---
id: hyperleaup
version: "0.1.2"
license: unclear
license_treatment: permissive
maintenance: aging
---

# hyperleaup — Create and publish Tableau Hyper files from Apache Spark DataFrames and Spark SQL.

License: permissive · Maintenance: aging · Downloads: 161.0K/mo

## What it is and what it does

Hyperleaup bridges Apache Spark and Tableau by writing Spark DataFrames and SQL query results directly to Tableau Hyper files, then publishing them as datasources to Tableau Server. It eliminates the slow ODBC-based extract pipeline that traditionally bottlenecks Tableau data refresh jobs. The package lets you execute distributed transformations in Spark, apply change-data-capture (CDC) upserts, and schedule repeatable extracts as part of ETL workflows.

The package depends on pyspark, tableauhyperapi, tableauserverclient, requests, and urllib3. It offers four creation modes (PARQUET, COPY, INSERT, LARGEFILE) to trade off memory usage and file size, plus configuration options for timestamp handling, null values, and decimal precision. The project is provided as-is by Databricks Labs without formal SLA support.

Use it for:

- Automate daily Tableau extract refresh by running Spark SQL queries and publishing results directly to Tableau Server without ODBC overhead.
- Implement CDC-based incremental updates to Tableau datasources by appending only new or changed rows from a Spark DataFrame.
- Schedule repeatable extract jobs in a Databricks or Spark cluster environment as the final step of an ETL pipeline.
- Reduce Tableau Server Backgrounder load by offloading heavy data transformations to Spark before writing to Hyper format.
- Create Hyper files from multiple distributed data sources using Spark's read/write capabilities, then publish as a single Tableau datasource.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Generates Tableau Hyper files directly from Apache Spark DataFrames and SQL queries, then publishes them to Tableau Server, bypassing slower ODBC-based extract workflows.

Yes, if you run Spark workloads and need to publish Tableau extracts at scale. The package solves a real performance problem (slow ODBC-based extracts) and integrates cleanly into Spark/Databricks environments. However, maintenance is aging (no releases since September 2023), so verify compatibility with your current versions of pyspark, tableauhyperapi, and Tableau Server before adopting for production. Not suitable if you need active upstream support or are using very recent Tableau versions.

## Install

pip install hyperleaup
uv add hyperleaup
poetry add hyperleaup

## Installing hyperleaup

Before you install: Low install friction with a pure-Python wheel. Maintenance is aging—last release was 2023-09-06 and the repository shows no activity since 2026-01-08—but the package remains archived=false and carries no known vulnerabilities. Suitable for stable use cases that do not require active upstream development.

License in practice: Licensed under Apache License 2.0 (permissive). You may use, modify, and distribute the package freely in commercial and private projects, though you must retain license notices and disclaim warranties.

Quickstart:

pip install hyperleaup

from hyperleaup import HyperFile

hf = HyperFile(name="my_data", sql="SELECT * FROM table", is_dbfs_enabled=True)
hf.publish(tableau_server_url, username, password, site_name, project_name, datasource_name)

Requires Apache Spark and Tableau Server credentials; intended for use in Spark environments (e.g., Databricks, local Spark cluster).

Verify before relying:

- Whether the package works with current versions of pyspark, tableauhyperapi, and tableauserverclient, given the last release was 2023-09-06.
- Performance characteristics and scalability limits for large datasets across different creation modes (PARQUET, COPY, INSERT, LARGEFILE).
- Compatibility with recent Tableau Server versions and whether CDC upsert behavior is production-ready.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 161.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tableau hyper file generation, spark to tableau extract, tableau datasource publishing, hyper file from spark sql, tableau server automation, spark dataframe to hyper, tableau extract pipeline, spark-integration, tableau-automation, data-pipeline

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