{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/4"}],"enrichment":{"capability":"Generates Tableau Hyper files directly from Apache Spark DataFrames and SQL queries, then publishes them to Tableau Server, bypassing slower ODBC-based extract workflows.","skillfed_tags":["spark-integration","tableau-automation","data-pipeline"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"hyperleaup","links":{"html":"https://skillfed.io/packages/hyperleaup","md":"https://skillfed.io/packages/hyperleaup.md","pypi":"https://pypi.org/project/hyperleaup/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2023-09-06","license_spdx":null,"license_treatment":"permissive","name":"hyperleaup","python_support":"supports_current","summary":"Create and publish Tableau Hyper files from Apache Spark DataFrames and Spark SQL."},"popularity":{"monthly_downloads":161022,"position":10645,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.2"}