Data Virtualization
Packed Lunch Webinar Series

Data Virtualization: An Introduction

What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too.

Advanced Analytics and Machine Learning with Data Virtualization

Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?

Building a Logical Data Lake with Data Virtualization

Historically data lakes have been created as centralized physical data storage platform for data scientists to analyze data. But lately the explosion of big data, data privacy rules, departmental restrictions among many other things have made the centralized data repository approach less feasible. Using Data Virtualization allows organizations to create a logical - or virtual data lake - without having to physically copy and centralize all of their data.

Attend this session to learn:

Big Insights from Your Big Data using Data Virtualization

Data lakes have grown to be a popular architecture that enables modern analytics and data science. However, complete replication of all corporate data into giant data lakes is unfeasible. Data volumes are too high, and replication to multiple systems creates brittle point-to-point connections. Out-of-synch data and uncontrolled replication leads to “data swamp” scenarios. On top of the physical data lake, a logical approach is more feasible: a logical layer that connects different systems (the data lake among them) and exposes them as one.

Minimizing the Complexities of Machine Learning with Data Virtualization

Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format.

Enabling Self-Service Analytics with Logical Data Warehouse

What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses are not easy to deal with for agile data access.

The Role of Data Virtualization in an API Economy

How data virtualization works as a service container, as a source for microservices and as an API gateway.

Virtual Sandbox for Data Scientists at Enterprise Scale

The Virtual Sandbox is an overarching framework to support the enterprise-scale roll out of data science programs using the industry standard, CRISP-DM methodology.