At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.

• Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.
• Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.
• Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.
• Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.
• Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.

Igor Kvachenok Master’s student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.

Connect: https://www.linkedin.com/in/igor-kvachenok/

Selim Nowicki Founder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today’s ML tooling and dbt’s impact on analytics.

Connect: https://www.linkedin.com/in/selim-nowicki/

Gülsah Durmaz Architect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.

Connect: https://www.linkedin.com/in/gulsah-durmaz/

Yashasvi (Yashi) Misra Data Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.

Connect: https://www.linkedin.com/in/misrayashasvi/

Mehdi Ouazza Developer Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.

Connect: https://www.linkedin.com/in/mehd-io/

https://www.bigdataldn.com/en-gb/conference/session-details.4500.251781.the-high-performance-data-and-ai-debate.html

Most Python developers reach for Pandas or Polars when working with tabular data—but DuckDB offers a powerful alternative that’s more than just another DataFrame library. In this tutorial, you’ll learn how to use DuckDB as an in-process analytical database: building data pipelines, caching datasets, and running complex queries with SQL—all without leaving Python. We’ll cover common use cases like ETL, lightweight data orchestration, and interactive analytics workflows. You’ll leave with a solid mental model for using DuckDB effectively as the “SQLite for analytics.”

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!

In this episode, we're thrilled to have special guest Mehdi Ouazza  diving into a plethora of hot tech topics: Mehdi Ouazza's Insights into his career, online community and working with DuckDB and MotherDuck.Demystifying DevRel: Definitions and distinctions in the realm of tech influence (dive deeper here).Terraform's Licensing Shift: Reactions to HashiCorp's recent changes and its new IBM collaboration, more details here.Github Copilot Workspace: Exploring the latest in AI-powered coding assistance, comparing with devin.ai and CodySnowflake's Arctic LLM: Discussing the latest enterprise AI capabilities and their real-world applications. Read more about Arctic - what it excels at, and how its performance was measuredMore legal kerfuffle in the GenAI realm: The ongoing legal debates around AI's use in creative industries, highlighted by a dispute over Drake’s use of late rapper Tupac’s AI-generated voice in diss track & the licensing deal between Financial Times and OpenAIFuture of Data Engineering: Examining the integration of LLMs into data engineering tools. Insights on prompt-based feature engineering and Databricks' English SDKAI in Music Creation: A little bonus with an AI generated song about Murilo, created with Suno

As Joe Reis recently opined, if you want to know what’s next in data engineering, just look at the software engineer. The MDS-in-a-box pattern has been a game changer for applying software engineering principles to local data development– improving the ability to share data, collaborate on modeling work and data analysis the same way we build and share open source tooling.

This panel brings together experts in data engineering, data analytics and software engineering to explore the current state of the pattern, pieces that remain missing today and how emerging tools and data engineering testing capabilities can refine the transition from local development to production workflows.

Speakers: Matt Housley, CTO, Halfpipe Systems; Mehdi Ouazza, Developer Advocate, MotherDuck; Sung Won Chung, Solutions Engineer, Datafold; Louise de Leyritz, Host, The Data Couch podcast

Register for Coalesce at https://coalesce.getdbt.com

We talked about:

Mehdi’s background The difference between startup, scale-up and enterprise Hypergrowth Data platform engineers in a scale-up environment What a data platform is and who builds it Managing the fast pace of a scale-up while ensuring personal growth Should a senior data person consider a scale-up or an enterprise? Should a junior data person consider a scale-up or an enterprise? Sourcing talent for hyper-growth companies and developing a community culture Generating content and getting feedback Generalization vs specialization for data engineers in a scale-up The ratio of work between platform building and use case pipelines Being proactive in order to progress to mid or senior level Caps and bass guitars MehdiO DataTV and DataCreators.Club (Mehdi’s YouTube Channel and podcast)

Links:

Mehdi's YouTube channel: https://www.youtube.com/channel/UCiZxJB0xWfPBE2omVZeWPpQ Mehdi's Linkedin:  https://linkedin.com/in/mehd-io/ Mehdi's Medium Blog: https://medium.com/@mehdio Mehdi's data creators club: https://datacreators.club/

ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html