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databricks-zerobus-ingest-sdk

Databricks Zerobus Ingest SDK for Python

With conditionsPyPI DatabaseReleased Aug 2026330.6K downloads / moPlatform wheel

Decision gist · record as of 2026-08-14

platform wheels — databricks_zerobus_ingest_sdk-1.6.1-cp39-abi3-manylinux_2_34_aarch64.whl · databricks_zerobus_ingest_sdk-1.6.1-cp39-abi3-manylinux_2_34_x86_64.whl · databricks_zerobus_ingest_sdk-1.6.1-cp39-abi3-win_amd64.whl
v1.6.1 · released 2026-08-13 · Python <3.15,>=3.9 · 2 runtime deps: protobuf, requests

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—verify the actual license before use in proprietary or restricted contexts. No known vulnerabilities. Active maintenance and broad Python version support (3.9–3.14) make it production-ready.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Requires Python 3.9–3.14; free-threaded builds (3.14t) are not supported.
  • Workspace setup, table creation, and service principal credentials required.
  • Medium install friction due to compiled wheels (abi3 bindings); active maintenance with release 1 day old.

License · maintenance · safety

(unclear)

last release 2026-08-13 (1 days)

0 known vulnerabilities (OSV.dev, 2026-08-14) · 330,586 downloads/mo, #7,534 on PyPI

Verify before relying

pip install databricks-zerobus-ingest-sdk

from zerobus.sdk.sync import ZerobusSdk
from zerobus.sdk.shared import RecordType, StreamConfigurationOptions, TableProperties

sdk = ZerobusSdk(server_endpoint, workspace_url)
table_properties = TableProperties("main.default.air_quality")
options = StreamConfigurationOptions(record_type=RecordType.JSON)
stream = sdk.create_stream(client_id, client_secret, table_properties, options)
stream.ingest_record_offset({"device_name": "sensor-1", "temp": 20})
stream.flush()
stream.close()
  • Whether the package's license is actually unspecified or simply not declared in PyPI metadata.
  • Whether free-threaded Python builds (e.g., 3.14t) will be supported in future releases.
  • Performance characteristics and throughput benchmarks compared to alternative ingestion methods.
Same gist for agents: .md · .json

What it is and 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—you ingest records in a loop and call flush() to confirm durability, or register callbacks to be notified as records commit.

Core ingestion (gRPC, OAuth, stream management) runs in the compiled Rust layer, delivering native performance. The package requires Python 3.9–3.14 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.

Use it for

  • 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.

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

With conditions

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—verify the actual license before use in proprietary or restricted contexts. No known vulnerabilities. Active maintenance and broad Python version support (3.9–3.14) make it production-ready.

Install

databricks-zerobus-ingest-sdk on PyPI

Before you install

Medium install friction due to compiled wheels (abi3 bindings); active maintenance with release 1 day old. Supports Python 3.9–3.14 on Linux (x86_64, aarch64), macOS, and Windows. Two lightweight runtime dependencies (protobuf, requests).

Requires Python 3.9–3.14; free-threaded builds (3.14t) are not supported. Workspace setup, table creation, and service principal credentials required.

Quickstart

pip install databricks-zerobus-ingest-sdk

from zerobus.sdk.sync import ZerobusSdk
from zerobus.sdk.shared import RecordType, StreamConfigurationOptions, TableProperties

sdk = ZerobusSdk(server_endpoint, workspace_url)
table_properties = TableProperties("main.default.air_quality")
options = StreamConfigurationOptions(record_type=RecordType.JSON)
stream = sdk.create_stream(client_id, client_secret, table_properties, options)
stream.ingest_record_offset({"device_name": "sensor-1", "temp": 20})
stream.flush()
stream.close()

Verify before relying

  • Whether the package's license is actually unspecified or simply not declared in PyPI metadata.
  • Whether free-threaded Python builds (e.g., 3.14t) will be supported in future releases.
  • Performance characteristics and throughput benchmarks compared to alternative ingestion methods.

Package facts

LicenseNot declared unclear
Python supportSupports the current Python release <3.15,>=3.9
Install frictionMedium. Platform-specific wheel
Runtime dependencies
2 packages
protobufrequests
MaintenanceActively maintained 1 days since the last release
First released
Downloads330,586 / month, #7,534 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9

Evidence: databricks_zerobus_ingest_sdk-1.6.1-cp39-abi3-manylinux_2_34_aarch64.whl; databricks_zerobus_ingest_sdk-1.6.1-cp39-abi3-manylinux_2_34_x86_64.whl; databricks_zerobus_ingest_sdk-1.6.1-cp39-abi3-win_amd64.whl

Tags

Capabilities
databricks streaming ingestiondelta table data streamingzerobus python clienthigh-performance data ingestdatabricks sdk pythonstream to delta tablesprotobuf json ingestion
Topics
databricks-integrationstreaming-ingestionrust-backed
PyPI keywords
zerobusdatabrickssdk

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See also snowpipe-streaming · azure-kusto-ingest · dbt-databricks · databricks-ai-search · databricks-sql-connector · databricks-dlt · ingestr · openmetadata-ingestion · proto-google-cloud-datastore-v1 · deltalite