Data Science

Understanding Big Data workloads from the Storage perspective

What is the role of DBAs in Managed Database Platforms?

Consider the scenario of using a database for your Enterprise application. In the on-prem world, this starts by acquiring the right hardware, deploying the database software, configuring for high availability, backups, and the whole…

The curse of Schema-on-read in Data Lakes

In the past, data warehouses were schema-on-write requiring DBAs and Data Architects to coordinate and interpret the data attributes before persisting within the warehouse. In contrast, Data Lakes today are schema-on-read making it easy to add any data…

Scorecard of a Data Platform for Analytics & ML

Enterprises in every vertical are developing Data Platforms to enable Analytics and ML-based insights. The charter of most Data…

Managing Data Issues as Incidents

Correctness of data pipelines is critical for in-product customer experiences (such as ML-based…

Wrong Data Talent — 8 traits of DQ (Data IQ)

The data landscape is rapidly evolving. The skills and technical expertise required today for data roles such as data engineers, data analysts, data scientists, ML engineers, will be significantly different in 5 years.