Christian Henrik Reich
Sr Solution Architect @ Microsoft
Copenhagen, Denmark
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Sr. Solution Architect at Microsoft. I started programming as a kid and never stopped. I have worked across everything from embedded systems to large‑scale data platforms. My work over the past decade has focused on Data and AI with a strong emphasis on Microsoft Fabric, SQL Server, Databricks, cloud‑scale architectures, modern data engineering, and Machine Learning. I volunteer teaching AI and Machine Learning, and I’m currently writing “The Data Engineer’s Guide to Microsoft Fabric” for O’Reilly.
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Adapting to Fabric Spark: A SQL Server Practitioner’s Path Forward
This workshop is for SQL Server practitioners who are familiar with administering databases, reading query plans, and managing access, and who are looking to step into the world of Fabric. This workshop will cover both Fabric Lakehouses and Spark. And, It will show how your skills are still relevant in:
* Understanding the Spark engine
* Reading query plans and Statistics
* Delta Lake storage engine
* Performance recommendations
The takeaway is that while the approaches may be different, the knowledge is the same.
The Data Engineer’s Guide to Microsoft Fabric: Building Modern Data Lakehouses
This full-day workshop covers the core Microsoft Fabric engines, data engineering patterns, and architectural decisions that matter in real projects. The session is focused on practical implementation, including how the platform fits together, when to use what, and how to design scalable, maintainable solutions. Attendees will gain a clearer understanding of Fabric workloads, integration points, tradeoffs, and the patterns that support effective data engineering delivery.
Empowering Lakehouse Solutions with Fabric Warehouse
Microsoft Fabric Lakehouses get lots of attention, yet the Warehouse’s evolution and true potential in a lakehouse solution are often overlooked.
This session covers:
* Engine deep dive
* Performance
* Warehouse’s role in lakehouse solutions
* Interoperability with Spark and lakehouses
Attendees will see how the Warehouse has evolved and how to apply it effectively in a lakehouse solution.
Fabric Interoperability: Keep the Platform, Get the Fabric Perks
Adopting Fabric doesn’t need to be a migration project or require changing your existing data solutions. You might already have a Snowflake or Databricks data platform that’s working well and in great shape, yet there’s still a desire from the business to use Azure or Fabric services.
In this session, you will learn how Fabric can work with other data platforms without complex integration or ETL pipelines, while still allowing data to flow through Fabric so it’s available across the wider Fabric and Azure ecosystem.
The takeaways from this session are how to set up Snowflake and Databricks to work seamlessly with Fabric so you can carry on and hardly notice Fabric is there.
Adapting to Fabric: A SQL Server Practitioner’s Path Forward
This workshop is for SQL Server practitioners who are familiar with administering databases, reading query plans, and managing access, and who are looking to step into the world of Fabric. This workshop will cover both Fabric Lakehouses and Warehouses in a day. And, It will show how your skills are still relevant in:
* Understanding the engines
* Reading query plans and Statistics
* Delta Lake storage engine
The takeaway is that while the approaches may be different, the knowledge is the same.
An Apache Spark query's journey through the layers of Databricks
A deep-dive session about Spark internals, where we explore how queries are executed in Apache Spark and within the layers of Databricks.
We will cover:
* Spark SQL and Catalyst
* A note on Tungsten
* Delta Lake
* Parquet files
These insights will be supported by glimpses into the official Apache Spark source code on GitHub.
The takeaway should be a better understanding of how queries are executed and some tools for problem-solving and optimizing for speed or cost.
Beyond Chatbots: Leveraging AI for Unstructured Data Processing
Much attention has been drawn to the rise of Generative AI (GenAI) and Large Language Models (LLMs) in general. In most cases, we are presented with yet another company chatbot for utilizing these technologies.
Less attention has been given to the fact that data has also moved into another era. Data is becoming less logically tangible, no longer just stored in tables, numbers, and words, but increasingly represented through interpretations of images, sounds, and texts.
As humans, we have the ability to form analyses by combining what we see, hear, and read. We are able to take such analyses and apply them to other types of data as well.
In this session, we will explore how to transfer this ability to computers. We'll discuss the necessary architectures and AI services to process unstructured data such as images, sounds, and texts, allowing us to integrate it with our tables from more relational sources. The session will provide attendees with actionable takeaways to serve as a starting point or inspiration for their next steps.
An Apache Spark query's journey through the layers of Microsoft Fabric
An Apache Spark Query's Journey Through the Layers of Microsoft Fabric
Join us for an exciting deep dive into the heart of Apache Spark! We'll take you on a journey to see exactly how your Spark queries get executed, both within Apache Spark itself and through the different layers of Microsoft Fabric. Here's what we'll explore together:
* Spark SQL and Catalyst: A break down how Spark SQL works hand-in-hand with the Catalyst optimizer to make your queries smarter and faster.
* A Note on Tungsten: Discover how Tungsten boosts Spark’s performance with better memory management and lightning-fast execution.
* A note on Fabrics native execution engine: Bringing the power of C++, for even faster query execution.
*Delta Lake: See how Delta Lake makes your data lakes more reliable and scalable, ensuring your data is always in top shape.
*Parquet Files: Learn why Parquet’s columnar storage is a game-changer for efficient data storage and quick retrieval.
We'll look into the official Apache Spark source code on GitHub, giving you a real, hands-on look at what's happening under the hood.
By the end of this session, you'll have a clearer understanding of how your queries run and some tools and tips to help you solve problems and optimize your Spark jobs for both speed and cost.
Outperform Spark with Python Notebooks in Fabric
When Microsoft Fabric was released, it came with Apache Spark out of the box. Spark's ability to work with more programming languages opened up possibilities for creating data-driven and automated lakehouses. On the other hand, Spark's primary feature to scale out and handle large amounts of data will, in many cases, be over-dimensioned, less performant, and more costly when working with trivial workloads.
With Python Notebooks, we have a better tool for handling metadata, automation, and processing of more trivial workloads, while still having the option to use Spark Notebooks for handling more demanding processing.
We will cover:
* The difference between Python Notebooks and a Single Node Spark cluster, and why Spark Notebooks are more costly and less performant with certain types of workloads.
* When to use Python Notebooks and when to use Spark Notebooks.
* Where to use Python Notebooks in a meta-driven Lakehouse
* A brief introduction to tooling and move workload between Python Notebooks and Spark Notebooks.
* How to avoid overload the Lakehouse tech stack with python technologies.
* Costs
After this session, attendees will have an understanding of how to apply Python Notebooks, as well as Spark Notebooks, to get the most out of a Fabric Capacity for data processing.
ML and AI Capabilities in Microsoft Fabric
Microsoft Fabric is becoming the one-stop shop for data in Azure, including machine learning and AI. Fabric's maturity is starting to enable real projects with its machine learning and AI capabilities. As with many other aspects of Fabric, there are also new libraries and tools for machine learning and AI. These might be different, especially for those coming from Azure ML.
The session will cover:
* Basic and AutoML machine learning
* Hyperparameter tuning
* OpenAI/GenAI
* MLOps, including model tracking, model repository, and model serving
* How is AzureML still relevant?
* Fabric Workspace layout, capacities, and costs
Attendees will leave with practical insights into using AutoML, hyperparameter tuning, and MLOps within Fabric, along with how OpenAI and GenAI fit into the ecosystem. We’ll also discuss how Azure ML remains relevant and how to navigate Fabric’s workspace, capacities, and associated costs to maximize your project's efficiency and potential.
Introduction to Vibe Coding and MCP for Building a Dataplatform in Microsoft Fabric
Vibe Coding (tell your computer what to code) and MCP servers have been growing rapidly over the last year. Terms like “Talk to your data” are appearing more and more. While it sounds ideal to simply tell a computer, using speech or text, how to build a data platform, there are important considerations to keep in mind to avoid pitfalls.
This session is an enthusiastic yet critical introduction to building data platforms with AI in Microsoft Fabric.
We will cover:
* What are we trying to achieve with AI? Can it close competence gaps or even replace developers?
* Introduction to MCP (Model Context Protocol)
* What Fabric MCP Servers are available
* Data modelling
*Testing and QA
* Security considerations
After this session, attendees should have a clear idea of how they can build a data platform by chatting, an understanding of common pitfalls, and inspiration to get started with their own MCP servers.
Data Platform Next Step 2023 Sessionize Event
Data Saturday Denmark - 2023 Sessionize Event
Christian Henrik Reich
Sr Solution Architect @ Microsoft
Copenhagen, Denmark
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