Introduction

Data visualization and business intelligence (BI) tools play a crucial role in decision-making for businesses. Among the popular BI tools, Microsoft Power BI and Apache Superset stand out as two strong contenders. Power BI is a widely adopted enterprise BI tool, while Apache Superset is an open-source alternative gaining traction in modern data engineering and cloud-native ecosystems.

1. Architecture & Deployment

Power BI Architecture

• Client-Server Model: Power BI follows a client-server model, where users create reports on Power BI Desktop and publish them to the Power BI Service (cloud-based) or Power BI Report Server (on-premises).
• Data Engine: Power BI’s core engine is VertiPaq, an in-memory columnar storage engine optimized for fast aggregation.
• Deployment Modes: Power BI can be deployed as Power BI Service (Cloud), Power BI Report Server (On-Premises), or Power BI Embedded for custom applications.
• Integration with Microsoft Ecosystem: Deep integration with Azure, SQL Server, and Office 365.

Apache Superset Architecture

• Cloud-Native & Open-Source: Superset is an open-source BI tool designed for modern cloud-native data stacks.
• Web-Based Interface: Runs as a web application using Flask & React.
• Database Query Execution: Uses SQLAlchemy to connect with various databases and supports direct querying (push-down queries) instead of in-memory storage.
• Deployment Options: Can be deployed using Docker, Kubernetes, or traditional servers.
• Lightweight and Scalable: Designed for distributed data platforms and works well with Presto, Trino, and BigQuery.

Key Differences:

• Power BI relies on a proprietary in-memory engine (VertiPaq), while Superset uses a query-driven approach with SQLAlchemy and Async Queries.
• Superset is cloud-native and designed for big data and distributed architectures, whereas Power BI is more traditional and integrated with Microsoft products.

2. Data Connectivity & Integration

Power BI Data Connectivity

• Supports over 150+ Data Connectors, including:
  • SQL Server, Azure SQL, PostgreSQL, Oracle, MySQL
  • Spark, Databricks, Snowflake, Google BigQuery
  • Excel, SharePoint, PowerApps, and more
• DirectQuery Mode for live database queries
• Import Mode for high-performance in-memory processing
• Power Query (M) for data transformation and ETL

Apache Superset Data Connectivity

• Uses SQLAlchemy for database connections
• Native support for:
  • PostgreSQL, MySQL, SQLite, SQL Server
  • BigQuery, Trino, Presto, ClickHouse, Druid, Apache Hive
  • No direct Excel or SharePoint support
• Query Processing:
  • Does not store data; always queries the database directly
  • Asynchronous query execution via Celery workers

Key Differences:

• Power BI supports Excel, SharePoint, and Azure connectors, making it a better fit for Microsoft users.
• Superset is optimized for big data and distributed SQL engines.

3. Data Modeling & Transformation

Power BI

• Data Modeling in Power BI Desktop:
  • Supports Star Schema, Snowflake Schema
  • Uses DAX (Data Analysis Expressions) for calculations
• Power Query (M Language) for ETL & data transformation
• Aggregations, relationships, calculated columns/measures

Superset

• SQL-based Transformations
• No built-in data modeling, relies on the underlying database schema
• Users must create views or materialized tables before visualization

Key Differences:

• Power BI offers a dedicated data model layer with relationships and computed measures.
• Superset requires pre-modeled data in the database and lacks a built-in ETL engine.

5. Performance: Power BI DirectQuery vs. Superset Query Performance

Power BI DirectQuery

• Executes live queries on the database instead of storing data in memory.
• Latency dependent on source database performance.
• Optimized with Aggregations and Composite Models (mix of Import & DirectQuery).
• Can be slow if queries involve multiple joins or large datasets.

Superset Query Performance

• Executes SQL queries directly on the database (push-down processing).
• Asynchronous queries via Celery workers improve concurrency.
• Scales better for big data systems like Presto, Trino, or ClickHouse.
• No in-memory caching by default, but can use external caching layers (e.g., Redis).

Key Differences:

• Power BI DirectQuery is ideal for real-time data but can slow down with complex queries.
• Superset handles distributed queries better but lacks native caching for repeated queries.
• Power BI Import Mode is faster than DirectQuery for pre-aggregated datasets.

6. Security & Access Control

Power BI Security

• Row-Level Security (RLS) and Object-Level Security (OLS)
• Azure AD Integration & Role-Based Access Control (RBAC)
• Data Encryption and Compliance with Enterprise Policies

Superset Security

• Role-Based Access Control (RBAC)
• OAuth, LDAP, and Database Authentication Support
• No built-in Row-Level Security (requires database implementation)

Key Differences:

• Power BI has more advanced security controls and native Azure integration.
• Superset security is flexible but requires manual configuration.

SUMMARY:

Power BI: If you need enterprise security, data modeling, and interactive dashboards.

Superset: If you need big data querying, distributed SQL support, and open-source flexibility.

P Sakhib Rahil