Cole Tramp's Microsoft Insights

Overview

At FabCon 2026, Microsoft took another important step toward reshaping how organizations manage databases by announcing the Database Hub in Microsoft Fabric. While the Database Hub is still in early access and not yet generally available, it represents a meaningful shift in how Microsoft is thinking about database management at scale.

For years, database teams have operated across fragmented tools, portals, and management experiences depending on whether the database lived on‑premises, in Azure PaaS, or in a SaaS environment. As data estates grow and AI workloads place greater pressure on operational data, that fragmentation becomes a real problem. The Database Hub is Microsoft’s answer to this challenge, providing a unified control plane for managing databases across the enterprise from within Fabric.

Anyone running SQL Server today, whether on‑premises or in Azure, should be paying close attention. This is not just another management experience. It is a signal of where Microsoft is taking databases as part of a single, converged data platform.

What the Database Hub Is

Read More

Overview

Microsoft Fabric security is built on two distinct layers that are often confused but serve very different purposes:

• Control plane access determines what you can do in Fabric, such as creating items, managing workspaces, and sharing content.
• Data plane access determines what data you can actually see or interact with inside OneLake.

For much of Fabric’s early life, workspace roles were used as the primary security boundary. That works for collaboration, but it becomes problematic as platforms scale and data products need stronger governance.

This is where OneLake security comes in. It introduces native, fine-grained security directly at the storage layer, allowing organizations to separate operational permissions from data access. At FabCon, Microsoft announced that OneLake security is going GA in April 2026, signaling that this model is ready to become the standard for enterprise Fabric deployments.

Control Plane Access: Workspace Roles

Read More

Daymark Solutions is at FabCon for a second year in a row!

There’s something energizing about being back at the Microsoft Fabric Community Conference. After attending last year, it’s great to once again be surrounded by people who are genuinely excited about Fabric, from data engineers and analysts to architects and partners building real world solutions every day.

Read More

Read More

Overview

Microsoft Fabric provides multiple ways to implement transformation logic and operationalize it within Fabric Data Factory pipelines. Two of the most common approaches are Fabric notebooks and SQL stored procedures.

Both are first class tools in Fabric and both can be orchestrated through Data Factory pipelines. The difference is not about which one is better. It is about how the processing is executed, where the logic lives, and what development style best fits the workload.

Notebooks are built on Apache Spark and are designed for distributed, code driven data engineering and analytics workflows. Stored procedures run directly in the SQL engine and are optimized for relational, database centric operations. In real world Fabric architectures, it is common and often recommended to use both together.

Understanding the strengths of each helps teams design pipelines that are scalable, maintainable, and aligned with how their data is structured and governed.

Fabric Notebooks

Read More

Overview

A data gateway in Microsoft Fabric and Power BI is the secure connectivity layer that allows cloud services to access data sources that aren’t publicly reachable. This includes on-premises systems, Azure resources locked behind private endpoints, and streaming platforms running inside private networks.

Gateways are critical because most real-world architectures are hybrid. Even as organizations adopt Fabric, they often need to integrate with legacy systems, tightly secured Azure services, or real-time platforms that cannot be exposed to the public internet. Gateways make this possible without compromising security or network boundaries.

Microsoft Fabric currently supports three gateway types, each optimized for a different scenario:

• On-premises data gateway
• Virtual network data gateway
• Streaming virtual network data gateway

Understanding when to use each one helps avoid unnecessary complexity and ensures the right balance of security, performance, and manageability.

What a Data Gateway Does in Fabric and Power BI

Read More

Overview

Organizations building modern analytics platforms on Azure often start with Azure Data Lake Storage Gen2 as their foundational storage layer. Azure Data Lake is highly scalable, cost effective, and flexible, making it an attractive landing zone for raw data of all types. However, without strong governance, modeling, and processing layers, many data lakes gradually devolve into what is commonly referred to as a data swamp.

Microsoft Fabric Lakehouse was introduced to address these challenges by combining the openness of a data lake with the structure, governance, and usability of a warehouse. Unlike a traditional data lake, Fabric Lakehouse is delivered as a fully managed SaaS experience that tightly integrates storage, compute, governance, security, and analytics into a single platform.

Understanding the differences between Azure Data Lake and Fabric Lakehouse is critical when designing scalable, maintainable, and business-ready data architectures.

Azure Data Lake Storage Gen2

Read More

Overview

Organizations modernizing their data ecosystems now manage data flowing from cloud applications, SaaS platforms, on-premises systems, and streaming sources. Traditional ETL tools such as SQL Server Integration Services (SSIS) have long powered enterprise data warehousing and batch integration, offering a robust, code-optional environment for building data workflows. SSIS provides a Windows-based, SQL Server-centric platform for extracting, transforming, and loading data using visual design tools, built-in connectors, and highly customizable task orchestration.

Microsoft Fabric introduces a different approach with Fabric Data Factory, a cloud-scale, fully managed data integration experience built on top of the unified Fabric platform. As the next evolution of Azure Data Factory, Fabric Data Factory offers more than 170 connectors, AI-assisted transformations, hybrid connectivity, and seamless integration with OneLake, making it suitable for modern cloud-first analytics and distributed architectures.

Rather than relying on a single on-premises integration engine, organizations now align workloads to cloud-native services that scale elastically, integrate across multicloud environments, and unify data ingestion with analytics and AI capabilities.

Key Differences Between SSIS and Fabric Data Factory

Read More

Overview

For many years, organizations commonly relied on on‑premises SQL Server as their enterprise data warehouse. While this approach made sense when cloud platforms were immature, it is no longer necessary, or efficient, in modern analytics architectures. Microsoft Fabric fundamentally changes this model by providing purpose-built, cloud-native analytical engines that eliminate the need to manage traditional data warehouse infrastructure.

Within Fabric, SQL Database and Data Warehouse serve different but complementary roles. Understanding when to use each is critical to building a scalable, governed, and future-proof data platform.

Instead of lifting and shifting legacy SQL Server workloads into the cloud, organizations can now adopt services designed specifically for analytical patterns, elasticity, and tight integration with the broader Fabric ecosystem.

 

The Shift Away from On-Prem SQL Server as a Data Warehouse

Read More

Overview

The data engineering community has long embraced dbt (Data Build Tool) for its simple, SQL-first approach to building, testing, and orchestrating data transformations. Historically, teams using dbt with Microsoft Fabric had to rely on external compute (local development machines, GitHub Actions, Azure DevOps pipelines, virtual machines, or standalone orchestration platforms like Airflow). These setups worked, but they added friction: managing environments, handling adapters, configuring authentication, and monitoring transformation jobs across disparate systems.

With dbt jobs now running natively inside Microsoft Fabric, that fragmentation disappears. dbt is no longer an external tool bolted onto the modern data estate, it is now a first‑class, integrated capability of Fabric, providing a unified experience for modeling, testing, scheduling, and monitoring transformations directly inside Fabric workspaces.

dbt, Natively Integrated: What’s New

Read More