data lakes

Presented at Chief Data Officer July 2021 (A/NZ)The current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL, and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML, and AI. Data Virtualization is a modern data integration technique in which a single semantic layer can be built containing semantic definitions of all of the underlying sources.Watch on-demand this session to understand how data virtualization:Helps organisations...

For years, TDWI research has tracked the modernization and evolution of data warehouse architectures, as well as the emergence of the data lake design pattern for organizing massive volumes of analytics data. The two have recently converged to form a new and richer data architecture. The architecture is fairly new, and not many organizations have embraced it yet, although the majority of respondents to our recent TDWI Best Practices Report survey see it as an opportunity because it provides more options for managing an increasingly diverse range of data structures, end user types, and...

The current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL, and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML, and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time-consuming. A logical data warehouse is a modern data architecture that allows organizations to leverage all of their data irrespective of where the data is stored, what format it is stored in...

Presented at Data Architecture Online A/NZThe current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time consuming.A logical data warehouse is a modern data architecture that allows organisations to leverage all of their data irrespective of where the data...

Presented at DAMA VancouverFor more than three decades, business users have demanded a unified view of organizational data. Technologists have addressed this need, over time, first with databases, then with data warehouses, and more recently with data lakes. However, these technologies have become data silos housing different types of data. That’s where technologies like data virtualization played a critical role to provide a unified view of the enterprise information, without replicating data and delivering faster speed-to-market for products and services. As businesses go increasingly...

Attend this session to hear some of the most innovative use cases of data virtualization. Includes three customer case study presentations – get a chance to vote for most innovative use case to select the winner for this innovative award category.

Denodo customer presentation by Wang Cheung, Director of Data Platform Architecture, Guardian Life Insurance.

Alex Rosenthal, Head of EDO and Big Data Technologies, Guardian Life presents how to create a data abstraction layer so that business users are insulated from underlying system changes.

Arun Murugan, Sr. Director, Data Architecture, GE Capital explains the benefits of aggregating the data without having to move them into a centralized repository.

Physical data lake solutions are constrained by factors such as duplication of data, governance limitations, and the risk of becoming another data silo. Data virtualization allows you to leave all of the source data in place, stored across myriad, heterogeneous systems, and establish a virtual, “logical data lake” for accessing all of the data.Download this solution brief to learn the benefits of a Logical Data Lake.