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About Collectors

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Pre-requisites

• Informatica 9.1+ with repository hosted in Oracle.

• Python 3.6 - 3.10

  Access to the KADA Collector repository that contains the Informatica whl

• The repository is currently hosted in KADA’s Azure Blob Storage. You will be given a SAS token to access the repository. Reach out to KADA Support (support@kada.ai) if you do not have access.

Download the Informatica whl (e.g. kada_collectors_extractors_informatica-#.#.#-py3-none-any.whl)

3.6 - 3.10

• Access to K landing directory

• Access to Informatica Repository (see section below)

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Create a user that has read access to the Informatica Server.

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Step 1: Create the Source in K

Create a Informatica source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File” option
  

  

• Give the source a Name - e.g. Informatica Production

• Add the Host name for the Informatica Server

• Click Finish Setup

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Step 2: Getting Access to the Source Landing Directory

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Step 3: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.Run the following command to install the collector

You can download the latest Core Library and whl via Platform Settings → Sources → Download Collectors

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Run the following command to install the collector

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_extractors_informatica-2.0.0-py3-lib-<version>-none-any.whl

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Step 4: Generate runtime mappings

In your environment you maybe using runtime overrides for parameters in your Informatica jobs. KADA uses the runtime overrides to resolve lineage for parameter driven jobs.

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import os
import argparse
from kada_collectors.extractors.utils import load_config, get_generic_logger
from kada_collectors.extractors.informatica import runtime_parser

get_generic_logger('root') # Set to use the root logger, you can change the context accordingly or define your own logger

_type = 'informatica_runtime_parser'
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, 'kada_{}_extractor_config.json'.format(_type))

parser = argparse.ArgumentParser(description='KADA Informatica Runtime Parser.')
parser.add_argument('--config', '-c', dest='config', default=filename, help='Location of the configuration json, default is the config json in the same directory as the script.')
args = parser.parse_args()

config_args = load_config(args.config)

runtime_parser(**{"input_path": config_args["input_path"], "output_path": config_args["output_path"]})

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Step 5: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from Informatica

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{
    "username": "",
    "password": "",
    "dsn": "",
    "repo_owner": "",
    "oracle_client_path": "",
    "cached": false,
    "input_path": "/tmp/input",
    "output_path": "/tmp/output",
    "mask": true
}

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Step 6: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

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If you are handling external arguments of the runner yourself, you’ll need to consider the following for the run method https://kadaai.atlassian.net/wiki/spaces/DATKSL/pages/18943181521902411777/Notes+v2.0.0#TheAdditional+Notes#Extractor-run-method

from kada_collectors.extractors.snowflake import Extractor

kwargs = {my args} # However you choose to construct your args
hwm_kwrgs = {"start_hwm": "end_hwm": } # The hwm values

ext = Extractor(**kwargs)
ext.run(**hwm_kwrgs)

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To edit the internal SQL being run refer to https://kadaai.atlassian.net/wiki/spaces/DATKSL/pages/18943181521902411777/Notes+v2.0.0#AddingAdditional+Notes#Adding-Custom-SQL

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Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

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If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions https://kadaai.atlassian.net/wiki/spaces/DATKSL/pages/18943181521902411777/Notes+v2.0.0#Storing-HWM-in-another-locationAdditional+Notes#Storing-High-Water-Marks-(HWM)

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Step 8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

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Example: Using Airflow to orchestrate the Extract and Push to K

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