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About Collectors

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• Python 3.6 - 3.10

• Access to the KADA Collector repository that contains the Athena BigQuery 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 Athena BigQuery whl (e.g. kada_collectors_extractors_athenabigquery-#.#.#-py3-none-any.whl)

• Access to K landing directory

• Access to Athena BiqQuery

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Step 1: Establish

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BigQuery Access

The service user/account/role will require permissions to the following

1. Execute queries against Athena with access to the INFORMATION_SCHEMA in particular the following tables:

   1. information_schema.views

   2. information_schema.tables

   3. information_schema.columns

2. Executing queries in Athena requires an s3 bucket to temporary store results.
   The policy must also allow Read Write Listing access to objects within that bucket, conversely, the bucket must also have policy to allow to do the same.

3. Call the following Athena APIs

   1. list_databases

   2. list_table_metadata

   3. list_query_executions

   4. list_work_groups

   5. batch_get_query_executions

   6. start_query_execution

   7. get_query_execution

4. The service user/account/role will need permissions to access all workgroups to be able to extract all data, if you omit workgroups, that information will not be extracted and you may not see the complete picture in K.

5. See IAM policies for accessing workgroups - Amazon Athena on how to add policy entries to have fine grain control at the workgroup level. Note that the extractor runs queries on Athena, If you do choose to restrict workgroup access, ensure that Query based actions (e.g. StartQueryExecution) are allowed for the workgroup the service user/account/role is associated to.

Example Role Policy to allow Athena Access with least privileges for actions, this example allows the ACCOUNT ARN to assume the role. Note the variables ATHENA RESULTS BUCKET NAME. You may also choose to just assign the policy directly to a new user and use that user without assuming roles. In the scenario you do wish to assume a role, please note down the role ARN to be used when onbaording/extracting

• Create a Service Account by going to the Google Cloud Admin or clicking on this link

  • Give the Service Account a name (e.g. KADA BQ Integration)

  • Select the Projects that include the BigQuery instance(s) that you want to catalog

  • Click Save
    

• Create a Service Token

  • Click on the Service Account

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  • Select the Keys tab. Click on Create new key

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  • Select the JSON option. After clicking ‘CREATE’, the JSON file will automatically download to your device. Provide this to the user(s) that will complete the next steps

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• Add permission grants on the Service Account by going to IAM page or clicking on this link

  • Click on ADD

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  • Add the Service Account to the ‘New principals’ field.

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  • Grant the following roles this principal as shown in the following screenshot.

    • BigQuery Job User

    • BigQuery Metadata Viewer

    • BigQuery Read Session User

    • BigQuery Resource Viewer

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    Click SAVE

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Step 2: Create the Source in K

Create a Informatica BigQuery source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File system” option
  

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• Give the source a Name - e.g. Athena BigQuery Production

• Add the Host name for the Athena BigQuery Server

• Click Finish Setup

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Step 3: Getting Access to the Source Landing Directory

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pip install kada_collectors_extractors_athenabigquery-3.0.0-py3-none-any.whl

You will also need to install the common library kada_collectors_lib-1.0.0 for this collector to function properly.

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Step 5: 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.

 Run the following command to install the collector

pip install kada_collectors_extractors_athena-3.0.0-py3-none-any.whl

You will also need to install the common library kada_collectors_lib-1.0.0 for this collector to function properly.

pip install kada_collectors_lib-1.0.0-py3-none-any.whl

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Step 5: Configure the Collector

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

{
    "type": "service_account",
    "project_id": "kada-data",
    "private_key_id": "",
    "private_key": "",
    "client_email": "kada.iam.gserviceaccount.com",
    "client_id": "1234",
    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
    "token_uri": "https://oauth2.googleapis.com/token",
    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
    "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/kada.iam.gserviceaccount.com"
}

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below

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

kada_athenabigquery_extractor_config.json

{
    "keyregions": ""[],
    "secretprojects": "",
    "server": "athena",
    "bucket": "s3://examplebucket/examplefolder",
    "catalogs": ["AwsDataCatalog"],
    "regionhost": "ap-southeast-2",
    "rolejson_credentials": ""{},
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true
}

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Step

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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.

This can be executed in any python environment where the whl has been installed. It will produce and read a high water mark file from the same directory as the execution called athenabigquery_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_athenabigquery_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.athena import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA AthenaBigQuery 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)

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from kada_collectors.extractors.snowflakebigquery 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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class Extractor(keyregions: strlist = None[], secretprojects: strlist = None[], serverhost: str = None, \
      bucket: str = None, catalogs: list = ['AwsDataCatalog']'bigquery', \
      regionjson_credentials: strdict = 'ap-southeast-2', role: str = None, \
      {}, output_path: str = './output', mask: bool = False, \
  compress: bool = False) -> None

key: AWS Access Key.
secret: AWS Secret.
region: Region.
server: Athena host that was onboarded on K.
role: AWS Role ARN if required to assume a role. bucket: s3 bucket used to temporary store results in the form s3://xxx.
catalogs: list of Catalogs from Athena to extract, by default this is just AwsDataCatalog.regions: The list of regions specified by user to extract
projects: The list of projects specified by user to extract
host: The host value onboarded in K
json_credentials: The json credentials for connection to BQ
sql: The list of SQL queries that will be executed by the program
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not

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Step

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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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A high water mark file is created in the same directory as the execution called athenabigquery_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

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Step

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8: Push the Extracts to K

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

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