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This his collector is for Informatica versions prior to Informatica Intelligent Cloud Services (IICS)

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About Collectors

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	CollectorsCollector MethodCollectors
	Collector Method
name	about

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Pre-requisites

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Python 3.6 - 3.10

Access to the KADA Collector repository

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Download the
Informatica
whl (e.g. kada_collectors_extractors_informatica-#.#.#-py3-none-any.whl)
9.1+ with repository hosted in Oracle.
Python 3.6 - 3.10
Access to K landing directory
Access to Informatica Repository (see section below)
Informatica repository access
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Establish Informatica Repository Access
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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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	CollectorsCollector MethodCollectors
	Collector Method
name	landing

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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.

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

Code Block
pip install kada_collectors_extractors_informatica<version>-none-2.0.0-py3any.whl

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

Code Block
pip install kada_collectors_lib-<version>-none-any.whl

Info
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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Code Block

language	sql

select 
'call infacmd.bat isp getsessionlog -dn DOMAIN<INFORMATICA_PRODUCTIONDOMAIN> -hp <HOST>:<PORT> -un <SERVER USERNAME> -pd <SERVER PASSWORD> -is <SERVERNAME> -rs <REPO NAME> -ru <REPO USERNAME> -rp <REPO PASSWORD> -fm xml -fn ' || ws.subject_area || ' -wf ' || ws.workflow_name || ' -ss ' || CASE WHEN hierarchy_structure is null then ws.instance_name ELSE '"' || substr(hierarchy_structure, 2) || '"' END || ' -lo <C:\\output\\path\\for\\logs\\>' || ws.workflow_id || '_' || ws.task_id || '_' || ws.instance_id as cmd
from (
    SELECT ti.instance_name,
        ti.task_id,
        ti.version_number,
        wws.instance_id,
        wf.workflow_id,
        wf.workflow_name,
        wf.workflow_comments,
        wf.server_name,
        wf.subject_area,
        hierarchy_structure,
        path
FROM (
        select path 
        , TO_NUMBER(substr(path, 2, instr(path,'/',1, 2)-2)) as workflow_id
        , TO_NUMBER(substr(path, -instr(reverse(path),'/', 1, 2)+1, instr(reverse(path),'/', 1, 2)-2)) as task_id
        , hierarchy_structure
        , instance_id
        from (SELECT DISTINCT '/' || temp1.task_id AS path
                , temp1.task_name AS hierarchy_structure
                , 0 as instance_id
                FROM opb_task temp1, opb_subject temp2
                WHERE temp1.subject_id = temp2.subj_id
                AND temp1.task_type = 71 -- workflows
                UNION ALL
                SELECT DISTINCT temp1.path
                    , temp1.task_name AS hierarchy_structure
                    , instance_id
                FROM (SELECT opb_task_inst.workflow_id, opb_task_inst.task_id, opb_task_inst.instance_id, LEVEL depth,
                        SYS_CONNECT_BY_PATH(opb_task_inst.workflow_id ,'/') || '/' || opb_task_inst.task_id || '/' path,
                        SYS_CONNECT_BY_PATH(opb_task_inst.instance_name ,'/') task_name
                        FROM opb_task_inst
                        WHERE opb_task_inst.task_type IN (68,70)
                        START WITH workflow_id IN (select distinct w.workflow_id
                                                        from rep_workflows w
                                                        join rep_task_inst ti on w.workflow_id = ti.workflow_id
                                                        where ti.task_type_name = 'Worklet'
                                                        and w.subject_area not in ('<SUBJECT_AREAS_TO_EXCLUDE>')
                                                    )
                        CONNECT BY PRIOR opb_task_inst.task_id = opb_task_inst.workflow_id
                    ) temp1,
                    opb_task temp2,
                    opb_subject temp3
                WHERE temp2.subject_id = temp3.subj_id
                AND temp2.task_id = SUBSTR(temp1.path,2, INSTR(temp1.path,'/', 1, 2) -2 )
                ORDER BY path ASC )
        where instance_id <> 0
) wws
    JOIN rep_task_inst ti on ti.task_id = wws.task_id and ti.task_type = 68
    JOIN REP_WORKFLOWS wf on wws.workflow_id = wf.workflow_id
UNION
SELECT ti.instance_name,
    ti.task_id,
    ti.version_number,
    ti.instance_id,
    wf.workflow_id,
    wf.workflow_name,
    wf.workflow_comments,
    wf.server_name,
    wf.subject_area,
    '' as hierarchy_structure,
    '' as path
FROM REP_WORKFLOWS wf
    JOIN rep_task_inst ti on ti.workflow_id = wf.workflow_id and ti.task_type = 68
where wf.subject_area not in ('<SUBJECT_AREAS_TO_EXCLUDE>')
) ws
    join (select distinct workflow_id as workflow_id from rep_wflow_run) active_wflows on ws.workflow_id = active_wflows.workflow_id

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Note
The session logs can take a long time to generate. We recommended that you run this step on an adhoc frequency when your Informatica jobs change.

These logs can then be parsed with the Use kada_informatica_runtime_parser.py to generate a runtime_session_overrides.json which is will be used when running by the Informatica extractor.

kada_informatica_runtime_parser.py

Code Block

language	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"]})

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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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Code Block

language	json

{
    "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

Code Block

language	py

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

Insert excerpt

	CollectorsCollector MethodCollectors
	Collector Method
name	airflow

Versions Compared

Old Version 2

New Version Current

Key

About Collectors

Pre-requisites

Step 1: Create the Source in K

Step 2: Getting Access to the Source Landing Directory

Step 3: Install the Collector

Step 4: Generate runtime mappings

Step 5: Configure the Collector

Step 6: Run the Collector

Step 7: Check the Collector Outputs

Step 8: Push the Extracts to K

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