SQL-based Data Analysis Frameworks
A comprehensive list of data analysis and query engine frameworks centered around SQL.
SQL Data Analysis Ecosystem
SQL has continued to evolve as the standard language for data manipulation for half a century and plays a central role in modern data analysis. Beyond traditional relational databases, diverse technologies including distributed query engines, columnar analytical databases, and data transformation tools have adopted SQL interfaces, promoting the democratization of data engineering.
Key Features
- Declarative Data Manipulation: Simply describe "what" to retrieve, and the engine optimizes "how" to process
- Standardized Syntax: Common syntax based on ANSI SQL enables easy knowledge transfer between different systems
- Wide Application Range: Supports all data processing from OLTP to OLAP, streaming to batch processing
- Low Learning Curve: Accessible to a wide range from data analysts to engineers
Latest SQL Technology Trends
- In-memory Analytics: High-speed analytical processing with DuckDB and others
- Distributed SQL Engines: Direct querying to data lakes with Trino and Presto
- Real-time Analytics: Second-level aggregation processing with ClickHouse
- Data Transformation Standardization: ELT pipeline construction and testing with dbt
Framework Selection Guidelines
It's important to select appropriate SQL-based frameworks according to use cases. DuckDB is suitable for ad-hoc analysis, Trino for multi-source integrated queries, ClickHouse for real-time analytics, and dbt for building data pipelines. By combining these tools, you can build a modern data stack and establish a foundation that supports data-driven decision making.
GitHub Star Comparison
| No | Name | GitHub Stars | Description | Trend | License | Official Site |
|---|---|---|---|---|---|---|
| 1 | DuckDB | ⭐ 31.3k | High-performance in-memory analytical database. Specialized for OLAP (Online Analytical Processing), combining SQLite's simplicity with PostgreSQL's functionality. Achieves ultra-fast analytical queries through columnar storage and vectorized execution. | Rising star experiencing rapid growth in data science field in 2025. Expanding adoption in Python ecosystem with faster data processing performance than Pandas and Polars. Becoming standard choice for small to medium-scale projects as embedded analytical database. | MIT | Official |
| 2 | Apache Drill | - | Schema-free SQL query engine. Provides unified SQL interface for heterogeneous data sources including JSON, Parquet, CSV, Hadoop, and NoSQL databases. Enables ad-hoc data exploration without schema definition. | Continues as important tool for multi-source data integration in 2025. Demonstrates value in ad-hoc analysis in data lake environments and integration with BI tools. Maintains niche demand as cloud-native data exploration solution. | Apache 2.0 | Official |
| 3 | Trino (Presto) | - | Distributed SQL query engine. Executes high-speed SQL queries across multiple data sources (Hadoop, S3, MySQL, PostgreSQL, etc.). Achieves petabyte-scale data analysis through memory-based parallel processing. | Plays central role in data lakehouse construction in enterprise environments in 2025. Simplified deployment through managed services on AWS, Azure, and GCP. Gaining attention as technology blurring boundaries between real-time analysis and batch processing. | Apache 2.0 | Official |
| 4 | ClickHouse | - | Columnar analytical database management system. Specialized for real-time analytical processing, executing aggregation queries on billions of rows in seconds. Demonstrates particularly high performance in time-series data, log analysis, and web analytics. | Rapid growth as leading choice for real-time analytical database in 2025. Expanding adoption in DevOps monitoring, IoT data analysis, and adtech fields. Increasing adoption by SMEs through cloud-native ClickHouse Cloud. | Apache 2.0 | Official |
| 5 | dbt (data build tool) | - | Data transformation tool for analytics engineers. Builds ELT pipelines using SQL, providing integrated management of data modeling, testing, and documentation generation within data warehouses. Enables data development using Git workflows and CI/CD. | Established as core component of Modern Data Stack in 2025. Rapid adoption expansion alongside the spread of Analytics Engineering profession. Became standard choice for enterprise data infrastructure construction through integration with Snowflake, BigQuery, and Redshift. | Apache 2.0 | Official |