Johan Ludvig Brattås

Join two experts on Fabric Real-Time Intelligence and Fabric IQ for a hands-on, full-day workshop where AI meets real-time data. Explore Microsoft Fabric’s Real-Time Intelligence and Fabric IQ to build intelligent, event-driven architectures.

Organizations are increasingly adopting lakehouse architectures to unify analytics and machine learning workloads. However, many implementations suffer from poor foundational decisions that become costly to remediate later.

Event-driven architecture (EDA) is a design pattern in which the flow of data through the layers of the data platform (such as ETL pipelines) is determined by events such as user actions, sensor outputs, or messages from other programs. Event-driven architecture can enable real-time data processing and decision-making…

Organizations are increasingly adopting lakehouse architectures to unify analytics and machine learning workloads. However, many implementations suffer from poor foundational decisions that become costly to remediate later.

Data Products are not just a part of the data mesh paradigm, even if this is the first place many hear about the concept. But what exactly IS a data product? There are surprisingly many takes on what a data product is, but in this session we will take a closer look at what core features every data product should have regardless. We will also discuss the various forms of data products that can appear as well as how you can place proper governance on them. We will also take a look at data contracts, which are a central feature when applying data products successfully.

The Delta lake format has become quite popular, and as the core storage format for most of the Fabric services it suddenly is very relevant for many Microsoft data professionals. But what exactly is Delta lake?

Real-Time Intelligence in Fabric is designed to enable organisations to bring their streaming, high-granularity, time-sensitive event data into Fabric and build various analytical, visual and action oriented data applications and experiences with it. In this session, you will learn about what Fabric Real-time…

The Delta lake format has become quite popular, and as the core storage format for most of the Fabric services it suddenly is very relevant for many Microsoft data professionals. But what exactly is Delta lake?

Real-Time Intelligence in Fabric is designed to enable organisations to bring their streaming, high-granularity, time-sensitive event data into Fabric and build various analytical, visual and action oriented data applications and experiences with it. In this session, you will learn about what Fabric Real-time…

Join us as we guide you through how to secure your real-time data, how to implement Fabric Real-time Intelligence at speed and at scale across your estate, and how to automate the creation of all the resources you need to incorporate real-time data into your data ecosystem. At the close of this session, you will have access to a code repository that will give you the tools to recreate every demo y

Real-Time Intelligence in Fabric is designed to enable organisations to bring their streaming, high-granularity, time-sensitive event data into Fabric and build various analytical, visual and action oriented data applications and experiences with it. In this session, you will learn about what Fabric Real-time Intelligence is meant for and will then build an end-to-end solution using streaming data. This session will get you hands-on with Real-Time Hub, Eventstream, Eventhouse, Data Activator, Real-time Dashboards, Power BI, and Copilot with combination of Microsoft MVPs and Fabric product group members to guide you along the way. The end-to-end solution we build together will encompass: • Discovering and cataloging your data streams using Real-Time Hub • Connecting to streaming data from various sources around the world • Cleaning, normalizing, preparing data for superior query time experiences • Leveraging Copilot to discover streaming data • Low-code/no-code experiences for data exploration • Developing dashboards

If you ask a user how often they need data their initial answer is often "in real time", right? Once you solve for getting them that data in real-time, then where would you store it? Once it was stored, what tool or tools would you use to visualize it? Once that ecosystem is set up and stable, how does it interact with the data warehouses and data lakes in your estate? The Real-Time Hub is your starting point for building real-time applications in Fabric. It's a kind of action center for bringing real-time events from a variety of sources (and clouds) into Fabric, learning how to analyze - and act upon - that data, storing it, and then visualizing it in real-time dashboards. In this session, we will explore what you can do with Real-Time hub and what real world scenarios you can unlock based on our real world experiences. Following on from there, we'll be using some real-time sample data sources provided within the hub to show you how to easy it is to pull in some real-time data and see all the capabilities that Real-Time Intelligence has to offer you.

Microsoft launched their new data and analytics offering Microsoft Fabric in preview in May this year. A Data Platform As A Service - available together with Power BI.

Real-Time Intelligence in Fabric is designed to enable organisations to bring their streaming, high-granularity, time-sensitive event data into Fabric and build various analytical, visual and action oriented data applications and experiences with it.

Snowflake has grown to become a huge success worldwide and one of the main contenders for modern analytics solutions. But why is it a data platform? And exactly how can it fit into your data landscape? We will look at the different parts of the Snowflake offering and how they might fit into the data landscape. What are Snowflakes strengths? And what are the weaknesses?

While Microsoft Fabric is a full-blown data platform in its own right, you might allready have invested in a modern data platform allready. You still use Power BI of course - because that is the best tool out there.

Join Johan and Rune as they present arguments from both sides and discuss why Data Mesh is the new way forward… or just the same old stuff in a different wrapping.

Real-time analytics is one of those topics we hear about but rarely get the chance to look closely at. Well, today this will change. Join in for a hands-on experience where you get to understand exactly what real-time analytics is about

On the quest for the holy grail of big data analysis, Frank and Johan do not have to cross catacombs or fly an airship. Instead, the quest for big data analysis is much easier as they are aided by the Azure Data Explorer (ADX).

Often we need to find a way to uncover the hidden insights buried deep within the data jungles of the company. This can be a challenging task, but myths talk about the legendary Azure Data Explorer (ADX). Many claim it can handle even the most massive and complex data sets, as it's a brand new timeseries database in Azure. If you can handle the adventure, join our two explorers when they set out on a journey to discover the vast capabilities of ADX. We'll navigate through the setup and configuration of ADX like Indiana Jones did through ancient ruins. With their whip-like queries we'll uncover the powers of the Kusto Query Language (KQL) to solve our data puzzles. As we delve deeper into this data exploration, we'll find out that ADX is much more than a simple tool; it's like having a trusty sidekick by our side, helping us to navigate the treacherous waters of data analysis. With much adventure, we'll also unearth the link between ADX and Azure Cosmos DB. At the end of this ride, the audience will realize that with ADX on their side, they too can become daring data explorers. They will know when to use ADX and what its strengths and weaknesses are.

No, not that kind of lakehouse... The data lakehouse architecture launched launched by Databricks a few years ago has become a viable solution for modern data platforms, increasingly so with the string of new features released from Databricks lately.

Microsoft has launched a lot of servicess in the data and analytics space the last years. And it has become pretty clear that their vision for this space is encompassing and solid.

…or the best original idea since the invention of sliced bread? In this session Johan and Rune will presents arguments from both sides and discuss why Data Mesh is the new way forward… or just the same old stuff in a different wrapping. We will further discuss how the current cloud toolset can be used in a “Data Mesh-way”… or just how it should be used anyways using the terms, methods and practices we know from before. Walking away from this session we hope we have clarified for you: - What is Data Mesh? - Why is Data Mesh a great idea… or not so great? - How can we do modern data platforming the “Data Mesh-way”… or not? Welcome to 60 minutes of enlightenment…?

Gartner claimed the death of data warehousing a few years ago. They where wrong. But the cloud data warehouse (or should I say lakehouse?) have some twists to the traditional on-premises warehouses. One of them is the amount of data in play, another is the various new services you can choose to combine into your new data warehouse. Snowflake, Databricks, Synapse, Redshift AQUA and what about Big Query? Let's take a look on the role the data warehousing service plays in your modern cloud data platform.

Data governance is seen as boring and something you don't really need. But it is in fact the key to success for your data platform. As the amount of data sources, as well as the size of the data from the various sources increases - how can your users know what data is available? Or where to find it and what state it is in? When your users trust the data you deliver, they will use what you develop. Another aspect to that trust is personal identifiable information - can citizens trust that you keep their data safe and respect their privacy? This session will show you how to establish both kinds of trust, using Azure native tooling as well as third-party tools. But most importantly - tips on methodology, routines and people!

The Azure Cloud Adoption Framework (CAF) is a set of guidance and best practices from Microsoft. It comes with reccomendations as well as sample ARM templates on how to establish various sized Azure tenants, subscriptions and policies.

Data governance is seen as boring and something you don't really need. But it is in fact the key to success for your data platform. As the amount of data sources, as well as the size of the data from the various sources increases - how can your users know what data is available? Or where to find it and what state it is in? When your users trust the data you deliver, they will use what you develop. Another aspect to that trust is personal identifiable information - can citizens trust that you keep their data safe and respect their privacy? This session will show you how to establish both kinds of trust, using Azure native tooling as well as third-party tools. But most importantly - tips on methodology, routines and people!

Azure is ready to recieve all your event- and devicedata for storage and analysis. But which options in the Azure IoT portfolio should you use to recieve and manage your data? In this session I will explain the different options in the Azure IoT portfolio, take a closer look at how they work and what this means for you. Furthermore, I will take a closer look at the Azure Stream Analytics (ASA) language. You will learn how to develop both simple and complex ASA queries, and how to debug. We will look at the possibilities, limitations and pitfalls in the Azure Stream Analytics language. And finally look at the different input and output choices and when to use which one. This includes a look at how to build a live stream dashboard with Stream Analytics data in PowerBI. The session is based on real world project experiences and will use real world data in the demos.