tap-hellobaton

tap-hellobaton is a Singer tap for hellobaton.

Built with the Meltano Tap SDK for Singer Taps.

Installation

Install directly from the repo source using pip

pip install git+https://github.com/dluftspring/tap-hellobaton.git

For developers

Install the dev dependencies using poetry

~$ poetry install

Then make sure the tests are passing locally

~$ poetry run pytest

Configuration

Accepted Config Options

A full list of supported settings and capabilities for this tap is available by running:

tap-hellobaton --about

You'll need to supply three config parameters

Parameter	Description	Type	Env Variable Alias
company	Your company instance used in the api url	string (required)	TAP_HELLOBATON_COMPANY
api_key	Api key for authentication	string (required)	TAP_HELLOBATON_API_KEY
user_agent	User agent to appear in monitoring jobs	string (optional)	TAP_HELLOBATON_USER_AGENT

You can set these parameters as environment variables or by specifying a json configuration file with the following info

  {
    "company": "<YOUR COMPANY>",
    "api_key": "<YOUR API KEY>",
    "user_agent": "<USER AGENT STRING>"
  }

Usage

You can easily run tap-hellobaton by itself or in a pipeline using Meltano.

Executing the Tap Directly

To see a catalog of the available json schemas you can run the tap in discovery mode

tap-hellobaton --version
tap-hellobaton --help
tap-hellobaton --config CONFIG_FILE_PATH --discover > ./catalog.json

To execute the tap directly from the cli

tap-hellobaton --config CONFIG_FILE_PATH

Running ELT with Meltano

Note: This tap will work in any Singer environment and does not require Meltano. Examples here are for convenience and to streamline end-to-end orchestration scenarios.

First install Meltano (if you haven't already) and any needed plugins. It's recommended that you do this in a virtual environment

# Install meltano
pip install meltano
# Initialize meltano within this directory
meltano init 

This will create a new meltano project in your working directory. You can edit the meltano.yml file with the following minimal configurations

version: 1
send_anonymous_usage_stats: true
project_id: c817018d-de7b-45fb-9fff-260fa84ddaf9
plugins:
  extractors:
  - name: tap-hellobaton
    namespace: tap_hellobaton
    pip_url: git+https://github.com/dluftspring/tap-hellobaton.git
    executable: tap-hellobaton
    capabilities:
    - catalog
    - state
    - discover
    settings:
    - name: company
      kind: string
    - name: api_key
      kind: password
    - name: user_agent
      kind: string
    config:
      company: YOUR_COMPANY
      user_agent: Singer Tap for hellobaton
    load_schema: YOUR_LOAD_SCHEMA

You can now install the tap with meltano using

meltano install

Lastly, you should add the api key to your configuration

meltano config tap-hellobaton set api_key YOUR_API_KEY
#now we can check if all the configs are properly set

meltano config tap-hellobaton list
company [env: TAP_HELLOBATON_COMPANY] current value: 'YOUR COMPANY' (from `meltano.yml`)
api_key [env: TAP_HELLOBATON_API_KEY] current value: 'YOUR API KEY' (from `.env`)
user_agent [env: TAP_HELLOBATON_USER_AGENT] current value: 'YOUR USER AGENT' (from `meltano.yml`)

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano invoke tap-hellobaton --version
# OR run a test `elt` pipeline:
meltano elt tap-hellobaton target-jsonl
# Google bigquery example
meltano elt tap-hellobaton target-bigquery

Note that to use the target-jsonl with a new meltano project you'll have to add the configuration to your meltano.yml project file and install the plugin. NOTE: make sure your destination path exists in the project directory or the command will throw an error

- name: target-jsonl
    variant: andyh1203
    pip_url: target-jsonl
    config:
      destination_path: load/jsonl_target_tests

To use the target-bigquery with a new meltano project you'll have to add the configuration to your meltano.yml project file and install the plugin. Follow the instructions to configure the bigquery target.

# For bigquery
loaders:
- name: target-bigquery
  variant: adswerve
  pip_url: git+https://github.com/adswerve/[email protected]
  config:
    project_id: {your gcp project}
    dataset_id: {your gcp dataset id}
    validate_records: true
  settings:
  - name: project_id
    kind: string
  - name: dataset_id
    kind: password
  - name: location
    kind: string
  - name: credentials_path
    kind: string
  - name: add_metadata_columns
    kind: boolean