turbodbc VS pyexasol

Compare turbodbc vs pyexasol and see what are their differences.

turbodbc

Turbodbc is a Python module to access relational databases via the Open Database Connectivity (ODBC) interface. The module complies with the Python Database API Specification 2.0. (by blue-yonder)

pyexasol

Exasol Python driver with low overhead, fast HTTP transport and compression (by exasol)
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turbodbc pyexasol
2 1
603 73
0.0% -
8.0 3.2
3 days ago 3 days ago
C++ Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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turbodbc

Posts with mentions or reviews of turbodbc. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-16.
  • Arrowdantic 0.1.0 released
    3 projects | /r/Python | 16 Apr 2022
    It supports reading from and writing to ODBC compliant databases at likely similar performance as turbodbc and it does not require conda to install.
  • arrow-odbc: Fetch arrow arrays from an ODBC data source in a pip installable environment
    2 projects | /r/Python | 23 Nov 2021
    turbodbc is great, but a pain to build, at least without conda. arrow-odbc-py uses cffi (rather than PyO3) to talk to a rust backend and than uses the Arrow C Data interface to provide the user with pyarrow compatible arrow arrays. The use of a dedicated C interface in both places, avoids linking directly against the Python C-Interpreter as well as the specific C++ Arrow libraries your pyarrow version depends on. Avoiding some pain of dependency hell.

pyexasol

Posts with mentions or reviews of pyexasol. We have used some of these posts to build our list of alternatives and similar projects.
  • How to do streaming(?) architecture and live product pricing?
    1 project | /r/Database | 16 Feb 2021
    Sorry for the delay. I think starting simple would be easiest. Then as you learn you’ll figure out what the next set of questions to ask are. Using something like python and pandas would be good to get the data, then you’ll want to write that data to a database. Then you’ll want to visualize the data with maybe python again using matplot. Or a visualization tool like PowerBI. Exasol has a free analytical db which is easy to get up and running easy to get up and running with virtualbox on your local machine. here is a pyexasol library for writing from pandas to Exasol (line 24) you can do this in Jupiter notebook

What are some alternatives?

When comparing turbodbc and pyexasol you can also consider the following projects:

arrow-odbc-py - Read Apache Arrow batches from ODBC data sources in Python

psycopg - New generation PostgreSQL database adapter for the Python programming language

soci - Official repository of the SOCI - The C++ Database Access Library

python-slack-sdk - Slack Developer Kit for Python

vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.

Dota2EsportDataset - This project is about making a Dota 2 Esport Dataset

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

vertica-python - Official native Python client for the Vertica Analytics Database.

arrow2 - Transmute-free Rust library to work with the Arrow format

odbc - Connect to ODBC databases (using the DBI interface)

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

pymport - Use Python libraries from Node.js