Cognos (Collector method) - v3.0.0

About Collectors

Collectors are extractors that are developed and managed by you (A customer of K).

KADA provides python libraries that customers can use to quickly deploy a Collector.

Why you should use a Collector

There are several reasons why you may use a collector vs the direct connect extractor:

1. You are using the KADA SaaS offering and it cannot connect to your sources due to firewall restrictions

2. You want to push metadata to KADA rather than allow it pull data for Security reasons

3. You want to inspect the metadata before pushing it to K

Using a collector requires you to manage

1. Deploying and orchestrating the extract code

2. Managing a high water mark so the extract only pull the latest metadata

3. Storing and pushing the extracts to your K instance.

Pre-requisites

Collector Server Minimum Requirements

Integration with Cognos requires Cognos Analytics APIs.

Cognos Analytics APIs are available from version 11.1.7 onwards.

Previous versions are currently not supported.

Cognos Requirements

• Cognos access

  • Cognos Analytics user that has the ability to read all objects in Cognos

  • A SQL Authenticated user Database User for the underlying Audit Database configured for Cognos

• Cognos auditing must be enabled (Log level - Basic)

  • See https://www.ibm.com/support/pages/how-configure-audit-reporting-cognos-analytics

Collector currently only supports a SQLServer version 2016 or higher Audit Database, if you use another Database type, please contact KADA support.

Step 1) Setup KADA user configuration in Cognos

This step is performed by a Cognos Admin.

• Log into your Cognos instance.

  • Note down the URL you use e.g. https://kada-cognos.cloudapp.net/ to be used in Step 3

• Create a new KADA user.

  • Follow the steps here - https://www.ibm.com/docs/en/cognos-analytics/11.2.0?topic=namespace-creating-managing-users

  • Add the user to a role that has read access to objects to be profiled/monitored.

  • To enable K to monitor ALL objects, the user will need read access to ALL Cognos objects.

  • Note down the Namespace ID for the namespace where the user was created.

Step 2) Setup KADA user in the Cognos Audit Database

• Log into your Cognos Audit Database e.g SQL Server

• Create a new KADA database user

• Give the KADA database user READ ONLY access to the following tables in the Audit Database:

  • COGIPF_VIEWREPORT

  • COGIPF_USERLOGON

  • COGIPF_RUNREPORT

  • COGIPF_RUNJOB

Step 3: Create the Source in K

Create a Cognos source in K

• Log into your K instance

• Go to Platform Settings, select Sources and click Add Source

• Select Cognos

• Select "Load from File" option

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

• Add the Host name - use the cognos URL from Step 1

• Click Finish Setup

Step 4: Getting Access to the Source Landing Directory

Step 5: Install the Collector

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

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_lib-<version>-none-any.whl

Note that you will also need an ODBC package installed at the OS level for pyodbc to use as well as a SQLServer ODBC driver, refer to https://docs.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver15

Step 6: Configure the Collector

kada_cognos_extractor_config.json

{
    "server_url": "http://xxx:9300",
    "username": "",
    "password": "",
    "namespace": "",
    "timeout": 20,
    "db_host": "",
    "db_username": "",
    "db_password": "",
    "db_port": 8060,
    "db_name": "",
    "db_schema": "",
    "db_use_kerberos": false,
    "meta_only": false,
    "output_path": "/tmp/output",
    "mask": false,
    "mapping": {},
    "compress": false
}

Step 7: Run the Collector

This is the wrapper script: kada_cognos_extractor.py

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

get_generic_logger('root')

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

parser = argparse.ArgumentParser(description='KADA Cognos Extractor.')
parser.add_argument('--config', '-c', dest='config', default=filename)
parser.add_argument('--name', '-n', dest='name', default=_type)
args = parser.parse_args()

start_hwm, end_hwm = get_hwm(args.name)

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

publish_hwm(args.name, end_hwm)

Step 8: Check the Collector Outputs

K Extracts

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

High Water Mark File

A high water mark file is created called cognos_hwm.txt.

Step 9: Push the Extracts to K

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

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