In this deep technical blog, the Microsoft Fabric Blog team explains how they engineered a robust, Petabyte-scale data platform on Microsoft Fabric, focusing on real-time telemetry and modern data engineering practices.

SQL Telemetry & Intelligence – How we built a Petabyte-scale Data Platform with Fabric

Overview

Over the past three years, the SQL Telemetry & Intelligence (T&I) Engineering team has developed a massive data lake (over 10 Petabytes) running on Microsoft Fabric. This platform ingests, processes, and analyzes real-time data from globally distributed SQL Server engines and control/data plane services, forming the analytics backbone for telemetry at scale.

Architecture and Design

  • Lakehouse Architecture: Employs a Medallion (bronze-silver-gold) layer pattern influenced by the Lambda architecture, with real-time streaming and optimized data compression (columnar Parquet).
  • Data Ingestion: Utilizes OpenTelemetry for instrumenting services, Event Hub for control plane events, and Azure Data Explorer for time-series storage. Real-time data flows are mirrored into Delta Lake storage via high-throughput C#/Rust services running on AKS with KEDA.
  • ETL & Streaming: Spark Streaming is used for schema enforcement, combining micro-batches, supporting low-latency processing and horizontal scalability. Transformations are managed via versioned samples and stateful/isolated checkpoints.
  • Datamodeling: Adopts Kimball STAR schemas with SCD2 dimensions and transaction grain, focusing on idempotency and data integrity (primary/foreign key enforcement, broadcast joins, and periodic snapshot tables).
  • Semantic Layer: Implements a DirectLake semantic model, leveraging Tabular Editor, Power BI Desktop, and DAX Studio for advanced analytics.

Operational Tooling & DevOps Practices

  • Development Environment: Teams use VSCode Devcontainer setups for local development with consistent dependencies (pinned Spark/JDK versions, extensions, etc.), streamlining build/test cycles. All code is tested locally before deploying to Fabric Spark via the VSCode Extension.
  • CI/CD Automation: Full-stack environments are provisioned per engineer using GitOps manifests, wrapping the Fabric CLI, Fabric-cicd, and APIs for automated deployment. Version control extends even to workspace icons.
  • Legacy & Integration: For regions lacking Fabric, Synapse Workspaces are maintained and an internal workspace-deployment app automates cross-platform environments.

Data Quality and Analytics

  • Data Quality: The team employs Deequ for robust data validation, anomaly detection, and data quality rules using DQDL. Integrity enforcement utilizes clustering and optimized query patterns (e.g., WHERE EXISTS over JOINs).
  • SLA Monitoring: SLAs are defined via YAML, version-controlled, and integrated with Spark DataFrames for breach detection, automated alerting via Fabric Activator, and visualization in Power BI.
  • Testing: Testing strategies involve parallelized execution and Spark configuration tuning to reduce runtimes by up to 67%.

Scaling & Challenges

  • Scalability: Autoscale billing in Spark allows handling variable workloads (8000 cores at peak). Backfilling and incremental view maintenance strategies provide resilience, minimize reprocessing, and protect checkpointed streaming jobs.
  • Incremental Processing: Explores advanced optimization using SQL AST rewriting (with references to Linkedin Coral and research on DBSP) and features from Fabric’s Materialized Lake Views for cost-efficient refreshes.

Future Directions

  • Expansion into dbt-on-Fabric to enable self-serve analytics for developers.
  • Broader use of AI/ML for anomaly detection and advanced KPI monitoring.

Key Technical Highlights:

  • Real-world application of Spark Streaming and Kimball modeling at hyperscale.
  • Heavy automation via DevOps pipelines, GitOps, and tailored deployment frameworks.
  • Emphasis on reproducibility, regression-proofing, and efficient local/cloud development.
  • Multi-layered quality enforcement (data, SLA, operational metrics).
  • Deep integration of Microsoft and open-source tools (Fabric, AKS, Spark, Delta Lake, Deequ, Power BI).

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