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…

Re-thinking Data Pipeline Patterns

A Data pipeline is a sequence of transformations that converts raw data into actionable insights. In the past, the processing and storage engines were coupled together e.g., a traditional MPP warehouse combines both a processing and storage engine. With decoupled…

True North of a Modern Data Platform: Any Data, Any Analytics, Any User

At Intuit, we are building a world-class data platform for analytics and ML that is 100% cloud-based — a platform that supports a wide-range of analytics use-cases for our business operations (marketing…

Blurring of lines: Is it a Data Warehouse? Is it a Big Data Platform?

Traditionally, data warehouses…

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…

Understanding Big Data workloads from the Storage perspective

Managing Data Issues as Incidents

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

De-coupling of Compute & Storage in Modern Databases

Databases started as integrated storage-compute solutions providing both a durable persistence layer (storage) as well as a query processing engine (compute). Given the growing variety of data types, changing analytical processing needs…

Under-the-hood of a scale-out Storage Engine

Imagine buying a car — you won`t just look at the color and interiors to make a buy decision (assuming of course that this is not an impulsive buy). You will typically have a few different car models and then compare the price/performance i.e., engine…