database management technology

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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: Ted Hills     Published Date: Mar 29, 2016
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. Ted Hills has been active in the Information Technology industry since 1975. At LexisNexis, Ted co-leads the work of establishing enterprise data architecture standards and governance processes, working with data models and business and data definitions for both structured and unstructured data. His book, NoSQL and SQL Data Modeling, was recently released by Technics Publications (http://technicspub.com).
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Ted Hills
Published By: MarkLogic     Published Date: Jun 09, 2017
NoSQL means a release from the constraints imposed on database management systems by the relational database model. This quick, concise eBook provides an overview of NoSQL technology, when you should consider using a NoSQL database over a relational one (and when to use both). In addition, this book introduces Enterprise NoSQL and shows how it differs from other NoSQL systems. You’ll also learn the NoSQL lingo, which customers are already using it and why, and tips to find the right NoSQL database for you.
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MarkLogic
Published By: Skillsoft     Published Date: Apr 23, 2015
This paper explains how embedding 7 key skills within your IT team can keep your organisation’s data safe and its employees productive. From data storage and security to database design and management, these key skills will ensure that your business is able to make the most of new technology as it develops.
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fundamental it skills, it staff development, professional it development, skillsoft
    
Skillsoft
Published By: MarkLogic     Published Date: Nov 07, 2017
NoSQL means a release from the constraints imposed on database management systems by the relational database model. This quick, concise eBook provides an overview of NoSQL technology, when you should consider using a NoSQL database over a relational one (and when to use both). In addition, this book introduces Enterprise NoSQL and shows how it differs from other NoSQL systems. You’ll also learn the NoSQL lingo, which customers are already using it and why, and tips to find the right NoSQL database for you.
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MarkLogic
Published By: MemSQL     Published Date: Nov 15, 2017
FREE O'REILLY EBOOK: BUILDING REAL-TIME DATA PIPELINES Unifying Applications and Analytics with In-Memory Architectures You'll Learn: - How to use Apache Kafka and Spark to build real-time data pipelines - How to use in-memory database management systems for real-time analytics - Top architectures for transitioning from data silos to real-time processing - Steps for getting to real-time operational systems - Considerations for choosing the best deployment option
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hardware trends, data pipelines, database management, architectures, technology
    
MemSQL
Published By: IBM     Published Date: Aug 30, 2010
Adopting an Integrated Data Management strategy can be a highly effective way to help IT support business growth, facilitate data governance and manage costs.
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ibm, back-up, data quality, database management, knowledge and data management, storage management, data management, databases, enterprise software, hardware, information technology, software, storage, database development, database security, data warehousing
    
IBM
Published By: IBM     Published Date: May 02, 2014
This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
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ibm, big data, big sql, querying data, database management technology, apache hadoop, data administrators, infosphere, biginsights, industry-standard sql, management systems, database metadata, application programming interfaces, api, it management
    
IBM
Published By: IBM     Published Date: May 28, 2014
The ability to keep pace with trends like big data, open data, sophisticated analytics, and the de-compartmentalization of data silos and processes, distinguishes leading master data management (MDM) solutions from their counterparts. IBM, one of the five vendors in this evaluation, is positioned as THE leader with the most prominent, widely implemented solution in the market.
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ibm, forrester, master data management, big data, database management technology, data administrators, infosphere, biginsights, industry-standard sql, management systems, database metadata, application programming interfaces, api, data persistence, virtualize data, lifecycle management, big data strategy, it management, data management, data center
    
IBM
Published By: IBM     Published Date: Mar 05, 2014
If you specialize in relational database management technology, you’ve probably heard a lot about “big data” and the open source Apache Hadoop project. Perhaps you’ve also heard about IBM’s new Big SQL technology, which enables IBM® InfoSphere® BigInsights™ users to query Hadoop data using industry-standard SQL. Curious? This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
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ibm, big data, ibm big sql, sql, database management, database management technology, software, tables, queries, data platform, big sql architecture, programming language, relational database management system, rdbms, analytical applications, business intelligence, database development, data warehousing
    
IBM
Published By: IBM     Published Date: May 02, 2014
Learn more about Forrester’s results, and how these organizations are realizing both economic and operational benefits with InfoSphere Optim solutions.
Tags : 
ibm, big data, big sql, querying data, database management technology, apache hadoop, data administrators, infosphere, biginsights, industry-standard sql, management systems, database metadata, application programming interfaces, api, data persistence, virtualize data, lifecycle management, big data strategy, it management, data management
    
IBM
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