This session is repeated. This introductory workshop caters to data engineers seeking hands-on experience and data architects looking to deepen their knowledge. The workshop is structured to provide a solid understanding of the following data engineering and streaming concepts: Introduction to Lakeflow and the Data Intelligence Platform Getting started with Lakeflow Declarative Pipelines for declarative data pipelines in SQL using Streaming Tables and Materialized Views Mastering Databricks Workflows with advanced control flow and triggers Understanding serverless compute Data governance and lineage with Unity Catalog Generative AI for Data Engineers: Genie and Databricks Assistant We believe you can only become an expert if you work on real problems and gain hands-on experience. Therefore, we will equip you with your own lab environment in this workshop and guide you through practical exercises like using GitHub, ingesting data from various sources, creating batch and streaming data pipelines, and more.
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Speaker
Frank Munz
5
talks
Frank Munz is a Principal TMM at Databricks, focusing on data engineering and AI tooling. He works at the intersection of engineering, product, and marketing. He previously established Technical Evangelism for AWS in Central Europe and holds a Ph.D. in Computer Science from the Technical University of Munich. He authored a book on Cloud Computing and Java Enterprise and was named Technologist of the Year for his work in Cloud Computing.
Bio from: DATA MINER Big Data Europe Conference 2020
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This introductory workshop caters to data engineers seeking hands-on experience and data architects looking to deepen their knowledge. The workshop is structured to provide a solid understanding of the following data engineering and streaming concepts: Introduction to Lakeflow and the Data Intelligence Platform Getting started with Lakeflow Declarative Pipelines for declarative data pipelines in SQL using Streaming Tables and Materialized Views Mastering Databricks Workflows with advanced control flow and triggers Understanding serverless compute Data governance and lineage with Unity Catalog Generative AI for Data Engineers: Genie and Databricks Assistant We believe you can only become an expert if you work on real problems and gain hands-on experience. Therefore, we will equip you with your own lab environment in this workshop and guide you through practical exercises like using GitHub, ingesting data from various sources, creating batch and streaming data pipelines, and more.
🌟 Session Overview 🌟
Session Name: Supernovas, Black Holes, and Streaming Data: A Journey in Space with Apache Kafka data streams from NASA Speaker: Frank Munz Session Description: In this fun, hands-on, and in-depth How-To, we explore NASA's GCN project, which publishes various events in space as Kafka topics.
The focus of my talk is on end-to-end data engineering, from consuming the data and ELT-ing the stream, to using generative AI tools for analytics.
We will analyze GCN data in real time, specifically targeting the data stream from exploding supernovas. This data triggers dozens of terrestrial telescopes to potentially reposition and point toward the event.
The speaker will kick off the session by contrasting various ways of ingesting and transforming the data, discussing their trade-offs: Should you use a declarative data pipeline, or can a data analyst manage with SQL only? Alternatively, when would it be better to follow the classic approach of orchestrating Spark notebooks to get the data ingested?
He will answer the question: Does a data engineer working with streaming data benefit from generative AI-based tools and assistants today? Is it worth it, or is it just hype?
The demo is easy to replicate at home, and Frank will share the notebooks in a GitHub repository so you can analyze real NASA data yourself!
This session is ideal for data engineers, data architects who enjoy some coding, generative AI enthusiasts, or anyone fascinated by technology and the sparkling stars in the night sky.
While the focus is clearly on tech, the demo will run on the open-source and open-standards-based Databricks Intelligence Platform (so inevitably, you'll get a high-level overview here too).
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Big Data Europe Onsite and online on 22-25 November in 2022 Learn more about the conference: https://bit.ly/3BlUk9q
Join our next Big Data Europe conference on 22-25 November in 2022 where you will be able to learn from global experts giving technical talks and hand-on workshops in the fields of Big Data, High Load, Data Science, Machine Learning and AI. This time, the conference will be held in a hybrid setting allowing you to attend workshops and listen to expert talks on-site or online.
As we venture into the future of data engineering, streaming and serverless technologies take center stage. In this fun, hands-on, in-depth and interactive session you can learn about the essence of future data engineering today.
We will tackle the challenge of processing streaming events continuously created by hundreds of sensors in the conference room from a serverless web app (bring your phone and be a part of the demo). The focus is on the system architecture, the involved products and the solution they provide. Which Databricks product, capability and settings will be most useful for our scenario? What does streaming really mean and why does it make our life easier? What are the exact benefits of serverless and how "serverless" is a particular solution?
Leveraging the power of the Databricks Lakehouse Platform, I will demonstrate how to create a streaming data pipeline with Delta Live Tables ingesting data from AWS Kinesis. Further, I’ll utilize advanced Databricks workflows triggers for efficient orchestration and real-time alerts feeding into a real-time dashboard. And since I don’t want you to leave with empty hands - I will use Delta Sharing to share the results of the demo we built with every participant in the room. Join me in this hands-on exploration of cutting-edge data engineering techniques and witness the future in action.
Talk by: Frank Munz
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