6 Repos
Applying SQL queries to analyze structured text files without database imports.
Distinguishing note: Existing candidates focus on SQL scripts or performance analysis rather than analyzing raw text files using SQL
Explore 6 awesome GitHub repositories matching data & databases · Flat-File SQL Analysis. Refine with filters or upvote what's useful.
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
Allows running SQL queries on CSV or TSV files to filter and aggregate data without importing it into a database.
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
Applies SQL queries to analyze structured profiling data imported from pprof files.
Briefer ist eine interaktive Daten-Notebook-Plattform und ein Business-Intelligence-Dashboard-Tool, das für kollaborative Datenanalyse und Berichterstattung verwendet wird. Es bietet eine containerisierte Umgebung zum Erstellen von Berichten, die SQL, Python und Markdown mit nativen Visualisierungen kombinieren. Die Plattform verfügt über einen integrierten Code-Assistenten, der Large Language Models verwendet, um SQL- und Python-Snippets aus natürlichsprachlichen Prompts zu generieren. Sie ist als Kubernetes-Datenanwendung konzipiert und wird über Helm-Charts bereitgestellt, um isolierte Rechenumgebungen zu verwalten und separate Ressourcen pro Seite durch Pod-basierte Isolierung sicherzustellen. Das System deckt ein breites Spektrum an Funktionen ab, einschließlich externer Datenbankkonnektivität, Echtzeit-Co-Editing und automatisierter Berichtszustellung via Scheduling. Es integriert sich mit OpenID Connect für die Identitätsbereitstellung und bietet rollenbasierte Zugriffskontrolle, sicheres Credential-Management und ergebnisbasiertes Query-Caching. Die Anwendung wird über Kubernetes-Cluster mittels verwalteter Helm-Charts bereitgestellt und skaliert.
Allows importing local files to be analyzed using SQL queries without needing a formal database import.
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
Applies SQL queries to analyze structured local files like CSV, JSON, and XLSX without requiring database imports.
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
Executes SQL queries against CSV, JSON, and Parquet files without importing them into a formal database engine.
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
Executes complex SQL queries and joins directly on flat CSV files without requiring a database engine.