The blog post reviews several command-line SQL tools for data analysis, focusing on clickhouse-local and textql. Clickhouse-local, developed by ClickHouse, requires installation of the entire ClickHouse binary or its Docker image, and supports a wide range of data formats such as CSV, Parquet, JSON, and Avro. It uses a SQL dialect similar to ANSI SQL and allows for customizable queries through various feature flags, although specifying data types for processing can be time-consuming. On the other hand, textql is a smaller open-source tool that utilizes SQLite-style SQL and is simpler to use, requiring no data type specification for columns, but it only supports CSV and TSV formats. The post highlights the ease of installation of both tools via Docker images and notes that while clickhouse-local offers comprehensive documentation, textql's documentation is limited, which could affect usability. Both tools are benchmarked using the Divvy Bikes dataset to demonstrate their capability in counting daily bike rides for January 2022, with clickhouse-local providing more flexibility for complex data architecture and textql offering a straightforward solution for quick text-file analysis.