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Fabric / Synapse

Unified data engineering, lakehouse, warehousing, parquet storage, data pipelines, notebooks, Power BI dashboards, semantic models, and analytics solutions with Microsoft Fabric and Azure Synapse.

What We Build

  • Microsoft Fabric lakehouses and OneLake data foundations for raw, curated, parquet, Delta, and Power BI-ready data.
  • Fabric Data Factory pipelines and Dataflows Gen2 for ingestion, transformation, orchestration, and repeatable data movement.
  • Synapse and Fabric notebooks for PySpark, SQL, feature engineering, model preparation, and large-scale analytics.
  • Fabric Warehouses and SQL analytics endpoints for views, reporting datasets, dashboard facts, and semantic models.
  • Power BI dashboards, apps, semantic models, KPIs, historical reports, and executive visualizations.

Example Use Cases

  • Build a lakehouse with parquet files, Delta tables, curated facts, and reporting-ready dimensions.
  • Transform API, SQL, file, stream, and operational data into warehouse tables and Power BI semantic models.
  • Use Synapse or Fabric notebooks to process large datasets and engineer ML-ready features.
  • Create executive dashboards from lakehouse and warehouse outputs.
  • Modernize legacy SQL or Synapse analytics into Fabric data engineering and Power BI reporting experiences.

Fabric and Synapse as the Data Platform

Fabric and Synapse provide the data side of the modern analytics architecture. They connect source systems, data lakes, parquet files, notebooks, SQL analytics, warehouses, semantic models, Power BI dashboards, and ML outputs into one governed analytics platform.

  • OneLake / Lakehouse: central data foundation for raw files, parquet files, Delta tables, curated data, and Power BI-ready outputs.
  • Data Pipelines: orchestrate ingestion, transformations, notebook execution, and reporting refreshes.
  • Dataflows Gen2: Power Query-style transformations for shaping and cleansing data.
  • Warehouse / SQL: views, SQL endpoints, reporting facts, dimensions, and semantic model support.
  • Power BI: dashboards, apps, visualizations, drill-through reports, insights, and executive reporting.
  • Synapse: notebooks, Spark pools, SQL pools, pipelines, and large-scale analytics development.

Fabric and Synapse in Use

The screenshots below are Azure/Fabric/Synapse-style visuals packaged locally with this page so they render reliably. They show lakehouse data, parquet files, data pipelines, Power Query/Dataflows, notebooks, warehouse SQL, Power BI dashboards, Synapse workspace, and full architecture.

Fabric Lakehouse OneLake screenshot

Fabric Lakehouse / OneLake

Lakehouse structure for raw files, parquet folders, Delta tables, ML features, and Power BI-ready curated data.

Parquet files in data lake screenshot

Parquet Files and Data Lake

Partitioned parquet files support efficient analytics scans, large historical datasets, and lakehouse reporting patterns.

Fabric data pipeline screenshot

Data Pipelines

Pipeline orchestration moves source data through copy, notebook, lakehouse, and Power BI refresh steps.

Fabric Dataflows Gen2 Power Query screenshot

Dataflows Gen2 / Power Query

Power Query-style transformations filter, merge, group, enrich, and output curated lakehouse data.

Fabric Synapse notebook screenshot

Fabric / Synapse Notebook

PySpark notebooks process lakehouse tables, engineer features, and publish gold dashboard metrics.

Fabric warehouse SQL analytics screenshot

Warehouse and SQL Analytics

Warehouse query editor supports views, reporting facts, predictions, and Power BI semantic model outputs.

Power BI dashboard screenshot

Power BI Dashboards

Executive dashboards visualize KPIs, historical trends, forecast accuracy, risk, and operational outcomes.

Azure Synapse workspace screenshot

Azure Synapse Workspace

Synapse workspace brings together data, notebooks, pipelines, SQL pools, Spark pools, and Power BI integration.

Fabric Synapse architecture screenshot

Full Data Platform Architecture

Sources, OneLake, Fabric/Synapse, semantic models, Power BI, ML, and AI outputs connected into one platform.

Architecture Flow

Sources

APIs, Azure SQL, files, streams, Cosmos, operational systems, and historical datasets.

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OneLake

Raw files, parquet, Delta tables, lakehouse folders, and curated data zones.

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Fabric / Synapse

Pipelines, Dataflows, notebooks, Spark, SQL analytics, and warehouses.

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Semantic Model

Relationships, measures, RLS, KPIs, curated facts, and reporting dimensions.

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Power BI

Dashboards, apps, visualizations, drill-through pages, reports, and insights.

This pattern gives organizations an end-to-end data platform where source data lands in the lakehouse, parquet and Delta tables preserve efficient history, Fabric and Synapse transform and model the data, and Power BI turns curated outputs into interactive dashboards and decision-ready insight.

Business Value

  • Unified data engineering, warehousing, analytics, and reporting in one platform.
  • Efficient historical storage using parquet and Delta table patterns.
  • Power BI dashboards built from governed lakehouse and warehouse outputs.
  • Reusable pipelines, notebooks, SQL endpoints, and semantic models.
  • Modern analytics foundation for dashboards, ML predictions, AI agents, and operational decisions.

Example Production Flow

  • Source data is ingested into OneLake and stored as raw files or parquet.
  • Data pipelines and Dataflows transform raw inputs into curated lakehouse tables.
  • Notebooks engineer features, aggregate history, and create ML-ready outputs.
  • Warehouse views and SQL analytics endpoints expose trusted reporting models.
  • Power BI semantic models and dashboards deliver executive visuals, trends, and insights.
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