Lena Hall

In this session, Lena Hall will show you how to extract value from your data to bring the impact of your low-code solutions to a new level.

Over the last couple of months, we were fortunate to receive a number of outstanding community stories as a part of [Applied Cloud Stories](https://aka.ms/applied-cloud-stories) initiative. Many of them shared lessons learned, trade-offs, tips and tricks, and valuable experience. We are grateful for every single story we received from you!

One of the challenges of large scale data analysis is being able to get the value from data with least effort. Doing that often involves multiple stages: provisioning infrastructure, accessing or moving data, transforming or filtering data, analyzing and learning from data, automating the data pipelines, connecting with other services that provide input or consume the output data, and more. There are quite a few tools available to solve these questions, but it's usually difficult to have them all in one place and easily connected.

If you are already working with Apache Kafka, it can be easy to simplify management of your event infrastructure? You can keep using your existing Apache Kafka applications unchanged, and rely on Azure Event Hubs as a backend for your event-ingestion by just swapping the connection information. This allows us to keep using Apache Kafka connectors and libraries to hundreds of projects and delegate the complexity to Azure Event Hubs behind the scenes to help you focus on code instead of maintaining infrastructure.

Applied Cloud Stories is a call for new content created by independent community members, focusing on practical stories about scenarios and workloads that can run on Azure. Applied Cloud Stories is now accepting submissions - send your story before March 15, 2020!

It's about the impact of our work, the complexity and obstacles we face, and what is important for building better distributed systems, especially when other life-critical areas rely on and build on what we create.

Often we hear about machine learning and deep learning as a topic that only researchers, mathematicians, or PhDs can be smart enough grasp. It is possible to explain seemingly complex fundamental concepts and algorithms of machine learning without using cryptic terminology or confusing notation.