AI for enterprises, particularly in the era of GenAI, requires rapid experimentation and the ability to productionize models and agents quickly and at scale. Compliance, resilience and commercial flexibility drive the need to serve models across regions. As cloud providers struggle with rising demand for GPUs in environments, VM shortages have become commonplace, and add to the pressure of general cloud outages. Enterprises that can quickly leverage GPU capacity in other cloud regions will be better equipped to capitalize on the promise of AI, while staying flexible to serve distinct user bases and complying with regulations. In this presentation we will show and discuss how to implement AI deployments across cloud regions, deploying a model across regions and using a load balancer to determine where to best route a user request.

Did you finish the Photon whitepaper and think, wait, what? I know I did; it’s my job to understand it, explain it, and then use it. If your role involves using Apache Spark™ on Databricks, then you need to know about Photon and where to use it. Join me, chief dummy, nay "supreme" dummy, as I break down this whitepaper into easy to understand explanations that don’t require a computer science degree. Together we will unravel mysteries such as:

• Why is a Java Virtual Machine the current bottleneck for Spark enhancements?
• What does vectorized even mean? And how was it done before?
• Why is the relationship status between Spark and Photon "complicated?"

In this session, we’ll start with the basics of Apache Spark, the details we pretend to know, and where those performance cracks are starting to show through. Only then will we start to look at Photon, how it’s different, where the clever design choices are and how you can make the most of this in your own workloads. I’ve spent over 50 hours going over the paper in excruciating detail; every reference, and in some instances, the references of the references so that you don’t have to.

Talk by: Holly Smith

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Elixir is an Erlang-VM bytecode-compatible programming language that is growing in popularity.

In this session I will show how you can apply Elixir towards solving data engineering problems in novel ways.

Examples include: • How to leverage Erlang's lightweight distributed process coordination to run clusters of workers across docker containers and perform data ingestion. • A framework that hooks Elixir functions as steps into Airflow graphs. • How to consume and process Kafka events directly within Elixir microservices.

For each of the above I'll show real system examples and walk through the key elements step by step. No prior familiarity with Erlang or Elixir will be required.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/