data warehouse

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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: Oracle EMEA     Published Date: Apr 15, 2019
Oracle Autonomous Data Warehouse Cloud is more than just a new way to store and analyze data; it’s a whole new approach to getting more value from your data. Market leaders in every industry depend on analytics to reach new customers, streamline business processes, and gain a competitive edge. Data warehouses remain at the heart of these business intelligence (BI) initiatives, but traditional data-warehouse projects are complex undertakings that take months or even years to deliver results. Relying on a cloud provider accelerates the process of provisioning data-warehouse infrastructure, but in most cases database administrators (DBAs) still have to install and manage the database platform, then work with the line-of-business leaders to build the data model and analytics. Once the warehouse is deployed—either on premises or in the cloud—they face an endless cycle of tuning, securing, scaling, and maintaining these analytic assets. Oracle has a better way. Download this whitepaper to f
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Oracle EMEA
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
As the foundation for most critical business decisions, today's data environments are not just a vital piece of IT infrastructure, but key component of corporate strategy.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
One of the biggest changes faces organizations making purchasing and deployment decisions about analytic databases -- including relational data warehouses -- is whether to opt for a cloud solution.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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Group M_IBM Q2'19
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
This report explores a new breed of data warehouse that can operate in a world of legacy on-premise systems while exploiting the potential of cutting edge technologies and deployment styles
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
In this paper, we focus on the DWA and how it has evolved over the years since its introduction. The XDW architecture is then described, in which the need to maintain the data warehouse is documented while adding new components and capabilities to extend the analytical capabilities. This section also discusses the appropriate usage of appliances within the XDW. The rest of the paper covers the benefits from implementing the DWA, the selection considerations for them and what the future holds for them.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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Group M_IBM Q119
Published By: Oracle     Published Date: Mar 01, 2019
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloud and what it can help organisations achieve. Talking about innovation,security and efficiency, they put the case for an autonomous future. Watch the webinar.
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Oracle
Published By: Oracle     Published Date: Mar 01, 2019
To keep up with rapid growth and stay ahead, disruptor fintechs must stay agile and go on innovating. Bangkok-based Forth Smart provides payment gateways that turn cash into digital currency via its thousands of vending machines. They needed to approach ITin new ways in order to free up budgets, resources and imaginations to focus on innovation. Oracle Cloud Specialist Marek Winiarski, talked to Forth Smart’s Data Scientist, Pawarit ‘Taa’ Ruengsuksilp about how the company has made cost savings and improved customer experience by adopting Oracle Autonomous Data Warehouse.
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Oracle
Published By: Attunity     Published Date: Feb 12, 2019
Read this checklist report, with results based on the Eckerson Group’s survey and the Business Application Research Center (BARC), on how companies using the cloud for data warehousing and BI has increased by nearly 50%. BI teams must address multiple issues including data delivery, security, portability and more before moving to the cloud for its infinite scalability and elasticity. Read this report to understand all 7 seven considerations – what, how and why they impact the decision to move to the cloud.
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cloud, business intelligence, analytics, cloud data, data lake, data warehouse automation tools, dwa, data warehouse, security and compliance, data movement, hybrid cloud, hybrid cloud environment, cross-platform automation, portability
    
Attunity
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: Google     Published Date: Jan 24, 2019
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions. This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
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Google
Published By: Attunity     Published Date: Jan 14, 2019
This whitepaper explores how to automate your data lake pipeline to address common challenges including how to prevent data lakes from devolving into useless data swamps and how to deliver analytics-ready data via automation. Read Increase Data Lake ROI with Streaming Data Pipelines to learn about: • Common data lake origins and challenges including integrating diverse data from multiple data source platforms, including lakes on premises and in the cloud. • Delivering real-time integration, with change data capture (CDC) technology that integrates live transactions with the data lake. • Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assemble a historical data store. • Leveraging a scalable and autonomous streaming data pipeline to deliver analytics-ready data sets for better business insights. Read this Attunity whitepaper now to get ahead on your data lake strategy in 2019.
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data lake, data pipeline, change data capture, data swamp, hybrid data integration, data ingestion, streaming data, real-time data, big data, hadoop, agile analytics, cloud data lake, cloud data warehouse, data lake ingestion, data ingestion
    
Attunity
Published By: Oracle     Published Date: Jan 09, 2019
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Oracle
Published By: Oracle     Published Date: Jan 09, 2019
Speed has become the necessary basis of competition. See how Oracle aims to help clients with this through their vision for autonomous database.
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Oracle
Published By: Oracle     Published Date: Jan 09, 2019
The Accenture Oracle data team has more than 20,000 professionals, who aid in delivering 50 billion transactions a day across more than three exabytes of data for clients globally. Accenture Oracle data specialists recently put the Oracle Autonomous Data Warehouse to a rigorous performance test to provide a real-life application usage experience. The data was then extrapolated and expanded to nine years’ worth of data to test the performance. Learn directly from Accenture experts about testing methodology and results that enable them to deliver more data intelligence faster to the enterprise and transform the way people live and work.
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Oracle
Published By: Oracle     Published Date: Dec 21, 2018
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloudand what it can help organisations achieve. Talking about innovation, security and efficiency, they put the casefor an autonomous future.
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Oracle
Published By: BMC ASEAN     Published Date: Dec 18, 2018
Big data projects often entail moving data between multiple cloud and legacy on-premise environments. A typical scenario involves moving data from a cloud-based source to a cloud-based normalization application, to an on-premise system for consolidation with other data, and then through various cloud and on-premise applications that analyze the data. Processing and analysis turn the disparate data into business insights delivered though dashboards, reports, and data warehouses - often using cloud-based apps. The workflows that take data from ingestion to delivery are highly complex and have numerous dependencies along the way. Speed, reliability, and scalability are crucial. So, although data scientists and engineers may do things manually during proof of concept, manual processes don't scale.
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BMC ASEAN
Published By: Google     Published Date: Oct 26, 2018
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions. This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
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Google
Published By: Group M_IBM Q418     Published Date: Oct 15, 2018
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data. Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
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Group M_IBM Q418
Published By: StreamSets     Published Date: Sep 24, 2018
The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.
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replatforming, age, data, lake, apache, hadoop
    
StreamSets
Published By: StreamSets     Published Date: Sep 24, 2018
Treat data movement as a continuous, ever-changing operation and actively manage its performance. Before big data and fast data, the challenge of data movement was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line.
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practices, modern, data, performance
    
StreamSets
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