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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
This paper explores the differences between three situations that appear on the surface to be very similar: a data attribute that may occur zero or one times, a data attribute that is optional, and a data attribute whose value may be unknown. It shows how each of these different situations is represented in Concept and Object Modeling Notation (COMN, pronounced “common”). The theory behind the analysis is explained in greater detail by three papers: Three-Valued Logic, A Systematic Solution to Handling Unknown Data in Databases, and An Approach to Representing Non-Applicable Data in Relational Databases.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing unknown data, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing that data are not applicable, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: DATAVERSITY     Published Date: Feb 27, 2013
In its most basic definition, unstructured data simply means any form of data that does not easily fit into a relational model or a set of database tables.
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white paper, dataversity, unstructured data, enterprise data management, data, data management
    
DATAVERSITY
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: DATAVERSITY     Published Date: May 25, 2014
Deconstructing NoSQL: Analysis of a 2013 Survey on the Use, Production, and Assessment of NoSQL Technologies in the Enterprise This report examines the non-relational database environment from the viewpoints of those within the industry–whether current or future adopters, consultants, developers, business analysts, vendors, or others. This paper is sponsored by: MarkLogic, Cloudant and Neo4j.
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research paper, analysis, nosql, database, nosql database, white paper, nosql white paper
    
DATAVERSITY
Published By: Couchbase     Published Date: Jul 15, 2013
NoSQL database technology is increasingly chosen as viable alternative to relational databases, particularly for interactive web applications. Developers accustomed to the RDBMS structure and data models need to change their approach when transitioning to NoSQL. Download this white paper to learn about the main challenges that motivates the need for NoSQL, the differences between relational databases and distributed document-oriented databases, the key steps to perform document modeling in NoSQL databases, and how to handle concurrency, scaling and multiple-place updates in a non-relational database.
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white paper, database, nosql, couchbase
    
Couchbase
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: 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: MarkLogic     Published Date: Jun 17, 2015
Modern enterprises face increasing pressure to deliver business value through technological innovation that leverages all available data. At the same time, those enterprises need to reduce expenses to stay competitive, deliver results faster to respond to market demands, use real-time analytics so users can make informed decisions, and develop new applications with enhanced developer productivity. All of these factors put big data at the top of the agenda. Unfortunately, the promise of big data has often failed to deliver. With the growing volumes of unstructured and multi-structured data flooding into our data centers, the relational databases that enterprises have relied on for the last 40-years are now too limiting and inflexible. New-generation NoSQL (“Not Only SQL”) databases have gained popularity because they are ideally suited to deal with the volume, velocity, and variety of data that businesses and governments handle today.
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data, data management, databse, marklogic, column store, wide column store, nosql
    
MarkLogic
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: Cambridge Semantics     Published Date: Aug 17, 2015
As the quantity and diversity of relevant data grows within and outside of the enterprise, business users and IT are struggling to extract maximum value from this data. Current approaches, including the rigid relational data warehouse and the unwieldy Hadoop-only Data Lake, are limited in their ability to provide users and IT with the answers they need with the proper governance and security required. Read this whitepaper to learn how The Anzo Smart Data Lake from Cambridge Semantics solves these problems by disrupting the way IT and business alike manage and analyze data at enterprise scale with unprecedented flexibility, insight and speed.
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Cambridge Semantics
Published By: Basho     Published Date: Mar 08, 2015
Many companies still use relational databases as part of the technology stack. However, others are innovating and incorporating NoSQL solutions and as a result they have simplified their deployments, enhanced their availability and reduced their costs. In this whitepaper you will learn: - Why companies choose Riak over a relational database. - How to analyze the decision points you should consider when choosing between relational and Nosql databases - Simple patters for building common applications in Riak using its key/value design Learn how you can lead your organization into this new frontier.
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data, data management, basho, database, nosql, data models
    
Basho
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: Amazon Web Services     Published Date: Apr 04, 2016
Amazon DynamoDB is a fully managed, NoSQL database service. Many workloads implemented using a traditional Relational Database Management System (RDBMS) are good candidates for a NoSQL database such as DynamoDB. This whitepaper details the process for identifying these candidate workloads and planning and executing a migration to DynamoDB.
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Amazon Web Services
Published By: MariaDB     Published Date: Apr 02, 2018
Learn how to use JSON functions for semi-structured data This white paper explains step-by-step how to support semi-structured data with JSON functions, introduced in MariaDB Server 10.2, using a practical use case with sample data and queries for everything from creating, reading and querying JSON documents to enforcing data integrity with check constraints and functions. You will learn how to: • Create, read and update JSON documents • Index and query JSON documents • Enforce data integrity with JSON documents • Combine relational data and JSON documents • Return JSON documents as relational data • Return relational data as JSON documents
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MariaDB
Published By: MarkLogic     Published Date: Sep 01, 2015
Organizations face a growing inability to handle the massive volumes of disparate, varied, and changing data with the relational databases that have been relied on for the past three decades. This white paper provides a detailed discussion of the key reasons why relational databases are ill-suited to address the challenges with today’s data, and an overview of how new kinds of databases solve those challenges.
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MarkLogic
Published By: EnterpriseDB     Published Date: Sep 02, 2014
This technical paper from EnterpriseDB reviews and illustrates Postgres’ NoSQL capabilities in the context of Postgres’ robust relational competencies. It describes performance tests that demonstrate that Postgres is a superior platform for handling most NoSQL workloads.
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white paper, nosql, database, enterprisedb, postgres
    
EnterpriseDB
Published By: Basho     Published Date: Nov 25, 2015
The landscape of Scalable Operational and Analytical Systems is changing and disrupting the norm of using relational databases for all workloads. With the growing need to process and analyze Big Data at Scale, the demand for alternative strategies has grown and has given rise to the emergence of NoSQL databases for scalable processing. Mike Ferguson, Managing Director of Intelligent Business Strategies, is an independent IT Analyst who specializes in Big Data, BI/Analytics, Data Management and Enterprise Business Integration. In this whitepaper he will discuss the movement towards NoSQL databases for scalable operational and analytical systems, what’s driving Big Data analytics from Hadoop to the emergence of Apache Spark, the value of operational analytics and the importance of in-memory processing, and why use Apache Spark as your in-memory analytical platform for operational analytics.
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Basho
Published By: Couchbase     Published Date: Jan 23, 2013
Navigating the Transition From Relational to NoSQL Technology looks at the differences between relational and document database technology, highlights the implications of those differences for application developers, and provides guidance.
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big data, white paper, analytics
    
Couchbase
Published By: MarkLogic     Published Date: Jun 16, 2013
The primary issue discussed within this paper boils down to two disparate database reliability models: ACID vs BASE. The first (ACID) has been around for some 30+ years, is a proven industry standard for SQL-centric and other relational databases, and works remarkably well in the older, yet still extant, world of vertical scaling. The second (BASE) has only recently gained popularity over the past 10 years or so, especially with the rise of social networking, Big Data, NoSQL, and other leviathans in the new world of Data Management. BASE requirements rose out of a need for ever-expanding horizontally scaled distributed networks, with non-relational data stores, and the real-time availability constraints of web-based transaction processing. While there are now more crossovers and negotiations between the two models, they essentially represent two competing groups, with Brewer’s CAP Theorem acting as the referee in the middle forcing tough decisions on each team.
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data, data management, unstructured data, nosql, database, acid, base, database transactioning
    
MarkLogic
Published By: Splice Machine     Published Date: May 19, 2014
SQL-on-Hadoop solutions have become very popular recently as companies solve the data access issues with Hadoop or seek a scale-out alternative for traditional relational database management systems. However, with all of the options available, choosing which solution is right for your business can be a daunting task. This white paper discusses the options you should consider and questions to ask, including: Is it really “Real-Time”? Is it true SQL? Does it support secondary indexes? Can it efficiently handle sparse data? Can it deliver fast performance on massive joins? Read this white paper to get a better understanding of the SQL-on-Hadoop landscape and what questions you should ask to identify best solution for your business.
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white paper, splice machine, sql, hadoop, nosql, nosql white paper, hadoop white paper, dataversity
    
Splice Machine
Published By: Datastax     Published Date: Dec 27, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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Datastax
Published By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
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database usage, database management, server usage, data protection
    
Hewlett Packard Enterprise
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