Hevo (via Collector method) - v3.0.0

About Collectors

Collector Method

Pre-requisites

Collector Server Minimum Requirements

Hevo Requirements

Access to Hevo

Step 1: Create Hevo API key/Secret

This step is performed by the Hevo Admin. Hevo documentation for creating an API Key is here Making your First API Request

Login to Hevo
Click on your Avatar in the top right hand corner and select Account in the drop down menu
Select API Keys in the side panel and click Generate a New API Key.
Copy the Access Key and Secret Key

Step 2: Create the Source in K

Go to Settings, Select Sources and click Add Source
Select Hevo as the Source Type
Select “Load from File system” option
Give the source a Name - e.g. Hevo Production
Add the Host name for the Hevo Server
Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

Collector Method

Step 4: 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

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

Step 5: Configure the Collector

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

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE

FIELD	FIELD TYPE	DESCRIPTION	EXAMPLE
api_key	string	API Key for Hevo
api_secret	string	Secret for the API Key
region	string	Region prefix as per https://docs.hevodata.com/getting-started/creating-your-hevo-account/regions/	au
output_path	string	Absolute path to the output location where files are to be written	“/tmp/output”
mask	boolean	To enable masking or not	true
timeout	integer	Timeout in seconds allowed against the Fivetran APIs	20
mapping	JSON	Mapping file of data source names against the onboarded host and database name in K	Assuming I have a “myDSN” data source name in powerbi, I’ll map it to host “myhost” and database “mydatabase” onboarded in K, snowflake type references are handled automatically `{ "myDSN": { "host": "myhost", "database": "mydatabase" } }`
compress	boolean	To gzip the output or not	true

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file.

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

kada_hevo_extractor_config.json

{
    "api_key": "",
    "api_secret": "",
    "region": "",
    "output_path": "/tmp/output",
    "mask": true,
    "timeout": 20,
    "mapping": {
        "myDSN": {
            "host": "myhost",
            "database": "mydatabase"
        }
    },
    "compress": true
}

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.

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 hevo_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_hevo_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.hevo import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Hevo 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.')
aparser.add_argument('--name', '-n', dest='name', default=_type, help='Name of the collector instance.')
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(_type, end_hwm)

Advance options:

If you wish to maintain your own high water mark files elsewhere 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. Refer to this document for more information Collector Integration General Notes | Storing HWM in another location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information Collector Integration General Notes | The run method

from kada_collectors.extractors.hevo 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)

class Extractor(api_key: str = None, api_secret: str = None, region: str = None, \
  mapping: dict = {}, timeout: int = 10, output_path: str = './output', \
  mask: bool = False, compress: bool = False)

api_key: The API Key for the registered application for access to Hevo APIs
api_secret: The API secret for the registered application for access to Hevo APIs
region: The region prefix for your Hevo
mapping: Dict of DNS to database and hostnames
timeout: Timeout for the API call
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

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

High Water Mark File

A high water mark file is created in the same directory as the execution called hevo_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 Collector Integration General Notes | Storing High Water Marks (HWM)

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)

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

Collector Method