snowpipe-streaming
Snowflake Streaming Ingest SDK
Decision gist · record as of 2026-08-14
Yes, if you need to stream data at scale. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers prebuilt wheels for common platforms. Medium install friction is acceptable for a compiled Rust extension. Install only if you have streaming requirements; it is not a general-purpose data tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or higher; Snowflake account credentials must be configured; glibc 2.26+ on Linux, macOS 11.0+ on macOS, Windows 10+ on Windows.
- Medium install friction due to compiled Rust bindings; prebuilt wheels available for Linux (x86_64, aarch64), macOS (arm64), and Windows (x86_64) on Python 3.9+.
- Active maintenance with a release 22 days ago.
License · maintenance · safety
MIT or Apache-2.0 (permissive) — Licensed under MIT or Apache-2.0 (permissive); either license allows commercial use, modification, and distribution with minimal restrictions.
last release 2026-07-23 (22 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 153,388 downloads/mo, #10,882 on PyPI
Alternatives
Verify before relying
pip install snowpipe-streaming
from snowpipe_streaming import StreamingIngestClient
client = StreamingIngestClient(
client_name="my_client",
db_name="my_database",
schema_name="my_schema",
pipe_name="my_pipe",
properties={"account": "your_account", "user": "your_user", "private_key": "your_private_key"}
)
channel, status = client.open_channel("my_channel")
channel.append_row({"id": 1, "name": "John Doe"})
channel.close()
client.close()- Throughput and latency benchmarks compared to alternative ingest methods
- Retry behavior and failure recovery specifics beyond 'built-in retry logic'
- Memory footprint under sustained high-volume streaming
- Backpressure handling thresholds and overflow behavior
What it is and what it does
Snowpipe Streaming is a Python SDK for real-time data ingestion built with a Rust core and Python bindings using stable ABI (abi3) for forward compatibility across Python 3.9–3.13. It manages channel lifecycle, row buffering, and communication with streaming infrastructure, handling retries and backpressure automatically. The single runtime dependency is msgspec for fast serialization.
You instantiate a client with credentials and pipe metadata, open a channel, append rows as dictionaries, and close when done. It targets production streaming workloads where latency and throughput matter—typical use is continuous or near-continuous data flow, not one-off bulk loads.
Use it for
- Real-time event streaming from application logs or sensors for live analytics
- Continuous ETL pipelines that ingest data from message queues or APIs
- Live dashboards and monitoring systems requiring sub-second data freshness
- IoT data ingestion where devices or edge systems stream measurements
- Change Data Capture workflows that stream database changes in near-real-time
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to stream data at scale.
The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers prebuilt wheels for common platforms. Medium install friction is acceptable for a compiled Rust extension. Install only if you have streaming requirements; it is not a general-purpose data tool.
Install
snowpipe-streaming on PyPI
Before you install
Medium install friction due to compiled Rust bindings; prebuilt wheels available for Linux (x86_64, aarch64), macOS (arm64), and Windows (x86_64) on Python 3.9+. Active maintenance with a release 22 days ago.
Requires Python 3.9 or higher; Snowflake account credentials must be configured; glibc 2.26+ on Linux, macOS 11.0+ on macOS, Windows 10+ on Windows.
License in practice
Licensed under MIT or Apache-2.0 (permissive); either license allows commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install snowpipe-streaming
from snowpipe_streaming import StreamingIngestClient
client = StreamingIngestClient(
client_name="my_client",
db_name="my_database",
schema_name="my_schema",
pipe_name="my_pipe",
properties={"account": "your_account", "user": "your_user", "private_key": "your_private_key"}
)
channel, status = client.open_channel("my_channel")
channel.append_row({"id": 1, "name": "John Doe"})
channel.close()
client.close()Verify before relying
- Throughput and latency benchmarks compared to alternative ingest methods
- Retry behavior and failure recovery specifics beyond 'built-in retry logic'
- Memory footprint under sustained high-volume streaming
- Backpressure handling thresholds and overflow behavior
Package facts
| License | MIT or Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagemsgspec |
| Maintenance | Actively maintained 22 days since the last release |
| First released | |
| Downloads | 153,388 / month, #10,882 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Programming Language :: RustTopic :: Database |
Evidence: snowpipe_streaming-1.7.0-cp39-abi3-macosx_11_0_arm64.whl; snowpipe_streaming-1.7.0-cp39-abi3-manylinux_2_24_x86_64.whl; snowpipe_streaming-1.7.0-cp39-abi3-manylinux_2_26_aarch64.whl; snowpipe_streaming-1.7.0-cp39-abi3-win_amd64.whl
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