On-demand Virtual Sessions

In recent years, the world of transportation has been going through a smart mobility revolution. Nowadays, many people are rethinking the necessity of private cars to get from point A to point B due to alternatives offering lower costs, more flexibility, and less air pollution. 

The data market is not much different. Companies today are interested in bringing their data “from point A to point B” in a fast, simple way, offered at a reasonable cost and adapted to their business needs. 

So, why do we still invest in infrastructure with high costs and large commitments that can’t handle the frequent changes in the world of data?

Get to know the Datorios way to deal with the changing needs of the data world. Discover the plug-and-play solution that doesn’t require big commitments or large investments. Disrupting the transformation landscape, Datorios offers the flexible, easy-to-adopt, non-committal, and affordable toolbox, so you can solve your data problems quickly and easily. 

To follow soon …

Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.

During this session, Ahana Product Manager Rohan Pednekar will discuss how to unlock the value of your data lake with the emerging Open Data Lakehouse architecture and why Presto, the open source SQL query engine, is critical to this approach. Finally, he will show why Ahana Cloud is the easiest way to deploy and run Presto on AWS S3.

Broken data is costly, time-consuming, and nowadays, an all-too-common reality for even the most advanced data teams. In this talk, I’ll introduce this problem, called “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and discuss how to eliminate it in your data ecosystem with end-to-end data observability. Drawing corollaries to application observability in software engineering, data observability is a critical component of the modern DataOps workflow and the key to ensuring data trust at scale. I’ll share why data observability matters when it comes to building a better data quality strategy and highlight tactics you can use to address it today.

In many organizations, data governance or privacy functions and data analytics functions struggle to have productive, nuanced discussions around the use of data. The crux of the issue is that they don’t have quantitative ways to discuss use cases, datasets or privacy risk, so the fear of setting an undesirable precedent limits the level of discussion that occurs. With a practical example, this talk illustrates how privacy-enhancing technologies (PETs) can foster more productive, collaborative conversations across disparate functions so that processes can be streamlined while ensuring all objectives are achieved.