enterprise data warehouse

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Published By: Zaloni     Published Date: Apr 24, 2019
Why your data catalog won’t deliver significant ROI According to Gartner, organizations that provide access to a curated catalog of internal and external data assets will derive twice as much business value from their analytics investments by 2020 than those that do not. That’s a ringing endorsement of data catalogs, and a growing number of enterprises seem to agree. In fact, the global data catalog market is expected to grow from US$210.0 million in 2017 to US$620.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 24.2%. Why such large and intensifying demand for data catalogs? The primary driver is that many organizations are working to modernize their data platforms with data lakes, cloud-based data warehouses, advanced analytics and various SaaS applications in order to grow profitable digital initiatives. To support these digital initiatives and other business imperatives, organizations need more reliable, faster access to their data. However, modernizing data plat
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Zaloni
Published By: Oracle     Published Date: Nov 28, 2017
Today’s leading-edge organizations differentiate themselves through analytics to further their competitive advantage by extracting value from all their data sources. Other companies are looking to become data-driven through the modernization of their data management deployments. These strategies do include challenges, such as the management of large growing volumes of data. Today’s digital world is already creating data at an explosive rate, and the next wave is on the horizon, driven by the emergence of IoT data sources. The physical data warehouses of the past were great for collecting data from across the enterprise for analysis, but the storage and compute resources needed to support them are not able to keep pace with the explosive growth. In addition, the manual cumbersome task of patch, update, upgrade poses risks to data due to human errors. To reduce risks, costs, complexity, and time to value, many organizations are taking their data warehouses to the cloud. Whether hosted lo
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Oracle
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: Infosys     Published Date: May 30, 2018
Enterprises often accord the lowest priority for modernizing systems running business-critical applications, for fear of disruption of business as well as the time it would take for the new system to stabilize and come up to speed. A large telecom company had the same fears when they decided to modernize the reporting data warehouse which produced reports critical for making business decisions. See how Infosys helped and the five key takeaways from the project.
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data, fortress, modernize, business, applications, telecom
    
Infosys
Published By: Dell Bedrock     Published Date: Feb 17, 2020
To run a digital business that is Intelligent and agile, your enterprise requires an IT landscape capable of connecting the front and back offices. In other words, the ability to engage customers depends on the oftenunseen capabilities in the data center. To remain on a path to digital transformation and capitalize on innovations in AI and machine learning (ML), companies are migrating their enterprise resource planning (ERP) and business warehouse (BW) workloads to SAP HANA and SAP S/4HANA. For enterprises running legacy SAP solutions, with an end of service life after 2025, the timetable for migration is rapidly approaching given that the average implementation is several years. IT decision makers (ITDMs) begin this journey by articulating the business case for why they should modernize their IT landscape for SAP and what factors they should consider along the way.
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Dell Bedrock
Published By: Oracle EMEA     Published Date: Apr 15, 2019
Emerging technologies and automation permeate every aspect of our work and lives today. The real opportunity of these technologies — which include artificial intelligence (AI), machine learning, the Internet of Things (IoT), and human interfaces — is to enable us to embrace innovation on a scale never seen before. These technologies help us reimagine what’s possible in work and in life - from self-driving cars and personalized medicine to precision agriculture and smart cities that are changing the way we experience our world. Autonomous opens a new world of opportunities for enterprises. Autonomous Database for Dummies consists of five chapters that describe emerging technology trends and the business value of autonomous. Download this whitepaper to discover the business value of autonomous, Deploy a data warehouse in seconds and more!
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Oracle EMEA
Published By: Red Hat     Published Date: Jan 09, 2014
A large enterprise data warehouse company used Red Hat® CloudForms to create a private cloud that includes automated provisioning and self-service for developers and testers. This let them build, test, and release new product versions faster. Find out how in this case study.
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red hat, cloudforms, data, data warehouse, enterprise cloud, cloud management, productivity, private cloud, service delivery, lifecycle management, business technology
    
Red Hat
Published By: DELL IS Cloud     Published Date: Apr 07, 2020
To run a digital business that is Intelligent and agile, your enterprise requires an IT landscape capable of connecting the front and back offices. In other words, the ability to engage customers depends on the oftenunseen capabilities in the data center. To remain on a path to digital transformation and capitalize on innovations in AI and machine learning (ML), companies are migrating their enterprise resource planning (ERP) and business warehouse (BW) workloads to SAP HANA and SAP S/4HANA. For enterprises running legacy SAP solutions, with an end of service life after 2025, the timetable for migration is rapidly approaching given that the average implementation is several years. IT decision makers (ITDMs) begin this journey by articulating the business case for why they should modernize their IT landscape for SAP and what factors they should consider along the way. Learn more about Dell Technologies solutions powered by Intel®
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DELL IS Cloud
Published By: TIBCO Software     Published Date: Mar 17, 2020
Data science and machine learning are key technologies for enterprises that want to take advantage of the massive insights buried in their data marts, data warehouses, Apache Hadoop lakes, and spreadsheets. But, despite the millions of dollars invested in analytics technologies, the majority of companies still struggle to establish an efficient and programmatic way to do analytics at scale. According to Gartner Inc., over 60% of models developed with the intention of operationalizing them were never actually operationalized. Why are these investments failing to meet expectations? In this paper, we delve into today's most common data science and ML myths and offer potential solutions.
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TIBCO Software
Published By: Attunity     Published Date: Feb 12, 2019
How can enterprises overcome the issues that come with traditional data warehousing? Despite the business value that data warehouses can deliver, too often they fall short of expectations. They take too long to deliver, cost too much to build and maintain, and cannot keep pace with changing business requirements. If this all rings a bell, check out Attunity’s knowledge brief on data warehouse automation with Attunity Compose. The solution automates the design, build, and deployment of data warehouses, data marts and data hubs, enabling more agile and responsive operation. The automation reduces time-consuming manual coding, and error-prone repetitive tasks. Read the knowledge brief to learn more about your options.
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dwa, data warehouse automation, etl development, extract transform load tools, etl tools, data warehouse, data marts, data hubs data warehouse lifecycle, data integration, change management, data migration, consolidating data, cloud data warehousing, data warehouse design, attunity compose
    
Attunity
Published By: EMC Corporation     Published Date: Jul 07, 2013
Forward-looking enterprises know there's more to big data than strong and managing large volumes of information. Big data presents an opportunity to leverage analytics and experiment with all available data to derive value never before possible with traditional business intelligence and data warehouse platforms. Through a modern, big data platform that facilitates self-service and collaborative analytics across all data, organizations become more agile and are able to innovate in new ways.
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enterprises, storage, information management, technology, platform, big data analytics, emc, self service, business intelligence, security, knowledge management, enterprise applications, data management, business technology
    
EMC Corporation
Published By: Google     Published Date: Feb 11, 2020
ESG created a three-year TCO model that compared the expected costs and benefits of upgrading an on-premises EDW solution from the leading vendor, migrating to a cloud-based solution provided by this vendor on AWS, or redesigning and migrating the EDW function to Google BigQuery. ESG found that our modeled organization can reduce their overall three-year costs by 52% when compared to the on premises solution and by 41% when compared to the solution on AWS.
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Google
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