Thibauld Croonenborghs
Data Architect at AE
Brugge, Belgium
Actions
Thibauld Croonenborghs started his studies at the Belgian Royal Military Academy and worked as an army officer for several years while simultaneously studying computer science.
He started his career, but has since transitioned into the data field and is Python, Azure and Spark enthousiast.
He has several years of working on data platforms across several technology stacks, including Microsoft Fabric, Azure, Hadoop and Databricks.
Links
Area of Expertise
Topics
Positioning Microsoft Fabric in enterprise Azure architecture
Microsoft Fabric is becoming an important part of the Microsoft data platform story, but for many Azure architects the positioning is still unclear. Is Fabric replacing Azure Data Factory, Synapse, Data Lake, Databricks or Power BI? Or is it something different entirely?
This session approaches Microsoft Fabric from an Azure architecture perspective. Instead of giving a product tour, we will use a practical mental model: Fabric as a SaaS analytics platform layer in the Azure ecosystem, where OneLake, workspaces, capacities, Lakehouse, Warehouse, Data Factory and Power BI come together to support governed data products.
The session is aimed at Azure architects, cloud architects and solution architects who understand the basics of Azure data services, but want to know how Fabric changes their architecture decisions. We will compare a traditional Azure data platform architecture with a Fabric-centered architecture, and discuss how curated, reusable and governed data products can become the bridge between platform teams, domain teams, BI users and AI workloads.
You will leave with a clearer way to position Fabric in enterprise Azure environments, including decision points around workspace design, capacity strategy, data product ownership, governance, security, DevOps, integration patterns and common anti-patterns. The goal is not to convince you that everything belongs in Fabric, but to help you decide what belongs in Fabric, what belongs in Azure, and how Fabric can help turn raw data platforms into trusted data products.
The curious case of the slow Spark job: A detective’s toolkit
Spark jobs rarely fail loudly; they slow down, stall or behave unpredictably. This often leaves engineers staring at dashboards wondering: what actually went wrong?
In this session, you will learn how to approach Spark performance issues like a detective, using the Spark UI and Spark History Server as your primary investigative tools. Rather than touring every tab, we focus on a practical workflow.
Through real-world examples, we will follow the clues: long-running stages, uneven task distributions, high shuffle wait times, and executor imbalances. You will also see how Fabric’s extended history server can accelerate root cause analysis after a job completes.
By the end of this session, you will have a repeatable approach to diagnosing Spark issues in Fabric, understand which signals matter most, and know exactly where to look first when things go wrong.
If you’ve ever asked “why is this Spark job slow?”, this session will give you the tools to answer it confidently.
Boost your Python testing with testcontainers
Writing tests that mimic production environments can be challenging, especially when dealing with dependencies like databases, message brokers, and external services
We will go through the following topics:
- What are testcontainers?
- Test environment setup
- Writing some test scenarios
- Integration with pytest and unittest
- Demo + some practical examples
Single-node technologies vs. Spark in Microsoft Fabric: Choosing the right tool for the job
In the early days of big data, distributed computing frameworks like Apache Spark and Hadoop became the de facto standards for processing massive datasets. Their ability to handle distributed computing made them essential for tackling large-scale data challenges. However, in today’s diverse data landscape, not all datasets qualify as "big data." For many use cases, single-node processing tools like Polars and DuckDB are proving to be compelling alternatives, offering exceptional performance, simplicity, and lower overhead compared to distributed frameworks.
Microsoft Fabric introduces a unique opportunity to leverage both worlds if necessary. By enabling Python notebooks within its ecosystem, Fabric allows you to build and execute pipelines using these modern single-node technologies. This flexibility ensures you can choose the most efficient tool for your specific workloads.
In this session, we will:
- Examine the evolution of data processing, contrasting distributed frameworks like Spark with single-node solutions.
- Explore how technologies like Polars and DuckDB operate, their strengths, and how they compare to Spark in performance and scalability.
- Evaluate use cases to determine which approach—distributed or single-node—fits best
Demystifying Spark performance optimizations in Microsoft Fabric
Optimizing Spark workloads in Microsoft Fabric can be a complex endeavor, given the array of available configurations and techniques. Terms like V-Order, deletion vectors, liquid clustering or simple considerations of read-heavy versus write-heavy profiles often add to the confusion. In this session, we'll bring clarity to these concepts, offering a structured overview of each optimization strategy and how it impacts performance on the table level of a Delta Lake table
Through practical examples and performance considerations, attendees will gain insights into how to fit these optimizations into their data architecture, whether dealing with read-intensive analytics or write-heavy data ingestion processes. By the end of this talk, you'll be equipped with the knowledge to make informed decisions, turning the complexity of Spark optimizations in Microsoft Fabric into a strategic advantage
From data engineer to AI-orchestrator: developing a data platform with agentic coding
AI-assisted coding is rapidly changing how software is written, but what does that mean for data engineers building modern data platforms?
In this session, we explore how AI agents can accelerate development while maintaining architectural control and code quality. Using a minimal data platform setup as a foundation, we demonstrate how AI can move beyond simple autocomplete and become a structured development assistant.
In this session, you will learn:
• What agentic coding really means in a data engineering context
• The tools and setup required to get started
• How to improve AI agents using structured prompts, skills, and context engineering
• The role of MCP and plugins in enabling tool-aware AI workflows
• Common pitfalls, limitations, and how to avoid over-reliance on AI
The session concludes with a live demo where we enhance a minimal dbt project using an AI agent to add tests, refactor models, and extend functionality in real time.
Attendees will gain practical insight into how AI can increase productivity in data platform development while understanding where human expertise remains essential.
European Microsoft Fabric + SQL Community Conference 2026 - Sessions Sessionize Event
FABCON 2026 & SQLCON 2026 - SESSIONS Sessionize Event
European Microsoft Fabric Community Conference 2025 Sessionize Event
Thibauld Croonenborghs
Data Architect at AE
Brugge, Belgium
Links
Actions
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top