Hevo (via Collector method) - v3.0.0
About Collectors
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 https://api-docs.hevodata.com/reference/building-your-first-api
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
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 |
---|---|---|---|
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
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
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
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