talk-data.com

Dear Community,

We’re back with another exciting AWS session! This time, we’re diving deep into Iceberg Data Lakes on AWS and AI-driven Incident Resolution in a friendly and engaging atmosphere.

Join us at BER16 Amazon Office for an evening filled with insightful talks, networking, and knowledge sharing. Whether you’re passionate about cloud technologies, AI, or data lakes, or simply looking to connect with fellow AWS enthusiasts, this is the perfect opportunity to learn, exchange ideas, and expand your network.

For further information, check out the details below, and we look forward to having all of you with us!

===================================================

18:30 - Warming up and networking chat 18:45 - Intro by AWS UG Berlin 19:00 - 19:40 - Burak Karakan // Building Iceberg Data Lake on AWS

This presentation explores building scalable data lakes on Amazon S3 using Apache Iceberg, an open table format that enhances data reliability through features like schema evolution and time travel. We'll demonstrate the practical implementation of a production-ready data lake on S3 and show how to leverage AWS Athena for efficient, serverless querying of Iceberg tables. Attendees will learn how to modernize their data infrastructure using these open-source technologies while optimizing for performance and cost.

19:40 - 20:00 - Network break with snacks & drinks

20:00 - 20:40 - Bastian de Groot & Benjamin Hofmann // AI as an Intelligent Assistant for 24/7 Incident Resolution in AWS

Hyground is a multi-agent system that integrates seamlessly with AWS services to simplify IT operations. This talk breaks down the complexity levels of multi-agent architectures and shows how to implement them using AWS Bedrock, Claude, Amazon EKS, and LangGraph. We’ll take a look at best practices and pitfalls when designing these systems.

20:40 - 21:00 - Closing Announcements & Networking

===================================================

Very Important: There is no "waitlist" for our regular sessions. However, there are limited seats available. If you want to make sure you can attend: Register yourself with your "full name" here at meetup.com Arrive on time - seats are first come, first serve.

As soon as there are seats available, you are welcome to join with your registration. In case there are no more seats available, we won't be able to let you join us this time.We thank you very much for your understanding!

===================================================

Additional Information

This event is wheelchair friendly. Help us spread the word, and invite your friends & colleagues! If you're attending with wheelchair and need assistance, please mail us: [email protected] for further details.

Would you like to host AWS UG Berlin events at your company? Register here Would you like to speak at AWS UG Berlin sessions? Submit your talk here

All-in-one Data Ingestion, Transformation, Data Quality & Governance

Bruin is an innovative data infrastructure company that aims to transform the data space by providing an integrated, code-first platform. Founded by Burak Karakan and Sabri Karagönen, Bruin addresses the "last mile" problem in data management, offering a comprehensive solution that combines data ingestion, transformation, quality, and governance.

Bruin brings together various data tools and processes into a single, cohesive platform. This integration allows for seamless data lineage tracking and improved efficiency across the data lifecycle.

Unlike no-code solutions, Bruin emphasizes version-controllable code, enabling companies to own and manipulate their data processes. The platform supports both SQL and Python, providing flexibility for various data tasks.

Summary The challenges of integrating all of the tools in the modern data stack has led to a new generation of tools that focus on a fully integrated workflow. At the same time, there have been many approaches to how much of the workflow is driven by code vs. not. Burak Karakan is of the opinion that a fully integrated workflow that is driven entirely by code offers a beneficial and productive means of generating useful analytical outcomes. In this episode he shares how Bruin builds on those opinions and how you can use it to build your own analytics without having to cobble together a suite of tools with conflicting abstractions.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementImagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!Your host is Tobias Macey and today I'm interviewing Burak Karakan about the benefits of building code-only data systemsInterview IntroductionHow did you get involved in the area of data management?Can you describe what Bruin is and the story behind it?Who is your target audience?There are numerous tools that address the ETL workflow for analytical data. What are the pain points that you are focused on for your target users?How does a code-only approach to data pipelines help in addressing the pain points of analytical workflows?How might it act as a limiting factor for organizational involvement?Can you describe how Bruin is designed?How have the design and scope of Bruin evolved since you first started working on it?You call out the ability to mix SQL and Python for transformation pipelines. What are the components that allow for that functionality?What are some of the ways that the combination of Python and SQL improves ergonomics of transformation workflows?What are the key features of Bruin that help to streamline the efforts of organizations building analytical systems?Can you describe the workflow of someone going from source data to warehouse and dashboard using Bruin and Ingestr?What are the opportunities for contributions to Bruin and Ingestr to expand their capabilities?What are the most interesting, innovative, or unexpected ways that you have seen Bruin and Ingestr used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bruin?When is Bruin the wrong choice?What do you have planned for the future of Bruin?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links BruinFivetranStitchIngestrBruin CLIMeltanoSQLGlotdbtSQLMeshPodcast EpisodeSDFPodcast EpisodeAirflowDagsterSnowparkAtlanEvidenceThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Join us at PyData Turkiye on September 26th for a full day of exploration into the fascinating world of data science and AI. This conference unites data professionals and enthusiasts alike to share knowledge and uncover new possibilities.

PyData Turkiye provides an excellent opportunity to network, gain insights, and discuss challenges and innovations in the field. Whether you're a seasoned data scientist or simply interested in the power of data, this event is tailored just for you. Join us for an inspiring day of learning and connection!

Keynote Speaker:

• Dr. Işıl Berkün - Technical Skills and Career Advancement in AI and Data Science

Sessions:

• Dr. Emir Öztürk - Introduction to Retrieval Augmented Generation (RAG)
• Oğuzhan B. Tekin - Use of HPC systems in the world of data
• Evgenii Ignatev - Python semi-patterns of style and structure
• Burak Karakan - Practical tips & tricks for production ML pipelines
• Redzhep Mehmedov Redzhebov - Converging Simulations and Reality: Advancing Industrial AI
• Peyman Beyranvand - Candidate Recall with Two Tower Approach for Personalized Product
• Emin M. Eraydın - Big Data, Bigger Impact: Optimizing Supply Chain Efficiency
• Yury Aksenov - The ways to cook data fixtures for a pipeline's test.