macrometa-source-snowflake
A macrometa source that extracts data from a Snowflake database and produces JSON-formatted data following the Singer spec.
How to use it
TODO: Add proper context If you want to run this macrometa-source-snowflake connector independently please read further.
Install and Run
First, make sure Python 3 is installed on your system or follow these installation instructions for Mac or Ubuntu. It's recommended to use a virtualenv:
make venv
Configuration
- Create a
config.jsonfile with connection details to snowflake, here is a sample config file. Note:tableis a mandatory parameter as well to avoid a long-running catalog discovery process. Please specify fully qualified table and view names and only that ones that you need to extract otherwise you can end up with very long running discovery mode of this source connector. Discovery mode is analysing table structures but Snowflake doesn't like selecting lot of rows fromINFORMATION_SCHEMAor runningSHOWcommands that returns lot of rows. Please be as specific as possible. - Run it in discovery mode to generate a
properties.json - Edit the
properties.jsonand select the streams to replicate - Run the source connector like any other singer compatible tap:
macrometa-source-snowflake --config config.json --properties properties.json --state state.json
Authentication Methods
You can either use basic user/password authentication or Key Pair authentication.
User / Password authentication
Populate user and password in the config.json file
Key Pair authentication
To use key pair authentication, omit the password and instead provide the private_key to the unencrypted version of the private key and, optionally, the private_key_passphrase.
Discovery mode
The macrometa-source-snowflake connector can be invoked in discovery mode to find the available table and columns in the database:
$ macrometa-source-snowflake --config config.json --discover
A discovered catalog is output, with a JSON-schema description of each table. A source table directly corresponds to a Singer stream.
Replication methods
The two ways to replicate a given table are FULL_TABLE and LOG_BASED.
Full Table
Full-table replication extracts all data from the source table each time the connector is invoked.
LogBased
Macrometa source Snowflake connector can be used as a CDC (Change Data Capture) connector by specifying the Replication Method as LOG_BASED to capture any changes done at source and identify what records were inserted or updated or deleted, it will extract first all the records (i.e. FULL_TABLE) and then it will continuously listen to a Stream created on the Table to extract only the changes done.
To run tests:
- Define environment variables that requires running the tests
export MACROMETA_SOURCE_SNOWFLAKE_ACCOUNT=<snowflake-account-name>
export MACROMETA_SOURCE_SNOWFLAKE_DBNAME=<snowflake-database-name>
export MACROMETA_SOURCE_SNOWFLAKE_USER=<snowflake-user>
export MACROMETA_SOURCE_SNOWFLAKE_PASSWORD=<snowflake-password>
export MACROMETA_SOURCE_SNOWFLAKE_PRIVATE_KEY=<snowflake-pk-path>
export MACROMETA_SOURCE_SNOWFLAKE_PRIVATE_KEY_PASSPHRASE=<snowflake-passphrase>
export MACROMETA_SOURCE_SNOWFLAKE_WAREHOUSE=<snowflake-warehouse>
- Install python dependencies
make venv
- To run unit tests: PS: There are no unit tests at the time of writing this document
make unit_test
- To run Integration tests
make integration_test
To run formatting and linting:
make venv format pylint
License
Apache License Version 2.0 See LICENSE to see the full text.
1 maintainer
Metadata
1 maintainer
Release files for macrometa-source-snowflake 1.0.0
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Release files / macrometa-source-snowflake-1.0.0.tar.gz
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