{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/3"}],"enrichment":{"capability":"A Python client for streaming data ingestion into Databricks Delta tables via the Zerobus service, supporting both JSON and Protocol Buffer serialization with synchronous and asynchronous APIs.","skillfed_tags":["databricks-integration","streaming-ingestion","rust-backed"],"use_cases":["Stream sensor or IoT telemetry data into Delta tables for real-time analytics.","Ingest application logs or events from multiple sources into a centralized Delta table.","Build a data pipeline that continuously appends records to Delta with automatic acknowledgment and recovery.","Prototype quick data ingestion workflows using JSON before migrating to Protocol Buffers for production throughput.","Integrate Zerobus ingestion into async Python applications without blocking."],"what_it_does":"The Databricks Zerobus Ingest SDK is a thin Python wrapper around a native Rust implementation (via PyO3 bindings) for streaming records into Databricks Delta tables. It provides both synchronous and asynchronous APIs, handles OAuth 2.0 authentication automatically, and supports two serialization formats: JSON (simpler, higher per-record overhead) and Protocol Buffers (compact, type-safe, recommended for production). The SDK manages stream lifecycle, automatic recovery on transient failures, and acknowledgment tracking\u2014you ingest records in a loop and call flush() to confirm durability, or register callbacks to be notified as records commit.\n\nCore ingestion (gRPC, OAuth, stream management) runs in the compiled Rust layer, delivering native performance. The package requires Python 3.9\u20133.14 and depends on protobuf and requests at runtime. Optional Arrow Flight ingestion is available via the [arrow] extra, which installs pyarrow with version selection for your Python version. The SDK is marked Production/Stable and released actively.","worth_installing":"Yes, if you are ingesting data into Databricks Delta tables and want a native-performance Python client with built-in recovery and acknowledgment tracking. The medium install friction (compiled wheels) is typical for performance-critical packages. License treatment is unclear\u2014verify the actual license before use in proprietary or restricted contexts. No known vulnerabilities. Active maintenance and broad Python version support (3.9\u20133.14) make it production-ready."},"id":"databricks-zerobus-ingest-sdk","links":{"html":"https://skillfed.io/packages/databricks-zerobus-ingest-sdk","md":"https://skillfed.io/packages/databricks-zerobus-ingest-sdk.md","pypi":"https://pypi.org/project/databricks-zerobus-ingest-sdk/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"unclear","name":"databricks-zerobus-ingest-sdk","python_support":"supports_current","summary":"Databricks Zerobus Ingest SDK for Python"},"popularity":{"monthly_downloads":330586,"position":7534,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.1"}