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Azure Analytics Platforms

BenchBox supports Azure-native analytics platforms alongside multi-cloud options (Databricks, Snowflake).

Overview

Microsoft Azure offers multiple analytics platforms for different workload patterns:

PlatformTypeFocus AreaStatus in BenchBox
Microsoft FabricUnified SaaS PlatformData engineering, warehousing, BI, real-time analyticsSupported (Warehouse + Spark + Lakehouse SQL)
Azure Synapse AnalyticsEnterprise Data WarehouseData warehousing, big data analyticsSupported (SQL + Spark)
Azure Data ExplorerReal-time Analytics EngineTime-series, telemetry, streaming dataUnder evaluation

Microsoft Fabric

Status: Supported (Warehouse + Spark + Lakehouse SQL)

Platform Type: Unified SaaS data analytics platform

Technical Characteristics:

  • OneLake data lake with multi-cloud support
  • Integrated workloads: Data Engineering, Data Factory, Data Science, Real-Time Intelligence, Data Warehouse, Databases
  • Capacity-based pricing model
  • Built-in Copilot AI assistance
  • Delta Lake Parquet format (not SQL Server native format)

Common use cases:

  • Organizations consolidating multiple data tools into unified platform
  • Teams requiring integrated data engineering, warehousing, and BI
  • Multi-cloud data lake architectures
  • Self-service BI with Power BI integration

BenchBox Integration:

  • Supports Microsoft Fabric Warehouse items via T-SQL
  • Entra ID authentication (service principal, default credential, interactive)
  • OneLake staging for bulk data loading via COPY INTO
  • Automatic query translation from standard SQL

BenchBox provides three adapters:

  • fabric-dw (or legacy fabric_dw) - For Warehouse items via T-SQL
  • fabric-spark - For Spark/Lakehouse workloads
  • fabric-lakehouse - For read-only SQL query benchmarking over Lakehouse tables

See Microsoft Fabric Platform Guide, Fabric Spark Platform Guide, and Fabric Lakehouse SQL Guide for detailed usage.

Azure Synapse Analytics

Status: Supported (SQL Pools + Spark)

Platform Type: Enterprise data warehouse and big data analytics service

Technical Characteristics:

  • Dedicated SQL Pools (MPP architecture)
  • Serverless SQL pools (pay-per-query)
  • Apache Spark pools for big data processing
  • T-SQL dialect with SQL Server compatibility
  • PolyBase for external data access
  • Azure AD authentication support

Common use cases:

  • Enterprise data warehousing on Azure
  • Big data analytics with Spark
  • Data lake queries via serverless SQL
  • Azure-native analytics workloads

Architecture: PaaS with choice of serverless or dedicated compute resources

BenchBox Integration:

  • synapse adapter for SQL pools via T-SQL
  • synapse-spark adapter for Spark pools
  • Entra ID authentication support
  • Automatic query translation from standard SQL

See Azure Synapse Spark Platform Guide for Spark-specific usage.

Azure Data Explorer (Kusto)

Platform Type: Fast analytics service for real-time data

Technical Characteristics:

  • Kusto Query Language (KQL) with T-SQL support
  • High-throughput ingestion (up to 12 Mbps per core)
  • Time-series analysis functions
  • Streaming data support
  • Integration with Azure Event Hubs, IoT Hub

Common use cases:

  • Real-time telemetry and log analytics
  • IoT data analysis
  • Time-series workloads
  • Application performance monitoring

Architecture: Fully managed service optimized for time-series and streaming data

Integration Considerations: KQL query translation, streaming ingestion patterns, time-series benchmark adaptations

Future Platforms

Azure Data Explorer is under evaluation for future BenchBox releases. Key considerations include:

  • KQL query translation requirements
  • Streaming ingestion patterns
  • Time-series benchmark adaptations

BenchBox is open source under the MIT License.