unstructured data

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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: 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: 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: 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: Expert System     Published Date: Mar 19, 2015
Establishing context and knowledge capture In today’s knowledge-infused world, it is vitally important for organizations of any size to deploy an intuitive knowledge platform that enables delivery of the right information at the right time, in a way that is useful and helpful. Semantic technology processes content for meaning, allowing for the ability to understand words in context: it allows for better content processing and interpretation, therefore enabling content organization and navigation, which in turn increases findability.
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enterprise data management, unstructured data, semantic technology, expert system
    
Expert System
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: 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: Amazon Web Services     Published Date: Jul 25, 2018
What is a Data Lake? Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. Download to find out more now.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Defining the Data Lake “Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
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Amazon Web Services
Published By: Splunk     Published Date: Sep 10, 2018
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential. Download this white paper to learn: *How organizations can answer critical questions that have been impeding business success *How the financial services industry can make great strides in security, compliance and IT *Common machine data sources in financial services firms
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Splunk     Published Date: Sep 10, 2018
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers. For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy. Download this report to learn: How to reduce monitoring blind spots when creating an ITOM strategy How to address ITOM requirements across IT and non-IT groups Distinct layers across ITOM Potential functionality gaps with domain-specific products
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cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
    
Splunk
Published By: Attivio     Published Date: Aug 20, 2010
With the explosion of unstructured content, the data warehouse is under siege. In this paper, Dr. Barry Devlin discusses data and content as two ends of a continuum, and explores the depth of integration required for meaningful business value.
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attivio, data warehouse, unified information, data, content, unstructured content, integration, clob, blob, business intelligence, database development, data quality, data warehousing
    
Attivio
Published By: SAP     Published Date: Feb 03, 2017
The SAP HANA platform provides a powerful unified foundation for storing, processing, and analyzing structured and unstructured data. It funs on a single, in-memory database, eliminating data redundancy and speeding up the time for information research and analysis.
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SAP
Published By: Dell EMC     Published Date: May 23, 2017
The rapid growth of unstructured data poses significant challenges to store, manage, secure and protect data across virtually every industry segment. You need a way to manage your data: simply, securely and cost-effectively.
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Dell EMC
Published By: Amazon Web Services     Published Date: Dec 15, 2017
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time. AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance. In this webinar, you’ll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences. Join this webinar to learn: How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development. How AWS supports BI solutions f
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Amazon Web Services
Published By: Infosys     Published Date: May 30, 2018
Customers today are far more concerned about the contents and origin of a product than ever before. in such a scenario, granting them easy access to product information, via digital initiatives such as SmartLabel™, goes a long way in strengthening customer trust in a brand. But it also means expending several man-hours of effort processing unstructured data, with the possibility of human error. Intelligent automation can help save effort and time, with virtually error-free results. A consumer products conglomerate wanted a smart solution to implement SmartLabel™ compliance. See how Infosys helped and the five key takeaways from the project.
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automation, brand, information, digital, customer
    
Infosys
Published By: IBM     Published Date: Jun 29, 2018
LinuxONE from IBM is an example of a secure data-serving infrastructure platform that is designed to meet the requirements of current-gen as well as next-gen apps. IBM LinuxONE is ideal for firms that want the following: ? Extreme security: Firms that put data privacy and regulatory concerns at the top of their requirements list will find that LinuxONE comes built in with best-in-class security features such as EAL5+ isolation, crypto key protection, and a Secure Service Container framework. ? Uncompromised data-serving capabilities: LinuxONE is designed for structured and unstructured data consolidation and optimized for running modern relational and nonrelational databases. Firms can gain deep and timely insights from a "single source of truth." ? Unique balanced system architecture: The nondegrading performance and scaling capabilities of LinuxONE — thanks to a unique shared memory and vertical scale architecture — make it suitable for workloads such as databases and systems of reco
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IBM
Published By: IBM APAC     Published Date: Nov 22, 2017
It is imperative that organizations start looking now for smarter solutions to the problems associated with unstructured data. There are many issues related to unstructured data that need to be readily addressed such as storage, discovery, organization, tagging and deduplication. One of the most important issues related to unstructured data is finding and discovering key business insights as quickly as possible, preferably in near real time, to gain a significant competitive advantage.
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storage, discovery, organization, tagging, deduplication, competitive, advantage
    
IBM APAC
Published By: Dell EMC     Published Date: Oct 08, 2015
Download this whitepaper to learn more about how to capture, analyze and manage a tidal wave of structured and unstructured data, turn this data into operational intelligence, And how to overcome the limitations of databases and data management tools that weren’t designed for a world of big data with Dell.
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Dell EMC
Published By: SAS     Published Date: Apr 20, 2015
This paper explores how organizations are leveraging unstructured data—and what they must avoid.
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SAS
Published By: Cleversafe     Published Date: Dec 07, 2012
Dispersed Storage is an innovative approach for cost-effectively storing large volumes of unstructured data while ensuring security, availability and reliability.
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dispersed, storage, unstructured, availability, reliability, large volumes, it management, business technology, data center
    
Cleversafe
Published By: MarkLogic     Published Date: Aug 31, 2017
With the proliferation of IT infrastructure and the rapid rise of unstructured data, navigating the complexities of complying with the EU Regulation MiFID II can be overwhelming. But the first steps to compliance involve addressing your data management challenges head on.
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MarkLogic
Published By: Dell EMC     Published Date: Aug 22, 2017
Dell EMC Isilon® scale-out NAS is the ideal platform to store, manage, protect and analyze your unstructured data. Isilon is the only platform that scales capacity and performance in minutes – allowing you to infinitely consolidate unstructured data, cut costs, and gain new levels of agility and insight to accelerate your business.
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Dell EMC
Published By: Dell EMC     Published Date: Aug 22, 2017
This Lab Validation Report documents the results of recent hands-on testing of Dell EMC Isilon All-Flash storage. Testing focused on the platform’s performance and scalability and how it enables organizations to support more applications and workloads with flash based on their ever-increasing volumes of unstructured data.
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Dell EMC
Published By: Dell EMC     Published Date: Aug 22, 2017
Identifying the benefits, costs, and risks associated with an Isilon implementation, Forrester interviewed several customers with experience using Isilon. Dell EMC Isilon is a scale-out NAS platform that enables organizations to store, manage, and analyze unstructured data. Isilon clusters are composed of different node types that can scale up to 68 petabytes (PB) in a single cluster while maintaining management simplicity. Isilon clusters can also scale to edge locations and the cloud
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Dell EMC
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