memory analytics

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Published By: SAP     Published Date: May 18, 2014
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
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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: SAP     Published Date: May 18, 2014
Download this whitepaper to learn the results of this latest exploration of the emerging world of in-memory database technologies.
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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: SAP     Published Date: May 18, 2014
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.
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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: SAP     Published Date: May 18, 2014
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
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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: SAP     Published Date: May 18, 2014
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
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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: SAP     Published Date: May 18, 2014
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
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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: SAP     Published Date: May 18, 2014
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
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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: SAP     Published Date: May 18, 2014
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
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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, it management, knowledge management
    
SAP
Published By: SAP     Published Date: May 18, 2014
This paper explores the results of a survey, fielded in April 2013, of 304 data managers and professionals, conducted by Unisphere Research, a division of Information Today Inc. It revealed a range of practical approaches that organizations of all types and sizes are adopting to manage and capitalize on the big data flowing through their enterprises.
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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: 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: SAP     Published Date: May 18, 2014
Over the course of several months in 2011, IDC conducted a research study to identify the opportunities and challenges to adoption of a new technology that changes the way in which traditional business solutions are implemented and used. The results of the study are presented in this white paper.
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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: SAP     Published Date: May 18, 2014
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
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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: SAP     Published Date: May 18, 2014
This white paper, produced in collaboration with SAP, provides insight into executive perception of real-time business operations in North America. It is a companion paper to Real-time Business: Playing to win in the new global marketplace, published in May 2011, and to a series of papers on real-time business in Europe, Asia-Pacific and Latin America.
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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: SAP     Published Date: May 18, 2014
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.
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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: 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: Oracle CX     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle CX
Published By: SAP     Published Date: Dec 04, 2015
Download this whitepaper to see how advanced technologies such as big data, cloud computing, mobile devices, and enterprise access to in-memory platforms, predictive analytics, and planning software can help CFOs make better and more sophisticated use of data, influence decisions, and take practical, timely action.
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finance function, finance, cfo, big data, cloud computing, mobile, in-memory platforms, predictive analytics, planning software
    
SAP
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"With the introduction of Oracle Database In-Memory and servers with the SPARC S7 and SPARC M7 processors Oracle delivers an architecture where analytics are run on live operational databases and not on data subsets in data warehouses. Decision-making is much faster and more accurate because the data is not a stale subset. And for those moving enterprise applications to the cloud, Real-time analytics of the SPARC S7 and SPARC M7 processors are available both in a private cloud on SPARC servers or in Oracle’s Public cloud in the SPARC cloud compute service. Moving to the Oracle Public Cloud does not compromise the benefits of SPARC solutions. Some examples of utilizing real time data for business decisions include: analysis of supply chain data for order fulfillment and supply optimization, analysis of customer purchase history for real time recommendations to customers using online purchasing systems, etc. "
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Oracle PaaS/IaaS/Hardware
Published By: IBM     Published Date: Jul 21, 2016
IBM's recently released DB2 version 11.1 for Linux, Unix and Windows (LUW) is a hybrid database that IBM says can handle transactional and analytic workloads thanks to its BLU Acceleration technology, which features an in-memory column store for analytical workloads that can scale across a massively parallel cluster.
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ibm, db2. analytics, mpp, data wharehousing
    
IBM
Published By: IBM     Published Date: Jul 06, 2017
DB2 is a proven database for handling the most demanding transactional workloads. But the trend as of late is to enable relational databases to handle analytic queries more efficiently by adding an inmemory column store alongside to aggregate data and provide faster results. IBM's BLU Acceleration technology does exactly that. While BLU isn't brand new, the ability to spread the column store across a massively parallel processing (MPP) cluster of up to 1,000 nodes is a new addition to the technology. That, along with simpler monthly pricing options and integration with dashDB data warehousing in the cloud, makes DB2 for LUW, a very versatile database.
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memory analytics, database, efficiency, acceleration technology, aggregate data
    
IBM
Published By: Amazon Web Services     Published Date: Apr 16, 2018
Since SAP introduced its in-memory database, SAP HANA, customers have significantly accelerated everything from their core business operations to big data analytics. But capitalizing on SAP HANA’s full potential requires computational power and memory capacity beyond the capabilities of many existing data center platforms. To ensure that deployments in the AWS Cloud could meet the most stringent SAP HANA demands, AWS collaborated with SAP and Intel to deliver the Amazon EC2 X1 and X1e instances, part of the Amazon EC2 Memory-Optimized instance family. With four Intel® Xeon® E7 8880 v3 processors (which can power 128 virtual CPUs), X1 offers more memory than any other SAP-certified cloud native instance available today.
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Amazon Web Services
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: Oracle     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle
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: HP Enterprise System     Published Date: Apr 03, 2014
SAP HANA is a powerful, in-memory computing platform that streamlines business suite applications, analytics, planning, predictive analysis, and sentiment analysis on a single platform, so businesses can operate in real time. The design approach for enterprise-level solutions involving SAP HANA, and the best practices surrounding them, isn’t intrinsically different from the approach to any other enterprise-level solution for technology implementations. This paper is written to address those elements of good solution design and apply them to the SAP landscape, with particular focus on the SAP HANA element.
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sap hana, as a service solution, solutions, cost effective, operational management, accelerate value, enterprise applications, data management
    
HP Enterprise System
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