Sep 23–26, 2019

Building a fast, scalable, efficient operational analytics and reporting application using MemSQL, Docker, Airflow, and Prometheus (sponsored by MemSQL)

Praveen Chitrada (Akamai Technologies)
11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 04/05

Akamai is the edge. Its globally distributed intelligent edge platform surrounds everything, from the enterprise to the cloud, so its customers and their businesses can be fast, smart, and secure using its edge security, web and mobile performance, and over-the-top (OTT) solutions. The company keeps decisions, apps, and experiences closer to users than anyone and attacks and threats far away.

Akamai operates at truly massive scale. Praveen Chitrada walks you through how Akamai built an ultra-fast, highly scalable, and easily supportable platform to calculate network utilization metrics for billing, reporting, and analytics that supports tens of thousands of customers and more than 30% of all internet traffic. Praveen demonstrates how to use MemSQL, Docker, Airflow, Prometheus, and a robot framework to ingest and manage operational data at Akamai scale.

This session is sponsored by MemSQL.

What you'll learn

  • Learn how to use MemSQL, Docker, Airflow, Prometheus, and other technologies to manage data at a large scale
Photo of Praveen Chitrada

Praveen Chitrada

Akamai Technologies

Praveen Chitrada is an engineering manager at Akamai, where he leads a team responsible for designing and building the next generation of applications. Praveen and members of his technology team love using cutting-edge technologies such as Airflow, Docker, Hadoop, MuleSoft, ODI, Prometheus, Spark, and of course MemSQL. Over nearly a decade at Akamai, Praveen has applied and expanded his extensive knowledge in building complex and scalable applications. His contributions include projects to generate usage metrics for billing, track the cost of goods sold, and implement event monitoring and real-time analytics at Akamai scale. Recently, he played a pivotal role in rearchitecting the network usage statistics application to meet demanding service-level agreements (SLAs) while accommodating and leveraging ongoing, exponential growth in data volumes.

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