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Share your passion for data!

Build your confidence, strenghten your network business connections and exchange best practice knowledge in the community by being a public speaker in your field!

 

We are looking for guest speakers at #DataEngineersLunch and #CassandraLunch!

 

If you are experienced in #ETL, #datawrangling, etc, and are interested in joining our events, please fill out this form and we will reach out to you ASAP!

Data Engineer’s Lunch

Speak at Data Engineer’s Lunch happening every Monday!

Apache Cassandra Lunch

Speak at Apache Cassandra Lunch happening every Thursday!

Become a guest speaker at Data Engineer’s Lunch!

Become a
guest speaker at
Data Engineer’s Lunch!

If you want to submit to speak at Cassandra Lunch, please check the following requirements:
Step 1: Please check the following requirements:
The session must be in English.
Session length will be 60 minutes.
Sessions will be online (via Zoom).
Step 2: Please fill out the form below:

Become a guest speaker at Cassandra Lunch!

Become a
guest speaker at
Cassandra Lunch!!

If you want to submit to speak at Cassandra Lunch, please check the following requirements:
Step 1: Please check the following requirements:
The session must be in English.
Session length will be 60 minutes.
Sessions will be online (via Zoom).
Step 2: Please fill out the form below:
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Data Engineer’s Lunch #86: Building Real-Time Applications at Scale

January 30, 2023 @ 12:00 pm - 1:00 pm

In this talk, we will explore common problems faced when building real-time applications at scale, with a focus on a specific use case.

As the demand for real-time data processing continues to grow, so too do the challenges associated with building production-ready applications that can handle large volumes of data and handle it quickly. In this talk, we will explore common problems faced when building real-time applications at scale, with a focus on a specific use case: detecting and responding to cyclist crashes. Using telemetry data collected from a fitness app, we’ll demonstrate how we used a combination of Apache Kafka and Python-based microservices running on Kubernetes to build a pipeline for processing and analyzing this data in real-time. We’ll also discuss how we used machine learning techniques to build a model for detecting collisions and how we implemented notifications to alert family members of a crash. Our ultimate goal is to help you navigate the challenges that come with building data-intensive, real-time applications that use ML models. By showcasing a real-world example, we aim to provide practical solutions and insights that you can apply to your own projects.

Key Takeaways:

  • An understanding of the common challenges faced when building real-time applications at scale
  • Strategies for using Apache Kafka and Python-based microservices to process and analyze data in real-time
  • Tips for implementing machine learning models in a real-time application
  • Best practices for responding to and handling critical events in a real-time application

Organizer

Anant