Security

Semantics has been a topic often associated with academia with little impact in the analytics world. However, that tendency is starting to change, fueled by self-service and data democratization initiatives that broaden the scope of who’s accessing data and what those users expect.In this session, Pablo Alvarez, Global Director of Product Management, takes you for a deep dive into the exciting uses of semantics in data management. He will discuss why semantics is important for your data strategy, and how it can radically change your approach to managing things like self service, security, and...

Podcast Guest: Fred Baradari, Partner & Channel Sales Director at Denodo

Security, data privacy, and data protection represent concerns for organizations that must comply with policies and regulations that can vary across regions, data assets, and personas.Data Virtualization offers a single logical point of access, avoiding point-to-point connections from consuming applications to the information sources. As a single point of data access for applications, it is the ideal place to enforce access security restrictions that can be defined in terms of the canonical model with a very fine granularity.Denodo has been successfully deployed in many organizations...

So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data...

So you’re building a data lake to solve your big data challenges. A data lake will allow you to keep all of your raw, detailed data in a single, consolidated repository; therefore, your problem is solved. Or is it? Is it really that easy?Data lakes have their use and purpose, and we’re not here to argue that. However, data lakes on their own are constrained by factors such as duplication of data and therefore higher costs, governance limitations, and the risk of becoming another data silo.With the addition of data virtualization, a physical data lake, can turn into a virtual or logical data...