TDWI

As organizations look to become more competitive, they are modernizing their data environments to support new data types and new analytics methods. The result is a complex data ecosystem that often includes both on-premises and cloud platforms. In a recent TDWI survey, while the majority of respondents agreed that the cloud was an important part of their data management strategy, the majority also acknowledged that they will utilize a multiplatform environment.

The logical data fabric powered by data virtualization is gaining momentum in enterprise architectures by virtue of its logical and real-time data integration and management capabilities. A well build data fabric platform supports data analytics, cataloging, self-service, governance, etc. and, at the same time offers strategies for optimizing cross-platform performance (such as dynamic query optimization, caching capabilities, summary tables, or in-memory computing), even across multiple cloud providers.This Checklist Report examines the six most popular and frequently used solution...

Chris Day, Director, APAC Sales Engineering for Denodo, gives a product demonstration of the Denodo Platform to show some of the capabilities that data virtualization can deliver when talking about logical data fabric.Watch full webinar here.

Join us for a webinar based on TDWI’s recent Best Practice Report, Unified Platforms for Modern Analytics, where we will discuss the role of the logical data fabric in a unified platform for modern analytics, focusing on several of the key findings outlined in this report.

In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value....

Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.Watch Now On-Demand!

Data scientists have three requirements for efficiently performing data science and advanced analytics: (1) access to wide-ranging enterprise data, (2) flexible modeling, and (3) easy data preparation. It is easier if all the data is normalized and stored in a single repository, but, in reality, data is stored across multiple systems and applications on premises or in the cloud, in diverse formats—structured, unstructured, and semistructured, and in different latencies—at rest or in motion.Logical data fabric powered by data virtualization promises to provide access to all enterprise data in...

Are you modernizing your legacy on-premises data warehouse to its cloud equivalent? Stop! Have you answered these questions: Which cloud data warehouse? Does it fit my performance, scale, and cost requirements? How long will it take? How will my business users access the data during the transition?Modernizing a legacy data warehouse that has been in place for the last 20-30 years by migrating to the cloud is not an easy job. Learn the right way to do it and do it correctly the first time. Don’t experiment and waste time, effort, and money!Join David Loshin, president of Knowledge Integrity,...

The demand for analytics continues to grow. More organizations want to advance beyond dashboards to self-service analytics and more sophisticated algorithms such as machine learning.To support this trend, enterprises are moving towards a unified environment for data and analytics. This platform is often cloud-based to take advantage of scale and flexibility and support massive amounts of data and compute-intensive workloads. It is automated and augmented to help address data and analytics complexity.This TDWI Best Practices Report examines the adoption, use, challenges, architectures, and...

The adoption of cloud data warehousing is no longer seen as teetering on the bleeding edge of technology; a growing number of organizations are already using a cloud data warehouse or cloud data lake to support enterprise business intelligence, reporting, and dashboards. Businesses that migrate to the cloud still face challenges that complicate their ability to leverage cloud-based resources. Although conventional wisdom promotes the cost-effectiveness of the cloud, ungoverned operations allow data storage, egress, and computing resource costs to spiral out of control. Cost management is a...