Accelerating Machine Learning on Databricks

There is a lot of focus on big data, but sometimes it makes sense to look at the small data at hand and explore how to drive value from that. In this webinar, Mike Weston will present how to iteratively work with the data available, from e.g. purchase patterns, web browsing or location data, and gradually grow big insights.

As businesses strive to advance digital transformation efforts, legacy architectures can be a significant obstacle to enabling the agility required to succeed in today's ever-changing data landscape. Many enterprises struggle with scaling the delivery of data and analytics to accommodate the growing array of data domains, users, and use cases. As a result, data mesh and data fabric architectures are on the rise with the aim of abstracting data management complexity, increasing data availability, and fostering greater collaboration.

According to the Office for National Statistics, in April 2020, the number of remote workers increased to 46% of the population. Now with the easing of lockdown measures, many businesses are considering or are already implementing what the Financial Times calls a "split model' a.k.a "hybrid working" that divides the time between the home and the office. If home-office working is to become the norm, then organisations need to consider the data protection challenges they will be faced with.

The economic model and elegance of cloud environments are motivating companies to assess their existing on-premises data warehouses and modernize their enterprise information environments. However, confusion about what is meant by “modernization” has led some to believe that “lifting and shifting” their on-premises implementation to a cloud environment is the default approach. In these cases, the results are mixed—while the system has technically been moved to a modern cloud platform, by no means is it modernized.