data warehouses

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Unifying Analytics Across Warehouse, Lake and Into the Cloud

Published By: Gigaom     Published Date: Sep 16, 2019
We’ve heard it before. A data warehouse is a place for formally-structured, highly-curated data, accommodating recurring business analyses, whereas data lakes are places for “raw” data, serving analytic workloads, experimental in nature. Since both conventional and experimental analysis is important in this data-driven era, we’re left with separate repositories, siloed data, and bifurcated skill sets. Or are we? In fact, less structured data can go into your warehouse, and since today’s data warehouses can leverage the same distributed file systems and cloud storage layers that host data lakes, the warehouse/lake distinction’s very premise is rapidly diminishing. In reality, business drivers and business outcomes demand that we abandon the false dichotomy and unify our data, our governance, our analysis, and our technology teams. Want to get this right? Then join us for a free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest, Dav
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Gigaom

Transforming from Passive Data Catalogs to Active Data Hubs

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

The Smarter Way to Manage Data

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

Adapting to the Cloud for New Data and Analytic Demands

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

Adapting to the Cloud for New Data and Analytic Demands

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

Adapting to the Cloud for New Data and Analytic Demands

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

For the Modernize Your Legacy ETL Technology and Accelerate Data Warehousing

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

Data Warehousing in the Cloud - Opportunities, Benefits and Best Practices

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

Big Data Multi-Cloud eBook

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

Data Warehousing in the Cloud - Opportunities, Benefits and Best Practices

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

Enterprise Data Warehouse Platform Modernization

Published By: Oracle     Published Date: Sep 21, 2018
Agility and speed are required in the cloud economy. Modernize data warehouses with built-in adaptive machine learning to eliminate manual labor for administrative tasks. With Oracle, businesses can now build data warehouses or data marts in minutes.
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Oracle

eBook: Modern Data Warehousing on AWS

Published By: AWS     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. 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 q
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AWS

Information and Data Management Solutions Generate Value

Published By: AWS - ROI DNA     Published Date: Jun 12, 2018
Traditional databases and data warehouses are evolving to capture new data types and spread their capabilities in a hybrid cloud architecture, allowing business users to get the same results regardless of where the data resides. The details of the underlying infrastructure become invisible. Self-managing data lakes automate the provisioning, reliability, performance and cost, enabling data access and experimentation.
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AWS - ROI DNA

Por que os clientes estão migrando seus data warehouses para a nuvem?

Published By: Oracle     Published Date: Apr 16, 2018
A velocidade e o volume de entrada de dados estão gerando demandas esmagadoras sobre os data marts tradicionais, os data warehouses e os sistemas analíticos. Uma solução em nuvem de data warehouse tradicional pode ajudar os clientes a suprirem tais demandas? Muitos clientes estão comprovando o valor dos data warehouses na nuvem através dos ambientes de testes ou de inovação, dos data marts na área de negócios e backup de banco de dados.
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clientes, estao, migrando, data, warehouses, nuvem
    
Oracle

¿Por qué los clientes trasladan sus data warehouses a la nube?

Published By: Oracle     Published Date: Apr 16, 2018
La velocidad y el volumen de los datos entrantes están dando lugar a una gran demanda en los centros de datos tradicionales, repositorios de datos empresariales y sistemas analíticos. ¿Puede una solución de almacén de datos tradicional en la nube ayudar a los clientes a satisfacer estas demandas? Muchos clientes están comprobando el valor de los repositorios de datos en la nube a través de entornos “de prueba”, repositorios de datos según el área de negocios y respaldos de base de datos.
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clientes, trasladan, sus, data, warehouses
    
Oracle

Successful Data Warehouse Approaches to Meet Today's Analytics Demands

Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18

Oracle Autonomous Data Warehouse Cloud

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

Successful Data Warehouse Approaches to Meet Today's Analytics Demands

Published By: IBM     Published Date: Nov 08, 2017
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, ibm, deployment models
    
IBM

Real-Time Analytics

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

Optimizing Customer Engagement Through Connected Data

Published By: RedPoint Global     Published Date: May 11, 2017
While they’re intensifying, business-data challenges aren’t new. Companies have tried several strategies in their attempt to harness the power of data in ways that are feasible and effective. The best data analyses and game-changing insights will never happen without the right data in the right place at the right time. That’s why data preparation is a non-negotiable must for any successful customer-engagement initiative. The fact is, you can’t simply load data from multiple sources and expect it to make sense. This white paper examines the shortcomings of traditional approaches such as data warehouses/data lakes and explores the power of connected data.
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customer engagement, marketing data, marketing data analytics, customer data platform
    
RedPoint Global

IDC Research Spotlight: Data Warehouses Ascend to the Cloud

Published By: Teradata     Published Date: May 02, 2017
Should the data warehouse be deployed on the cloud? Read this IDC Research Spotlight to learn more.
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data warehouse, data storage, data management, data analytics, data preparation, data integration, system integration
    
Teradata

Scaling Data Integration to Handle Big Data

Published By: IBM     Published Date: Mar 30, 2017
In today’s competitive on-line world, the speed of change in customer behaviour is increasing. In addition, in industries such as retail banking, car insurance and to some extent retail, the Internet has become the dominant way in which customers interact with an organisation. Yet in many data warehouses today, being able to analyse customer on-line behaviour is often not possible because the clickstream web log data needed to do this is missing. It is a key point because customer access to the web has made loyalty cheap.
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cyber attacks, data protection, it security, security solutions, system protector, web security, analytics
    
IBM

Adapting to the Cloud for New Data and Analytic Demands

Published By: IBM     Published Date: Mar 29, 2017
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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cloud, analytics, data, organization, ibm
    
IBM

Snowflake Elastic Data Warehouse

Published By: SnowFlake     Published Date: Jul 08, 2016
Data today comes from diverse sources in diverse forms and needs to be analyzed by ever more users as quickly as possible. Those demands are stressing the limitations of traditional data warehouses and data platforms. Snowflake has reinvented the data warehouse, making it possible to bring all your business data together in a single system that can support all your users and workloads. Built from the cloud up as a software service, Snowflake eliminates the cost, complexity, and inflexibility of existing solutions while allowing you to use the tools and skills you already have.
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snowflake, data, technology, best practices, solutions, cloud support, storage, business intelligence, business technology, cloud computing
    
SnowFlake

Media Buy Agility: Snowflake Computing Empowers Accordant Media’s Optimized Media Purchasing

Published By: SnowFlake     Published Date: Jul 08, 2016
This EMA case study profiles the implementation of the Snowflake Elastic Data Warehouse, a new generation of cloud-based data warehouses, by Accordant Media. This document details significant tangible and intangible improvements and opportunities the Snowflake solution created for the Accordant Media infrastructure and analytical teams.
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snowflake, media, data, technology, cloud-based data, best practices, business intelligence, productivity, business technology
    
SnowFlake
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