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Published By: Amazon Web Services     Published Date: Nov 14, 2018
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources. This e-book aims to provide you with expert tips on how to use Amazon Redshift Spectrum to increase performance and potentially reduce the cost of your queries.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Nov 15, 2018
Businesses are generating staggering amounts of data—and extracting the most value from this information is paramount. Amazon Redshift provides organizations what they’re looking for: Affordability and flexibility combined with a powerful feature set. Download our solution overview covering some of the best practices on loading data and making the most of Amazon Redshift, including: • Loading data for faster results • Querying data for gaining actionable insights • Creating a schema to forgo complicated queries, saving time
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Amazon Web Services
Published By: SAP     Published Date: May 18, 2014
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
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sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
    
SAP
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
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Amazon Web Services
Published By: Oracle CX     Published Date: Oct 19, 2017
The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty. The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors: Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression. Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It
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Oracle CX
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
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Amazon Web Services
Published By: Dell Server     Published Date: Aug 07, 2018
Drive a competitive advantage. Deploy and analyse more high-value data workloads faster with a 41% increase in transactions per second and a 50% reduction in average query response time with the Dell EMC PowerEdge Servers, powered by Intel® Xeon® Platinum processor.
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Dell Server
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
This ESG Lab review documents the results of recent testing of the Oracle SPARC M7 processor with a focus on in-memory database performance for the real-time enterprise. Leveraging new advanced features like columnar compression and on-ship in-memory query acceleration, ESG Lab compared the in-memory database performance of a SPARC T7 system with a SPARC M7 processor to an x86-based system.
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Oracle PaaS/IaaS/Hardware
Published By: Idera     Published Date: Nov 08, 2013
SQL Diagnostic Manager: Download Trial Today
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idera, sql diagnostic, free trial, download software, performance monitoring, query monitoring, history browsing, predictive alerting
    
Idera
Published By: Lucidworks     Published Date: Aug 24, 2016
In reality, we developed search technologies, not primarily as a front-end service but to alleviate the challenges of the back end: our data. Search grew in direct response to data problems. The search story can’t be told without the data story, and high-volume data would be nearly useless without search technology. Download this eBook to learn more about search technologies.
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search, search query, search technologies, search apps, data problems, ecommerce search
    
Lucidworks
Published By: Lucidworks     Published Date: Aug 24, 2016
Online search functionality should feel seamless. Type what you’re looking for, and watch it appear instantly—like magic. No stumbling through category hierarchies or landing pages; just fast, accurate, search results pointing you to exactly what you want. Unfortunately, ecommerce search isn’t quite there yet. Download this eBook to learn 12 must-have query types to optimize your organization's eCommerce search.
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query types, search engine optimization, seo, search functionality, search results, ecommerce search
    
Lucidworks
Published By: Oracle     Published Date: Jul 05, 2017
Every user’s first interaction with your website begins with a series of DNS queries. The Domain Name System (DNS) is a distributed internet database that maps human-readable names to IP addresses, ensuring users reach the correct website when entering a URL. DNS mappings are maintained in special-purpose servers called DNS nameservers. When a user enters your company’s URL, a DNS query is routed to a DNS nameserver containing the address mappings for your company’s internet domain.
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advanced traffic steering, global internet visibility, open programming interfaces, risk management tools, secondary dns
    
Oracle
Published By: Oracle     Published Date: Nov 30, 2017
Every user’s first interaction with your website begins with a series of DNS queries. The Domain Name System (DNS) is a distributed internet database that maps human-readable names to IP addresses, ensuring users reach the correct website when entering a URL. DNS mappings are maintained in special-purpose servers called DNS nameservers. When a user enters your company’s URL, a DNS query is routed to a DNS nameserver containing the address mappings for your company’s internet domain.
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traffic, dns, internet, visibility, programming, interfaces, optimization, routing
    
Oracle
Published By: Oracle Dyn     Published Date: Dec 06, 2017
Every user’s first interaction with your website begins with a series of DNS queries. The Domain Name System (DNS) is a distributed internet database that maps human-readable names to IP addresses, ensuring users reach the correct website when entering a URL. DNS mappings are maintained in special-purpose servers called DNS nameservers. When a user enters your company’s URL, a DNS query is routed to a DNS nameserver containing the address mappings for your company’s internet domain.
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traffic, dns, internet, visibility, programming, interfaces, optimization, routing
    
Oracle Dyn
Published By: FICO     Published Date: Jun 07, 2016
With the increasing mainstream adoption of big data infrastructure — highly distributed file storage and query tools — more businesses are taking a new look at what business intelligence and analytics can do to grow revenue, increase profits and ultimately develop strategic relationships with customers.
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FICO
Published By: IBM     Published Date: Jan 27, 2017
In today’s highly distributed, multi-platform world, the data needed to solve any particular decision making need is increasingly likely to be found across a wide variety of sources. As a result, traditional manual approaches requiring prior collection, storage and integration of extensive sets of data in the analyst’s preferred exploration environment are becoming less useful. Data virtualization, which offers transparent access to distributed, diverse data sources, offers a valuable alternative approach in these circumstances.
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IBM
Published By: MarkLogic     Published Date: Nov 08, 2016
In this report, you will learn: 1. Why semantic technology is gaining traction now 2. How semantics is solving major challenges in the real world 3. How to model data as RDF and query with SPARQL Download now!
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MarkLogic
Published By: Oracle + Dyn     Published Date: Jun 27, 2017
"DNS has evolved from a set-it and forget it solution into a mission-critical layer of your online infrastructure. The capabilities of DNS go far beyond speeds, feeds and query resolution. Download this “Rethink DNS” eBook and discover smarter ways to use DNS to optimize performance, build resiliency and manage traffic across hybrid cloud environments. This eBook covers: • Why DNS is central to the user experience • Costs and revenue impact of latency or outages • Risks and challenges of in-house and managed DNS services • Selecting the right DNS provider for your business Download eBook! "
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Oracle + Dyn
Published By: Oracle     Published Date: Oct 20, 2017
The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty. The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors: Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression. Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It
Tags : 
    
Oracle
Published By: Lucidworks     Published Date: Jun 24, 2015
Using content, context, and the crowd to build better search applications.
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search platform, search query, search technologies, big data, high-volume data, database advancements, it management, knowledge management, platforms, data management, intent, context
    
Lucidworks
Published By: Lucidworks     Published Date: Aug 13, 2015
12 Must have query types for Ecommerce search.
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lucidworks, query, search, ecommerce, conversion rate, transactions, search marketing, emerging marketing, internet marketing, crm & customer care, e-commerce
    
Lucidworks
Published By: Amazon Web Services     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
Tags : 
    
Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set: • Enterprise-class relational database query and management system • Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools • Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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Amazon Web Services
Published By: MuleSoft     Published Date: Jul 06, 2017
In a microservice architecture a query or command will, by the very nature of the architecture’s functional decomposition, result in chains of API calls and messages across multiple microservice instances. Regardless of where a particular microservice sits in the chain, we must not lose sight of the user at the front end.
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microservice architecture, chains of api, user
    
MuleSoft
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