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About Collectors

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

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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.

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Pre-requisites

• Python 3.6 - 3.10

• Support SQL SSRS 2016+ where the database is called ReportServer$RS

  • if your SSRS databases differs from this, please Advise KADA of the SSRS version and what the database is called.

  • The collector will need access to the underlying SQLServer Database with permissions to read the following tables:

    • ReportServer$RS.DBO.CATALOG

    • ReportServer$RS.DBO.EXECUTIONLOG3

    • ReportServer$RS.DBO.USERS

• Access to K landing directory

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

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The following connection types are NOT currently supported:

1. Teradata IP Reference Only Data Source

2. SAP NetWeaver Data Source

3. XML Data Source

4. Web Service Data Source

5. XML Document Data Source

6. Sharepoint Data Source

The following catalog item types are currently NOT supported:

1. Linked Reports

2. Files

3. Power BI Desktop Files

4. Report Models

Parameter resolution is not supported.

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Some TSQL syntax is not support. These are mostly statements that contain not standard ANSI SQL constructs. Examples include:

1. Variables (DECLARE)

2. Flow control (IF BEGIN .. )

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Step 1: Create the Source in K

Create a SSRS 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. SSRS Production

• Add the Host name for the SSRS Server

• Click Finish Setup

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Step 2: Getting Access to the Source Landing Directory

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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.

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Step 4: Configure the Collector

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

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{
    "server": "",
    "username": "",
    "password": "",
    "ssrs_database": "",
    "mapping": {},
    "driver": "ODBC Driver 17 for SQL Server",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true
}

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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.

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This code will produce and read a high water mark file from the same directory as the execution called ssrs_hwm.txt and produce files according to the configuration JSON.

Advanced Usage

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.

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username: username to sign into sqlserver
password: password to sign into sqlserver
ssrs_database: Name of the SSRS database of the sqlserver host
server: sqlserver host
driver: sqlserver driver name
mapping: Dict of DNS to database and hostnames
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 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

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

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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)

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

# 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 configed 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.
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

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