Informatica (via Collector method)

About Collectors

Pre-requisites

• Python 3.6 - 3.10

• Access to the KADA Collector repository

  • 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 Informatica Repository (see section below)

Informatica repository access

Establish Informatica Repository Access

Create an Oracle user with read access to all tables in the Informatica repository database.

Establish Informatica Server Access

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

Generate runtime mappings

Use the script informatica_generate_infacmd_args.sql to generate infacmd commands to extract session logs in XML format.

The commands can be be combined in a bat script like the example below to dump out the latest log per session.

@echo off
cd /d C:
cd "C:\Informatica\9.1.0\clients\DeveloperClient\infacmd"
echo %cd%
<ADD CALLS from SQL here>
pause

These logs can then be parsed with the kada-collector parse_runtime_logs to generate a runtime_session_overrides.json. This file is used when running the Informatica extractor

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

Step 2: Getting Access to the Source Landing Directory

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

pip install kada_collectors_extractors_informatica-2.0.0-py3-none-any.whl

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

Step 4: Configure the Collector

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

KADA provides an out of the box script that reads a configuration JSON file and runs the extractor. Below is the configuration file.

kada_informatica_extractor_config.json

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

Step 5: 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.

This can be executed in any python environment where the whl has been installed.

This code sample uses the kada_informatica_extractor.py for handling the configuration details

import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.informatica import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Informatica Extractor.')
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()

start_hwm, end_hwm = get_hwm(_type)

ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

publish_hwm(_type, end_hwm)

There is also a wrapper script to parse runtime logs

kada_informatica_runtime_parser.py

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"]})

This is used to produce a session.json file which is used in the input folder for the extractor.

from kada_collectors.extractors.informatica import runtime_parser

kwargs = {my args} # However you choose to construct your args

runtime_parser(**kwargs)

Advance options:

If you wish to maintain your own high water mark files else where you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format.

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/DAT/pages/1894318152/Notes+v2.0.0#The-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)

class Extractor(username: str = None, password: str = None, dsn: str = None, \
    repo_owner: str = None, oracle_client_path: str = None, \
    cached: bool = False, input_path: str = './input', \
    output_path: str = './output', mask: bool = False) -> None

username: username to sign into server
password: password to sign into server
dsn: server address
repo_owner: Oracle table owner
oracle_client_path: library path for the Oracle Instant Client
cached: Set to prevent re-extracting data
input_path: full or relative path to the directory containing the input files
output_path: full or relative path to where the outputs should go

The runtime parser can also be called in isolation

from kada_collectors.extractors.informatica import runtime_parser

kwargs = {my args} # However you choose to construct your args

runtime_parser(**kwargs)

def runtime_parser: (input_path: str = './input', output_path: str = './output') -> None

input_path: full or relative path to the directory containing the input files
output_path: full or relative path to where the outputs should go

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

Step 6: 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

High Water Mark File

A high water mark file is created in the same directory as the execution called informatica_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

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

Step 7: 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)

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