Breadcrumbs

Glue (via 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 to 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 pulls the latest metadata

  3. Storing and pushing the extracts to your K instance

Pre-requisites

Collector Server Minimum Requirements

For the collector to operate effectively, it will need to be deployed on a server with the below minimum specifications:

  • CPU: 2 vCPU

  • Memory: 8GB

  • Storage: 30GB (depends on historical data extracted)

  • OS: unix distro e.g. RHEL preferred but can also work with Windows Server

  • Python 3.10.x or later

  • Access to K landing directory

Glue Requirements

  • Access to Glue

Step 1: Establish Glue Access

It is advised you create a new Role for the service user provided to KADA and have a policy that allows the below, see Identity and access management in Glue - Amazon Glue

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

  1. Ability to GET and LIST s3 resources that you want the user to have access to if required.

  2. Call the following Glue APIs

    1. get_tables

    2. get_databases

Example Role Policy to allow Glue Access with least privileges for actions. Note the YOUR-REGION and AWS-ACCOUNT-ID.

Note this is a Cloudformation Template and is a YAML not JSON file

YAML
AWSTemplateFormatVersion: "2010-09-09"
Description: 'AWS IAM Role - Glue Access to KADA'
Resources: 
  KadaGlueRole: 
    Type: "AWS::IAM::Role"
    Properties: 
      RoleName: "KadaGlueRole"
      MaxSessionDuration: 43200
      Path: "/"
      AssumeRolePolicyDocument: 
        Version: "2012-10-17"
        Statement: 
        - Effect: "Allow"
          Principal:
            AWS: "[ACCOUNT ARN]"
          Action: "sts:AssumeRole"

  KadaGluePolicy: 
    Type: 'AWS::IAM::Policy'
    Properties:
      PolicyName: root
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Action: 
              - glue:GetTables
              - glue:GetDatabases
            Resource:
              - 'arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:catalog'
              - 'arn:aws:glue:your-region:your-account-id:database/*'
              - 'arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:table/*/*'
      Roles:
        - !Ref KadaGlueRole

Alternatively you may wish to just create the Policy using this example JSON

JSON
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "KadaGluePolicy",
            "Effect": "Allow",
            "Action": [
                "glue:GetDatabases",
                "glue:GetTables"
            ],
            "Resource": [
                "arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:catalog",
                "arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:database/*",
                "arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:table/*/*"
            ]
        }
    ]
}

Step 1 Optional: Creating Glue Crawlers over S3

See https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html for more details.

You may also wish to set up a crawler over your s3 data to ingest into Glue.

  1. The crawler will need an IAM role with the direct policies attached

    1. AWSGlueServiceRole

    2. AWSS3FullAccessRole

  2. Create a Database in the Glue Console

    1. In the left navigation pane, choose "Databases"

    2. Click "Add database" and provide a name for the database

  3. Create a s3 Crawler

    1. In the left navigation pane, choose "Crawlers"

    2. Click "Add crawler" and provide a name for the crawler

    3. Choose "Data stores" and select "S3" as the data store type

    4. Specify the S3 path to the bucket you want to crawl

    5. Choose "Next" and select the IAM role you created earlier

    6. Choose "Next" and select the Database created in Step 2.

    7. Configure other settings like frequency etc.

    8. Choose "Next" to review settings then "Finish"

  4. Run the Crawler

    1. Select the Crawler you created and click "Run Crawler" and wait for completion

Step 2: Create the Source in K

Create an Athena source in K

  • Go to Settings, Select Sources and click Add Source

  • Select "Load from File system" option

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

  • Add the Host name for the Athena Server, recommended to use the convention [AWS ACCOUNT ID]_glue e.g. 3255667_glue

  • Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

[

When using a Collector you will push metadata to a K landing directory.

To find your landing directory you will need to:

  1. Go to Platform Settings - Settings. Note down the value of this setting:

    • If using Azure: storage_azure_storage_account

    • If using AWS:

      • storage_root_folder - the AWS s3 bucket

      • storage_aws_region - the region where the AWS s3 bucket is hosted

  2. Go to Sources - Edit the Source you have configured. Note down the landing directory in the About this Source section.

To connect to the landing directory you will need:

  • If using Azure: a SAS token to push data to the landing directory. Request this from KADA Support (support@kada.ai)

  • If using AWS:

    • An Access key and Secret. Request this from KADA Support (support@kada.ai)

    • OR provide your IAM role to KADA Support to provision access.

Step 4: Install the Collector

You can download the Latest Core Library and Athena 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

Under the covers this uses boto3 and may have OS dependencies see https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html

Step 5: Configure the Collector

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

key

string

Key for the AWS user

"xcvsdsdfsdf"

secret

string

Secret for the AWS user

"sgsdfdsfg"

server

string

This is the host that was onboarded in K for Glue

"43234234_glue"

regions

string

A list of regions in which you have Glue set up

["ap-southeast-2"]

catalogId

string

This is generally your AWS Account Id

"43234234"

role

string

If your access requires role assumption, place the full arn value here

""

output_path

string

Absolute path to the output location

"/tmp/output"

mask

boolean

To enable masking or not

true

compress

boolean

To gzip the output or not

true

kada_glue_extractor_config.json

{
    "key": "",
    "secret": "",
    "server": "43234234_glue",
    "regions": ["ap-southeast-2"],
    "catalogId": "43234234",
    "role": "",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": true
}

Step 6: Run the Collector

This is the wrapper script: kada_glue_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.glue import Extractor

get_generic_logger('root')

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

parser = argparse.ArgumentParser(description='KADA Glue 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 7: 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 glue_hwm.txt.

Refer to Collector Integration General Notes for more information.

Step 8: 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

The following example is how you can orchestrate the Tableau collector using Airflow and push the files to K hosted on Azure. The code is not expected to be used as-is but as a template for your own DAG.

Python
# built-in
import os

# Installed
from airflow.operators.python_operator import PythonOperator
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup

from plugins.utils.azure_blob_storage import AzureBlobStorage

from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.tableau import Extractor

# To be configured by the customer.
# Note variables may change if using a different object store.
KADA_SAS_TOKEN = os.getenv("KADA_SAS_TOKEN")
KADA_CONTAINER = ""
KADA_STORAGE_ACCOUNT = ""
KADA_LANDING_PATH = "lz/tableau/landing"
KADA_EXTRACTOR_CONFIG = {
    "server_address": "http://tabserver",
    "username": "user",
    "password": "password",
    "sites": [],
    "db_host": "tabserver",
    "db_username": "repo_user",
    "db_password": "repo_password",
    "db_port": 8060,
    "db_name": "workgroup",
    "meta_only": False,
    "retries": 5,
    "dry_run": False,
    "output_path": "/set/to/output/path",
    "mask": True,
    "mapping": {}
}

# To be implemented by the customer.
# Upload to your landing zone storage.
# Change '.csv' to '.csv.gz' if you set compress = true in the config
def upload():
  output = KADA_EXTRACTOR_CONFIG['output_path']
  for filename in os.listdir(output):
      if filename.endswith('.csv'):
        file_to_upload_path = os.path.join(output, filename)

        AzureBlobStorage.upload_file_sas_token(
            client=KADA_SAS_TOKEN,
            storage_account=KADA_STORAGE_ACCOUNT,
            container=KADA_CONTAINER,
            blob=f'{KADA_LANDING_PATH}/{filename}',
            local_path=file_to_upload_path
        )

with DAG(dag_id="taskgroup_example", start_date=days_ago(1)) as dag:

    # To be implemented by the customer.
    # Retrieve the timestamp from the prior run
    start_hwm = 'YYYY-MM-DD HH:mm:SS'
    end_hwm = 'YYYY-MM-DD HH:mm:SS' # timestamp now

    ext = Extractor(**KADA_EXTRACTOR_CONFIG)

    start = DummyOperator(task_id="start")

    with TaskGroup("taskgroup_1", tooltip="extract tableau and upload") as extract_upload:
        task_1 = PythonOperator(
            task_id="extract_tableau",
            python_callable=ext.run,
            op_kwargs={"start_hwm": start_hwm, "end_hwm": end_hwm},
            provide_context=True,
        )

        task_2 = PythonOperator(
            task_id="upload_extracts",
            python_callable=upload,
            op_kwargs={},
            provide_context=True,
        )

        # To be implemented by the customer.
        # Timestamp needs to be saved for next run
        task_3 = DummyOperator(task_id='save_hwm')

    end = DummyOperator(task_id='end')

    start >> extract_upload >> end