in machine learning logistics, what challenges arise, especially in a production setting, and they introduce an innovative solution: the rendezvous architecture. This new design for model management is based on a streaming approach in a microservices style. Rendezvous addresses the need to preserve and share raw data, to do effective model-to-model comparisons and to have new models on standby, ready for a hot hand-off when a production model needs to be replaced.
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MapR Technologies
Published By: MapR Technologies     Published Date: Aug 04, 2018
Legacy infrastructures simply cannot handle the workloads or power the applications that will drive business decisively forward in the years ahead. New infrastructure, new thinking and new approaches are in the offing, all driven by the mantra 'transform or die.' This book is meant for IT architects; developers and development managers; platform architects; cloud specialists; and big data specialists. For you, the goal is to help create a sense of urgency you can present to your CXOs and others whose buy-in is needed to make essential infrastructure investments along the journey to digital transformation.
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MapR Technologies
Published By: Information Asset, LLC     Published Date: Feb 11, 2014
An In-Depth Review of Data Governance Software Tools: Reference Architecture, Evaluation Criteria, and Vendor Landscape
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white paper, data governance, data, data management, data management white paper, data governance white paper
    
Information Asset, LLC
Published By: Cloudant - an IBM Company     Published Date: Mar 19, 2015
In a world where the pace of software development is faster and data and piling up, how you architect your data layer to ensure a global user base enjoys continual access to data is more important than ever. Download our new whitepaper now to explore - Why the new category of NoSQL databases has become a popular option - The various types of NoSQL databases available today - Differences and commonalities of NoSQL databases - Why you should consider implementing a NoSQL solution
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cloudant, dashdb, ibm, nosql, nosql database, database
    
Cloudant - an IBM Company
Published By: AnalytixDS     Published Date: Mar 21, 2017
AnalytiX DS Mapping Manager is an award winning platform that has enabled organizations worldwide to meet challenges, bring wide-spread collaboration, and put structure and governance in place, regardless of the size of their architecture, data, or user base. Scalable, adaptable, and value-added through its feature rich modules, it can make a difference as to whether you see BASEL III as a challenge or an opportunity.
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AnalytixDS
Published By: VoltDB     Published Date: Feb 12, 2016
The need for fast data applications is growing rapidly, driven by the IoT, the surge in machine-to-machine (M2M) data, global mobile device proliferation, and the monetization of SaaS platforms. So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple? In this report, Ryan Betts and John Hugg from VoltDB examine ways to develop apps for fast data, using pre-defined patterns. These patterns are general enough to suit both the do-it-yourself, hybrid batch/streaming approach, as well as the simpler, proven in-memory approach available with certain fast database offerings.
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VoltDB
Published By: WhereScape     Published Date: Mar 16, 2016
Industry expert Wayne Eckerson provides an overview of the emerging data warehouse automation market and outlines the value of using automation tools for developing data warehouses, data marts, analytical environments and big data platforms. Eckerson details WhereScape’s architecture—which enables a data-driven approach to automation. Eckerson also discusses how agility and automation together encourage iterative development and closer collaboration between business and IT.
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WhereScape
Published By: FairCom     Published Date: May 25, 2016
As companies embrace NoSQL as the “next big thing,” they are rightly cautious of abandoning their investment in SQL. The question a responsible developer or IT manager must investigate is “in which cases are each of these technologies, SQL and NoSQL, the appropriate solution?” For example, cloud provider BigStep offered this assessment: “NoSQL is not the best model for OLTP, ad hoc queries, complicated relationships among the data, and situations when stability and reliability outweigh the importance of speed.” While that statement may be true of many NoSQL databases, c-treeACE is the exception. Its unique, No+SQL architecture offers the advantages of SQL on top of a robust, high-performance NoSQL core engine. In this white paper, you'll read five ways c-treeACE breaks the NoSQL mold in terms of: • Data Integrity • Availability and Reliability • Complex Data Relationships • Flexible Queries • Performance
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FairCom
Published By: Semarchy     Published Date: Feb 12, 2018
This whitepaper covers a recently completed a ground-breaking industry wide survey of executives, architects, and business stakeholders from data-driven organizations by Enterprise Management Research in order to explore the growing role of the CDO, and to explore the various data management maturity levels of enterprise companies. This whitepaper explains how industry visionaries use data as an asset, and discusses the growing importance of data governance leadership. Additionally, it creates a data management maturity index to show how various companies match-up in their data management vision and capabilities. Finally, the whitepaper covers the top data-focused applications used and their average implementation timelines.
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Semarchy
Published By: CloverETL     Published Date: Nov 24, 2017
The volume of data is increasing by 40% per year (Source: IDC). In addition, the structure and quality of data differs vastly with a growing number of data sources. More agile ways of working with data are required. This whitepaper discusses the vast options available for managing and storing data using data architectures, and offers use cases for each architecture. Furthermore, the whitepaper explores the benefits, drawbacks and challenges of each data architecture and commonly used practices for building these architectures.
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CloverETL
Published By: Wave Computing     Published Date: Jul 06, 2018
This paper argues a case for the use of coarse grained reconfigurable array (CGRA) architectures for the efficient acceleration of the data flow computations used in deep neural network training and inferencing. The paper discusses the problems with other parallel acceleration systems such as massively parallel processor arrays (MPPAs) and heterogeneous systems based on CUDA and OpenCL, and proposes that CGRAs with autonomous computing features deliver improved performance and computational efficiency. The machine learning compute appliance that Wave Computing is developing executes data flow graphs using multiple clock-less, CGRA-based System on Chips (SoCs) each containing 16,000 processing elements (PEs). This paper describes the tools needed for efficient compilation of data flow graphs to the CGRA architecture, and outlines Wave Computing’s WaveFlow software (SW) framework for the online mapping of models from popular workflows like Tensorflow, MXNet and Caffe.
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Wave Computing
Published By: Attunity     Published Date: Oct 19, 2018
Change data capture (CDC) technology can modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems. To realize these benefits, enterprises need to understand how this critical technology works, why it’s needed, and what their Fortune 500 peers have learned from their CDC implementations. This book serves as a practical guide for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC. Read this book to understand: ? The rise of data lake, streaming and cloud platforms ? How CDC works and enables these architectures ? Case studies of leading-edge enterprises ? Planning and implementation approaches
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Attunity
Published By: graphgrid     Published Date: Oct 02, 2018
Whether it’s for a specific application, optimizing your existing operations, or innovating new customer services, graph databases are a powerful technology that turn accessing and analyzing your data into a competitive advantage. Graph databases resolve the Big Data limitations and free up data architects and developers to build amazing solutions that predict behaviors, enable data driven decisions and make insightful recommendations. Yet just as cars aren’t functional with only engines, graph databases require surrounding capabilities including ingesting multi-source data, building data models that are unique to your business needs, ease of data interaction and visualization, seamless co-existence with legacy systems, high performance search capabilities, and integration of data analysis applications. Collectively, this comprehensive data platform turns graph capabilities into tangible insights that drive your business forward.
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graphgrid
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