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To better serve Wise business and customer needs, the PipelineWise codebase needs to shrink. We have made the difficult decision that, going forward many components of PipelineWise will be removed or incorporated in the main repo. The last version before this decision is v0.64.1

We thank all in the open-source community, that over the past 6 years, have helped to make PipelineWise a robust product for heterogeneous replication of many many Terabytes, daily

pipelinewise-target-s3-csv

Singer target that uploads loads data to S3 in CSV format following the Singer spec.

This is a PipelineWise compatible target connector.

How to use it

The recommended method of running this target is to use it from PipelineWise. When running it from PipelineWise you don't need to configure this tap with JSON files and most of things are automated. Please check the related documentation at Target S3 CSV

If you want to run this Singer Target independently please read further.

Install

First, make sure Python >=3.7 is installed on your system or follow these installation instructions for Mac or Ubuntu.

It's recommended to use a virtualenv:

  python3 -m venv venv
  pip install pipelinewise-target-s3-csv

  make venv

To run

Like any other target that's following the singer specification:

some-singer-tap | target-s3-csv --config [config.json]

It's reading incoming messages from STDIN and using the properties in config.json to upload data into Postgres.

Note: To avoid version conflicts run tap and targets in separate virtual environments.

Configuration settings

Running the target connector requires a config.json file. An example with the minimal settings:

{
  "s3_bucket": "my_bucket"
}

Profile based authentication

Profile based authentication used by default using the default profile. To use another profile set aws_profile parameter in config.json or set the AWS_PROFILE environment variable.

Non-Profile based authentication

For non-profile based authentication set aws_access_key_id , aws_secret_access_key and optionally the aws_session_token parameter in the config.json. Alternatively you can define them out of config.json by setting AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_SESSION_TOKEN environment variables.

Full list of options in config.json:

Property	Type	Required?	Description
aws_access_key_id	String	No	S3 Access Key Id. If not provided, `AWS_ACCESS_KEY_ID` environment variable will be used.
aws_secret_access_key	String	No	S3 Secret Access Key. If not provided, `AWS_SECRET_ACCESS_KEY` environment variable will be used.
aws_session_token	String	No	AWS Session token. If not provided, `AWS_SESSION_TOKEN` environment variable will be used.
aws_endpoint_url	String	No	AWS endpoint URL.
aws_profile	String	No	AWS profile name for profile based authentication. If not provided, `AWS_PROFILE` environment variable will be used.
s3_bucket	String	Yes	S3 Bucket name
s3_key_prefix	String		(Default: None) A static prefix before the generated S3 key names. Using prefixes you can
delimiter	String		(Default: ',') A one-character string used to separate fields.
quotechar	String		(Default: '"') A one-character string used to quote fields containing special characters, such as the delimiter or quotechar, or which contain new-line characters.
add_metadata_columns	Boolean		(Default: False) Metadata columns add extra row level information about data ingestions, (i.e. when was the row read in source, when was inserted or deleted in snowflake etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix `_SDC_`. The column names are following the stitch naming conventions documented at https://www.stitchdata.com/docs/data-structure/integration-schemas#sdc-columns. Enabling metadata columns will flag the deleted rows by setting the `_SDC_DELETED_AT` metadata column. Without the `add_metadata_columns` option the deleted rows from singer taps will not be recongisable in Snowflake.
encryption_type	String	No	(Default: 'none') The type of encryption to use. Current supported options are: 'none' and 'KMS'.
encryption_key	String	No	A reference to the encryption key to use for data encryption. For KMS encryption, this should be the name of the KMS encryption key ID (e.g. '1234abcd-1234-1234-1234-1234abcd1234'). This field is ignored if 'encryption_type' is none or blank.
compression	String	No	The type of compression to apply before uploading. Supported options are `none` (default) and `gzip`. For gzipped files, the file extension will automatically be changed to `.csv.gz` for all files.
naming_convention	String	No	(Default: None) Custom naming convention of the s3 key. Replaces tokens `date`, `stream`, and `timestamp` with the appropriate values. <br><br>Supports "folders" in s3 keys e.g. `folder/folder2/{stream}/export_date={date}/{timestamp}.csv`. <br><br>Honors the `s3_key_prefix`, if set, by prepending the "filename". E.g. naming_convention = `folder1/my_file.csv` and s3_key_prefix = `prefix_` results in `folder1/prefix_my_file.csv`
temp_dir	String		(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.

To run tests:

Define environment variables that requires running the tests

  export TARGET_S3_CSV_ACCESS_KEY_ID=<s3-access-key-id>
  export TARGET_S3_CSV_SECRET_ACCESS_KEY=<s3-secret-access-key>
  export TARGET_S3_CSV_BUCKET=<s3-bucket>
  export TARGET_S3_CSV_KEY_PREFIX=<s3-key-prefix>

Install python test dependencies in a virtual env and run unit and integration tests

    make venv

To run unit tests:

  make unit_test

To run integration tests:

  make integration_test

To run pylint:

Install python dependencies and run python linter

    make venv pylint

License

Apache License Version 2.0

See LICENSE to see the full text.