Myth Busters Webinar Series

In the past, data governance was looked upon as a thankless task, more of a hindrance to business than a facilitator. However, because of today's data privacy regulations, data governance is increasingly seen as a critical function across most organizations. Companies are investing in governance tools, data quality tools, data catalogs, and so on in an effort to improve their data governance function.

So your company has decided to modernize its systems and migrate to the Cloud. Or, maybe, you're in the middle of this modernization and migration process right now. Taking advantage of the dynamic agility and flexibility of the Cloud offerings is certainly something to look forward to. But how do you ensure that your users can access the data that they need from the various Cloud and on-premise data sources - before, during, and after the migrations?

A data mesh architecture offers a lot of promise to change the way we manage data – and for the better. But there’s a lot of confusion about a data mesh. People will tell you that you can build a data mesh on top of a data lake or on top of a data warehouse, and that you don’t need data virtualization to build a data mesh.

Your business stakeholders need more data, faster data, more relevant, and more timely data. They are pushing for a data and analytics self-service model to speed up getting key insights. The CEO has made it clear that this is critical to the continued growth and health of the business...this is your top priority.

So your data architecture keeps getting more and more complex - new data sources, new data types and formats, and ever-increasing demands from the users for more, better, and faster data. You're struggling to keep up and your users are getting more and more vocal about the amount of time it takes to get their data. But you have faith in your ETL tools. They've solved the problem in the past and you're sure that they can do the same now…or can they?

ETL tools - or even the ELT variants - have their use and purpose. We're not going to argue that.

When you hear data virtualization, do you think BI and analytics? If so, you’re misinformed and missing out on a whole set of possibilities and capabilities of this technology. It is probably also why you think data virtualization is of no use to you if you need to access your data through APIs.

This is why we’re back with another episode of Myth Busters!

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.

Reporting tools, while important and necessary, focus on the visualization of data and it’s presentation to the business user. Data virtualization is a governed data access layer designed to connect to and provide transparency of all enterprise data. Yet the myth suggests that these technologies are interchangeable. So we’re going to take it on!

Watch this webinar as we compare and contrast BI tools and data virtualization to draw a final conclusion.

In the first webinar of the series, we bust the 'performance' myth.

“What about performance?” is usually the first question that we get when talking to people about data virtualization. After all, the data virtualization layer sits between you and your data, so how does this affect the performance of your queries? Sometimes the myth is perpetuated by people with alternative solutions… the ‘Put all your data in our Cloud and everything will be fine. Data virtualization? Nah, you don’t need that! It can't handle big queries anyway,’ type of thing.

Watch this webinar to look at the basis of the 'performance' myth and examine whether there is any underlying truth to it.