apache-airflow-providers-exasol
Provider package apache-airflow-providers-exasol for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Airflow and need to integrate Exasol into your data pipelines. The package is actively maintained, has no known vulnerabilities, installs with low friction, and carries a permissive Apache license. Install only if you have Airflow >=2.11.0 already running and an Exasol cluster to connect to.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and an Exasol cluster with network connectivity from your Airflow environment.
- Low friction install as a wheel.
- Maintained actively with a recent release.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 license is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-08-08 (6 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 316,586 downloads/mo, #7,674 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-exasol
from airflow.providers.exasol import operators
from airflow import DAG
with DAG('exasol_example') as dag:
task = operators.ExasolOperator(
task_id='query_exasol',
exasol_conn_id='exasol_default'
)- Specific operators and hooks provided beyond basic SQL execution
- Whether sqlalchemy extra is required for typical workflows or optional
- Data transfer performance characteristics or volume limits
- Example DAG code and typical usage patterns
What it is and what it does
This is an Apache Airflow provider package that adds Exasol database support to Airflow workflows. It bridges Airflow's task orchestration with Exasol's distributed SQL engine, allowing you to define, schedule, and monitor data operations against Exasol clusters as part of larger data pipelines.
The package depends on apache-airflow, pyexasol for native Exasol connectivity, pandas for data manipulation, and common Airflow provider libraries for SQL and compatibility abstractions. It supports Python 3.10, 3.11, 3.12, 3.13, and 3.14, and requires Airflow >=2.11.0. An optional sqlalchemy extra is available for additional SQL toolkit integration.
Use it for
- Schedule and run SQL queries against Exasol in Airflow DAGs as part of ETL workflows
- Load data from external sources into Exasol tables on a schedule
- Extract data from Exasol and pass it downstream to other Airflow tasks
- Monitor Exasol query execution and data pipeline health within Airflow's UI
- Orchestrate multi-step analytics workloads that include Exasol transformations
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Airflow and need to integrate Exasol into your data pipelines.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and carries a permissive Apache license. Install only if you have Airflow >=2.11.0 already running and an Exasol cluster to connect to.
Install
apache-airflow-providers-exasol on PyPI
Before you install
Low friction install as a wheel. Maintained actively with a recent release. Requires Apache Airflow >=2.11.0 and depends on pyexasol, pandas, and common Airflow provider libraries.
Requires Apache Airflow >=2.11.0 and an Exasol cluster with network connectivity from your Airflow environment.
License in practice
Apache-2.0 license is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install apache-airflow-providers-exasol
from airflow.providers.exasol import operators
from airflow import DAG
with DAG('exasol_example') as dag:
task = operators.ExasolOperator(
task_id='query_exasol',
exasol_conn_id='exasol_default'
)Verify before relying
- Specific operators and hooks provided beyond basic SQL execution
- Whether sqlalchemy extra is required for typical workflows or optional
- Data transfer performance characteristics or volume limits
- Example DAG code and typical usage patterns
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesapache-airflowapache-airflow-providers-common-compatapache-airflow-providers-common-sqlpyexasolpandas |
| Maintenance | Actively maintained 6 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 316,586 / month, #7,674 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_exasol-4.10.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “airflow exasol provider”
- apache-airflow-providers-exasolIntegrates Exasol database with Apache Airflow, providing operators…
- apache-airflow-providers-common-ioProvides common I/O utilities and operators for Apache Airflow…
- apache-airflow-providers-sqliteIntegrates SQLite databases with Apache Airflow as a provider…
Give your agent the search over MCP, or paste the wish link into any chat.
More Monitoring packages
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
Provides generated Python code for OpenTelemetry semantic conventions, enabling standardized attribute naming and constant definitions for instrumentation and telemetry collection.
Install it if you are using OpenTelemetry and want to follow semantic conventions correctly.
Provides the reference implementation of the OpenTelemetry API for collecting and exporting traces, metrics, and logs from Python applications.
Provides the abstract API and interfaces for OpenTelemetry instrumentation in Python, defining how to emit traces, metrics, and logs without tying code to a specific SDK implementation.
Exports OpenTelemetry observability data to an OpenTelemetry Collector using Protobuf-encoded messages over HTTP.
Install it if you are using OpenTelemetry in Python and need to send data to a Collector over HTTP.
Provides automatic instrumentation commands and programmatic APIs to inject distributed tracing into Python applications without code changes, detecting and instrumenting packages used by your program.
Install it if you need distributed tracing without code changes and have compatible instrumented packages in your environment.
See also apache-airflow-providers-salesforce · apache-airflow-providers-presto · apache-airflow-providers-apache-impala · apache-airflow-providers-microsoft-mssql · apache-airflow-providers-apache-hive · pyexasol · apache-airflow-providers-oracle · apache-airflow-providers-common-sql · apache-airflow-providers-trino · exasol-integration-test-docker-environment