MICROSOFT FABRIC & POWER BI

ANALYTICS & DATA PLATFORM

From Raw Data to Boardroom-Ready Insights

Microsoft Fabric and Power BI analytics consulting Mission Beach Far North Queensland

Microsoft Fabric is the next generation of Microsoft's data platform — combining data engineering, data science, real-time analytics, and business intelligence into a single, unified SaaS product. Where organisations previously needed separate tools for ETL pipelines, data warehousing, machine learning, and reporting, Fabric brings them together under one governance model, one billing meter, and one set of identities. The result is a dramatically simpler analytics stack for Australian businesses that want enterprise-grade data capabilities without enterprise-grade complexity.

At ELECTRICLATTE, we help organisations at every stage of the data maturity curve — from businesses producing their first Power BI reports from an Excel export, through to teams ready to build a fully governed lakehouse architecture in Microsoft Fabric. We design solutions that match your actual data volumes, team capabilities, and reporting requirements — not solutions that look impressive in a slide deck but are over-engineered for the problem at hand.

LAKEHOUSE & PIPELINES

Ingest, transform, and store data at scale in Microsoft Fabric Lakehouse, with Data Factory pipelines automating the movement of data from your source systems.

SEMANTIC MODELS (DAX)

Business logic centralised in reusable Power BI semantic models — a single source of truth for calculations, KPIs, and hierarchies shared across all reports.

POWER BI DASHBOARDS

Interactive reports and dashboards published to Power BI Service with row-level security, scheduled refresh, and self-service capabilities for business users.

PLATFORM CAPABILITIES IN DETAIL

What We Build in Microsoft Fabric & Power BI

Microsoft Fabric is an end-to-end platform, and our analytics practice covers the full stack — from ingestion through transformation, modelling, and visualisation. Here's what we deliver at each layer.

Microsoft Fabric Lakehouse

The Fabric Lakehouse combines the flexibility of a data lake (storing raw files in open formats like Parquet and Delta) with the query performance of a data warehouse (accessible via SQL endpoint). We design lakehouse architectures using the medallion pattern — raw Bronze data landing from source systems, Silver transformations for cleansed and conformed data, and Gold aggregated tables optimised for Power BI and analytical queries. Delta Lake format provides ACID transactions, schema enforcement, and time-travel capabilities on top of OneLake storage, ensuring your data is reliable and auditable even as schemas evolve.

Data Factory Pipelines & Dataflows

Getting data into your lakehouse reliably is half the battle. Fabric Data Factory provides both pipeline-based ETL (for complex, orchestrated data movement with conditional logic, error handling, and scheduling) and Dataflows Gen2 (for visual, low-code transformations using Power Query M). We design pipeline architectures that handle incremental loads efficiently — only processing new or changed records rather than full table scans — and include parameterisation so the same pipeline logic works across multiple source systems without code duplication. Pipelines are triggered on schedules, on arrival of new files, or through REST API calls from external systems.

Spark & Notebook Workloads

For data volumes and transformation complexity that exceed what Power Query or SQL can handle efficiently, Fabric's Spark engine provides distributed processing with Python (PySpark) or Scala notebooks. We use Spark notebooks for large-scale data cleansing, machine learning feature engineering, complex joins across multi-billion-row datasets, and custom transformation logic that can't be expressed in M or T-SQL. Notebooks integrate with the Fabric Lakehouse directly — reading and writing Delta tables without additional connectors — and can be orchestrated through Data Factory pipelines as part of a larger data workflow.

Semantic Models & DAX

A Power BI semantic model (formerly called a dataset) is the layer between your data and your reports — it defines the business logic, calculations, hierarchies, and relationships that every report in the organisation builds on. Well-designed semantic models are the difference between reports that are fast and trustworthy, and report sprawl where every analyst has their own slightly different version of the same metric. We design star-schema models optimised for DAX query performance, write reusable measure groups covering revenue, margin, volume, variance, and time-intelligence calculations, and implement row-level security (RLS) so users see only the data their role permits.

Power BI Reports & Dashboards

Power BI Desktop reports surface your semantic model data through interactive visuals — bar charts, line charts, maps, tables, KPI cards, and custom visuals from the AppSource marketplace. We design reports with a clear visual hierarchy: summary KPIs at the top, supporting trends and breakdowns below, and detail drill-through pages for investigation. Report design follows your brand guidelines — custom colour palettes, fonts, and logo placement — so the output looks like it belongs to your organisation rather than a generic BI tool. We publish reports to Power BI Service workspaces, configure scheduled dataset refresh, and set up distribution through apps, embedding, or shared links.

Governance, RLS & Power BI Service

A Power BI deployment without governance quickly becomes a maintenance burden — datasets duplicated across workspaces, reports with hardcoded credentials, and no consistent approach to who can see what. We implement Power BI governance frameworks covering workspace structure (development, test, production promotion with deployment pipelines), dataset certification, sensitivity labels using Microsoft Purview Information Protection, and row-level security at both the semantic model and warehouse layer. We also configure Power BI Embedded for applications that need analytics embedded within a custom web interface, removing the requirement for end users to have Power BI Pro licences.

YOUR DATA JOURNEY

Where Most Organisations Start

Most Australian businesses don't need a full Fabric lakehouse on day one. The most common starting point is connecting Power BI directly to existing data sources — SQL Server, Azure SQL, SharePoint lists, or Excel files shared on OneDrive — and building an initial set of reports that replace the manual spreadsheet dashboards your finance or operations team produces every week.

From there, as reporting requirements grow, we introduce a semantic model to centralise business logic and improve performance. When data volumes increase or multiple source systems need to be combined, we introduce the lakehouse layer with automated pipelines replacing manual exports. This incremental approach means you get value early, reduce risk, and build internal familiarity with the platform at each stage.

We also provide Power BI training for your team — so business users can build their own reports from certified semantic models, and the analytics burden doesn't fall entirely on your IT department.

Typical Analytics Engagement Steps

  • Discovery: Understand your data sources, current reporting pain points, and priority questions the business needs answered.
  • Connect & Model: Connect Power BI or Fabric to your data sources and build a certified semantic model with agreed KPIs and business logic.
  • Report Build: Deliver the priority report set — executive dashboards, operational reports, and self-service pages for analysts.
  • Automate & Scale: Introduce pipelines, scheduled refresh, and governance as your data platform matures.
COMMON QUESTIONS

Microsoft Fabric & Power BI — Frequently Asked Questions

Power BI is Microsoft's business intelligence and data visualisation tool — it connects to data sources and produces interactive reports and dashboards. Microsoft Fabric is the broader data platform that Power BI now lives inside — it adds data engineering (pipelines, Spark notebooks), data warehousing (lakehouse, warehouse), real-time analytics (KQL database), and data science (ML models) alongside Power BI's reporting capabilities. For many organisations, Power BI alone (connecting directly to existing databases) is the right starting point. Fabric becomes valuable when you need to integrate multiple source systems, handle large data volumes, or build a centralised data layer that multiple tools can query.

Users need a Power BI Pro or Premium Per User (PPU) licence to view reports shared from standard Power BI Service workspaces. However, there are alternatives: if reports are published from a Premium capacity workspace, consumers can view them with a free licence. Power BI Embedded allows reports to be embedded in your own web application, where end-user licensing is covered by the embedding capacity rather than individual licences — useful when you have a large number of viewers or want to embed analytics in a customer-facing portal. We can help you choose the most cost-effective licensing model for your distribution requirements.

Yes, and this is one of the most common use cases we address. Power BI has hundreds of native data connectors covering SQL Server, MySQL, Azure SQL, SharePoint, Dynamics 365, Salesforce, Google Analytics, REST APIs, Excel files, and many more. For simple reporting across a small number of sources, Power Query transformations within Power BI Desktop can combine and shape the data directly. For more complex scenarios with large volumes or dozens of source systems, we build a Fabric lakehouse that consolidates everything into a single, clean data layer that Power BI connects to — giving you a single source of truth rather than reports that tell slightly different stories depending on which system they came from.

DAX (Data Analysis Expressions) is the formula language used in Power BI semantic models to define calculated measures and calculated columns. While basic Power BI reports can be built without writing much DAX, almost every real-world business reporting requirement eventually hits the limits of drag-and-drop — and that's where DAX expertise becomes the difference between a report that's fast, accurate, and flexible, and one that's slow, wrong, or impossible to extend. Common DAX patterns include time intelligence (year-over-year growth, rolling averages, period comparisons), semi-additive measures (balance sheet values, headcount), and complex filter context manipulation. Our analysts write and review DAX thoroughly, and we document the calculation logic so your team understands what each measure does.

Row-Level Security (RLS) in Power BI controls which rows of data each user sees — so a regional manager sees only their region's sales, or a business unit head sees only their cost centres. RLS is defined using DAX filter expressions in the semantic model and enforced regardless of which report the user opens or what visual they interact with. For more complex access patterns, we implement dynamic RLS using a security table that maps user email addresses to permitted data segments, meaning adding a new user or changing their access requires only a data update, not a model change. We also configure Object-Level Security (OLS) to hide specific columns — such as salary data — from users without the appropriate role.
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