A More Efficient Approach to Analytics with Cohesity

Abhijit Chakankar from Cohesity discusses how their platform addresses the challenges enterprises face with analytics, particularly concerning secondary data. He outlines the inefficiencies associated with copying data to separate analytics stacks and how Cohesity’s architecture offers a streamlined, integrated solution for in-place analytics.

Chakankar delves into the architecture of Cohesity’s solution, highlighting how production systems can write directly onto Cohesity, where snapshots are taken and potentially cloned for test and development environments. This setup consolidates data into a single, uniform view that simplifies and enhances analytic operations. With built-in analytics features, quality of service (QoS) capabilities, and ample compute resources, Cohesity supports powerful in-place analytics without the need for data redundancies or additional infrastructure.

He further explains key components of Cohesity’s analytics capabilities, including built-in analytics for utilization and capacity, a real-time indexing engine, and the Analytics Workbench (AWB), which uses a MapReduce-based framework. AWB allows for deep, customizable analysis by accepting user-defined code, enabling extensive use cases such as e-discovery, threat analysis, and data anonymization. Chakankar provides a detailed demonstration of creating an AWB app, showcasing its ability to scan for specific patterns, such as social security numbers, and underscores the platform’s flexibility and efficiency in managing and analyzing data directly within the Cohesity ecosystem.