meltanolabs-target-snowflake
Singer target for Snowflake, built with the Meltano SDK for Singer Targets.
Decision gist · record as of 2026-08-14
Yes. This is an actively maintained, low-friction Singer target for Snowflake with permissive licensing, no known vulnerabilities, and a clear role in ELT pipelines. Install it if you are using Singer taps and need a reliable Snowflake destination; it is the standard choice for this integration pattern.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Snowflake account credentials (user, password or private key, account, database) and a working Singer tap producing valid JSON records.
- Low install friction with a pure-Python wheel.
- Actively maintained with a release within 30 days.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-07-15 (30 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 401,225 downloads/mo, #6,928 on PyPI
Alternatives
Verify before relying
pip install meltanolabs-target-snowflake
# Configure with JSON, then pipe a Singer tap:
tap-some-source | target-snowflake --config config.json
# Or use the CLI directly:
target-snowflake --version- Performance characteristics and throughput limits for batch_size_rows and typical data volumes.
- Whether stream-maps and schema-flattening work with all Singer tap schemas or have known limitations.
- Support status and community activity level beyond the 30-day release cadence.
What it is and what it does
This is a Singer target built on the Meltano SDK that acts as the destination endpoint in a Singer-based ELT pipeline, receiving data from any Singer tap and writing it into Snowflake. It handles the full lifecycle of loading: authentication (password, private key, or OAuth), connection management, schema validation, and data insertion using Snowflake's native capabilities.
The target supports three load strategies—append-only (write all records), upsert (update existing, insert new), and overwrite (replace all)—and can optionally flatten nested JSON schemas and apply stream-level transformations. It includes an interactive CLI for initializing a Snowflake account with the necessary users, roles, warehouses, and databases, and supports configuration via JSON files or environment variables.
Use it for
- Pipe data from a SaaS tap (e.g., Salesforce, Google Analytics) into Snowflake for analytics and reporting.
- Build an ELT pipeline in Meltano that orchestrates multiple taps feeding into Snowflake as a central data warehouse.
- Upsert transactional data into Snowflake to keep a table in sync with an upstream source without full reloads.
- Flatten and transform nested API responses on ingest using stream maps before writing to Snowflake.
- Initialize and configure a new Snowflake environment programmatically with proper users and grants via the CLI.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is an actively maintained, low-friction Singer target for Snowflake with permissive licensing, no known vulnerabilities, and a clear role in ELT pipelines. Install it if you are using Singer taps and need a reliable Snowflake destination; it is the standard choice for this integration pattern.
Install
meltanolabs-target-snowflake on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with a release within 30 days. Requires modern Python (3.10+) and depends on snowflake-connector-python and sqlalchemy, which are standard for Snowflake integrations.
Requires Snowflake account credentials (user, password or private key, account, database) and a working Singer tap producing valid JSON records.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install meltanolabs-target-snowflake
# Configure with JSON, then pipe a Singer tap:
tap-some-source | target-snowflake --config config.json
# Or use the CLI directly:
target-snowflake --versionVerify before relying
- Performance characteristics and throughput limits for batch_size_rows and typical data volumes.
- Whether stream-maps and schema-flattening work with all Singer tap schemas or have known limitations.
- Support status and community activity level beyond the 30-day release cadence.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagescertificryptographysinger-sdksnowflake-connector-pythonsnowflake-sqlalchemysqlalchemy |
| Maintenance | Actively maintained 30 days since the last release |
| First released | |
| Downloads | 401,225 / month, #6,928 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: meltanolabs_target_snowflake-0.18.14-py3-none-any.whl
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See also meltano · singer-sdk · pipelinewise-singer-python · dbt-snowflake · snowflake-ml-python · snowflake-cli · snowflake.core · snowflake-labs-mcp · snowflake · snowpipe-streaming