Awesome GitHub RepositoriesFlat-File SQL Analysis

TextQL is a command line SQL query engine designed to execute relational queries directly against structured text files, such as CSV and TSV, without requiring a database import. It functions as a relational text file analyzer and a CSV processor that treats plain text files as virtual tables for filtering, joining, and aggregating data. The tool is built as a pipe-compatible data transformation utility, allowing it to process data from standard input and output formatted datasets. It enables relational joins across multiple files or directories within a single query to analyze relationships

Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

Briefer is an interactive data notebook platform and business intelligence dashboard tool used for collaborative data analysis and reporting. It provides a containerized environment for building reports that combine SQL, Python, and Markdown with native visualizations. The platform features an integrated code assistant that uses large language models to generate SQL and Python snippets from natural language prompts. It is designed as a Kubernetes data application, deploying via Helm charts to manage isolated compute environments and ensure separate resources per page through pod-based isolati

Franchise is a database query tool and notebook SQL client that allows users to run queries and analyze datasets. It functions as a local data processor with a browser-based engine for executing SQL commands against CSV, JSON, and XLSX files without uploading data to a remote server. The project uses a cell-based interface to organize queries and results in an interactive, document-like layout. It supports a workflow where users can fork queries into side-by-side layouts to compare different SQL variations and their results without overwriting existing code. The system provides a unified int

dsq is a command-line interface and data engine for executing SQL queries against local structured files, such as CSV, JSON, Parquet, and Excel, without requiring a formal database import. It functions as a schema-inference engine that automatically detects data types and maps heterogeneous file structures into relational tables for analysis. The tool utilizes a lazy stream data processor and checksum-based disk caching to handle large datasets with minimal memory usage. It provides a persistent interactive shell for iterative data exploration, allowing users to inspect inferred schemas and r

qsv is a high-performance command line toolkit for querying, transforming, and analyzing comma-separated value files. It functions as a data wrangling interface and a tabular data profiler, featuring a query engine capable of executing SQL statements and joins directly on flat files without requiring a database. The project is distinguished by its ability to process massive datasets that exceed available system memory. This is achieved through disk-based external memory processing, including multithreaded merge sorting, on-disk hash tables for deduplication, and lightweight file indexing for