duckdb_fdw VS parquet_fdw

Compare duckdb_fdw vs parquet_fdw and see what are their differences.

parquet_fdw

Parquet foreign data wrapper for PostgreSQL (by adjust)
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duckdb_fdw parquet_fdw
4 2
235 316
- 7.0%
7.3 2.7
2 months ago 8 days ago
PLpgSQL C++
MIT License PostgreSQL License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

duckdb_fdw

Posts with mentions or reviews of duckdb_fdw. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-30.

parquet_fdw

Posts with mentions or reviews of parquet_fdw. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-03.

What are some alternatives?

When comparing duckdb_fdw and parquet_fdw you can also consider the following projects:

subzero-starter-kit - Starter Kit and tooling for authoring GraphQL/REST API backends with subZero

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

geoparquet - Specification for storing geospatial vector data (point, line, polygon) in Parquet

aquameta - Web development platform built entirely in PostgreSQL

cstore_fdw - Columnar storage extension for Postgres built as a foreign data wrapper. Check out https://github.com/citusdata/citus for a modernized columnar storage implementation built as a table access method.

postgres_vectorization_test - Vectorized executor to speed up PostgreSQL

parquet_s3_fdw - ParquetS3 Foreign Data Wrapper for PostgresSQL

delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs