pacman82/odbc2parquet — A command line tool to query an ODBC data source and write the result into a parquet file.

Description and use

**Brief Summary**
pacman82/odbc2parquet is a Rust‑based CLI that connects to any ODBC‑compatible data source, runs a query, and streams the result directly into a Parquet file. It is aimed at teams that need a quick, scriptable way to export relational data for analytics, data‑lake ingestion, or automated reporting pipelines.

**Value**

• **Fast, lossless export** – Parquet’s columnar format dramatically reduces storage costs and speeds up downstream analytics compared to raw CSV or JSON dumps.
• **Zero‑code integration** – By leveraging ODBC, the tool works with virtually any commercial or open‑source database (SQL Server, PostgreSQL, Oracle, etc.) without writing custom adapters.
• **Pipeline‑friendly** – The CLI can be chained in shell scripts or CI/CD jobs, making it easy to feed fresh snapshots into ETL workflows, data‑science notebooks, or BI tools that natively consume Parquet.

**Practical Adoption Path**

1. **Prototype** – Install the binary (or build from source) on a dev machine, configure an ODBC DSN, and run a simple query to verify the Parquet output matches expectations.
2. **Validate schema & performance** – Compare the generated Parquet schema with the source table, and benchmark export speed for representative data volumes.
3. **Wrap in a script** – Encode the ODBC connection string, query, and target file path in a shell or Python script; add error handling and logging.
4. **Integrate with orchestration** – Schedule the script via Airflow, Prefect, or a cron job, and point downstream jobs (e.g., Spark, DuckDB, or AWS Athena) at the produced Parquet files.

**Production Readiness**

• **Maturity**: Medium. The project has modest community traction (≈250 ★, 21 forks) and was updated recently (May 2026), indicating active maintenance but limited enterprise‑grade testing.
• **Dependencies**: Relies on the system ODBC driver stack and the Rust runtime; these must be version‑pinned and validated in your environment.
• **Risks**: Integration details (e.g., handling of complex data types, authentication mechanisms, and error reporting) are not fully documented, so a short proof‑of‑concept phase is advisable.
• **Recommendation**: Suitable for internal prototypes, data‑lake ingestion jobs, or reporting pipelines where the conversion step is isolated. For mission‑critical production workloads, perform thorough testing, lock dependency versions, and consider adding a thin wrapper that validates the Parquet file before downstream consumption.

Problem

pacman82/odbc2parquet helps convert raw data into searchable, analyzable, or automated pipelines.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• organize analytics pipelines
• process datasets
• improve reporting workflows

Evidence

A command line tool to query an ODBC data source and write the result into a parquet file.

Source