Import Parquet to Postgresql Automatically
Looking to import data from Parquet to Postgresql?
Advanced ETL Processor Enterprise provides everything you need to automate the data import without writing custom scripts. Visually map and transform your fields, then run the Import immediately or schedule it to repeat automatically.
Parquet is a column oriented file format designed for analytics workloads and modern data processing systems. It is widely used in data lakes and big data environments because it provides excellent compression and high performance query processing.
- Column based storage improves analytical query performance
- High compression reduces storage requirements
- Optimized for large datasets commonly used in data lakes
- Efficient data scanning reads only required columns
- Supported by modern analytics tools including Spark, Python, and cloud data platforms
Because of these advantages, many modern data pipelines generate Parquet files as an intermediate or export format. These datasets often need to be loaded into relational databases for operational use.
Watch Advanced ETL Processor In Action
Watch a general data-import walkthrough showing how to connect databases, map fields, and run an import in Advanced ETL Processor.
About PostgreSQL Database
PostgreSQL is a powerful open source relational database known for its reliability, flexibility, and advanced features. It is widely used in enterprise applications, analytics platforms, and cloud services.
- Highly reliable and stable database engine
- Advanced SQL features and extensibility
- Excellent performance for complex queries
- Open source and widely supported
- Popular for web applications and data platforms
For organizations using PostgreSQL, importing Parquet data allows analytics datasets to be integrated directly into operational systems, reporting platforms, and internal applications.
Useful links for this import
Start with the Advanced ETL Processor Enterprise overview, then download the fully functional 30-day trial. The import-data hub lists the other Parquet import workflows.
FAQ
Can Advanced ETL Processor import Parquet to Postgresql?
Yes. Advanced ETL Processor can read Parquet, map fields, validate data, write to Postgresql, and log the import.
Do I need to write scripts for the import?
No scripting is required for the normal import workflow. You can configure the reader, writer, mapping, validation, and schedule visually.
Can the Parquet import run on a schedule?
Yes. The package can run on a schedule, process matching Parquet files, archive originals, and write rows to Postgresql with the same validation rules each time.
Can imported data be transformed before loading?
Yes. You can clean values, convert data types, calculate fields, split columns, and apply lookup rules before writing to the target.
Can bad rows be logged or rejected?
Yes. Add validation rules so rejected rows, failed files, row counts, and error details are visible after each run.
What should I check before the first production import?
Check source layout, target table, key fields, data types, date formats, write mode, archive folder, and failure handling.
When should I not automate the import yet?
Do not automate it until the source layout, target table, key fields, and bad-row handling are clear. Automation repeats rules; it does not invent them.
Can I test the import before buying?
Yes. Download the fully functional 30-day trial, build one small import, and test it with a deliberately awkward sample file.
Automate Your Parquet-to-Postgresql Imports
Download the fully functional 30-day trial and build your first import using your own Parquet and Postgresql databases.
Direct download. No registration required.