Speaker

Shubha Vijayasarathy

Shubha Vijayasarathy

Program Manager (Azure Messaging, Event Hubs), Microsoft

12+ years of experience in Web development and cloud. Expertise on Messaging, Streaming, Big Data, PaaS Messaging services, Kafka and more

Unlock real-time insights with serverless streaming on Azure from Event Hub Kafka

In today’s business environment, given the rapidly increasing volume of data, diverse data types, and the need to respond to events in real-time, traditional business analytics platforms that use static data are no longer enough to help you keep up with dynamic demands. Streaming solutions on Azure make it easy to ingest, process, and analyze streaming data from any source, enabling powerful insights to drive real-time action. Allow existing Apache Kafka clients and applications to talk to Event Hubs without any code changes and build an end-to-end streaming pipeline in minutes, with seamless integration with Azure Stream Analytics. No need to provision VMs or clusters.

Join us in this session where we’ll walk through a real-time anomaly detection scenario, utilizing Azure Event Hubs for Apache Kafka and the built-in ML capabilities of Azure Stream Analytics.

Serverless Streaming at Scale Deep Dive

One day a customer comes to you with a very simple requirement: ingest, process and store at least 10,000 msg/sec using only PaaS solutions. Is this possible? How? What is the correct architecture to sustain such volume (or go ever higher than that) and what are the implementation best practices to make sure everything is balanced and you're not just throwing money at the problem? In this session we'll show how to architect, build and deploy such solution. We will start from 10,000 msg/sec…and we'll see together how high we can go (and we’ll go pretty high!), so that you can be sure that even the most demanding workload can be handled, gaining a good understanding of how Azure work behind the scenes for us.

Stream Processing at Scale on Azure

Stream processing is becoming more and more important in many scenarios. It can be found in Microservices Architectures, Near Real Time Operational Analytics, IoT and Smart Building solutions and Real Time Data Processing. Azure offers a lot of options to implement a Streaming At Scale solution, and a good knowledge of the pros and cons of the various technologies involved is vital to architect and implement the correct Lambda or Kappa architecture for your solution.
In this session we'll go through the most common way to implement a Streaming at Scale solution, sharing what we have learned from many engagements with the most diverse customers throughout the world.

Kafka on Azure - A canonical Customer Scenario Deepdive

Kafka is the one of the most used OSS with large enterprises and a defacto messaging bus. We have worked with number customers and seen a common pattern how customers use Kafka on Azure to solve their business problems. This session will walk through Kafka use case with Lambda Architecture for Down streaming processing at scale

Shubha Vijayasarathy

Program Manager (Azure Messaging, Event Hubs), Microsoft