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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)

• 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.

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You may require an ODBC package for the OS to be installed as well as an oracle client library package if do you not have one already, see https://www.oracle.com/au/database/technologies/instant-client.html

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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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To edit the internal SQL being run refer to https://kadaai.atlassian.net/wiki/spaces/DAT/pages/1894318152/Notes+v2.0.0#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/DAT/pages/1894318152/Notes+v2.0.0#Storing-HWM-in-another-location

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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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