Documentation

⌘K
Connectors
Getting started with the Kensu Community Edition
Marketing campaign
Financial data report
Getting credentials
Recipe: Observe Your First Pipeline
Agents: getting started
Python
PySpark
Scala Spark
Databricks Notebook
Agent Listing
Docs powered by archbee 

Installation and configuration

7min

You can install Python locally and the required packages or you can use Docker.



For Mac M1 users, if Pandas is not already installed, we advise you to use Docker. The current release (Spring 2022) of Pandas has some installation problems on Mac M1.

1️⃣ Clone the GitHub repository

Shell
|
git clone https://github.com/kensuio-oss/kensu-public-examples.git
cd kensu-public-examples/Financial_Data_Report




2️⃣ Edit the configuration of the data observability agent

The agent collects information inside the application to send it to Kensu. The agent augments the program library and gathers metadata, defines the lineage, and computes the metrics before sending this to Kensu through the API. To configure the agent, a configuration file is needed.

Edit the /conf.ini file, with the following:

  • kensu_ingestion_url: This is the URL of the API of the Kensu platform, which is already pre-configured for you if you are using the Community edition
  • kensu_ingestion_token: This token will be used in the HTTPS communication with the kensu_ingestion_url in order to add an identity to the traces, logs and metrics sent with your user for easier governance and management
  • kensu_api_token: This is the Kensu API (Personal Access Token) which will allow you to Create a monitoring rule programmatically

If you haven't got your kensu_ingestion_url and kensu_api_token yet, please follow the instructions in Getting Kensu Credentials.

conf.ini
|
[kensu]
;Kensu API endpoint
kensu_ingestion_url=https://community-api.kensuapp.com


;token for Kensu Ingestion API
kensu_ingestion_url=https://community-api.kensuapp.com
;token for Kensu Expectation API (Personal Access Token)
kensu_api_token=


project_name=Financial Report
environment=Staging
user_name=guest
code_location=git:cie/Intraday/Intraday.git
logical_data_source_naming_strategy=File


3️⃣ For Docker Only

If Docker is not installed, please follow these steps.

(The minimum requirements for Docker are 4 CPUs, 4 GB of Memory, 1GB of Swap, 40GB of Disk Image.)

Run this command to build the image:

Shell
|
docker build -t kensu-use-case .




4️⃣ For Python/PySpark Installed Locally

  1. Create a python virtual environment: python3 -m venv kensu_env
  2. Activate the environment: source kensu_env/bin/activate
  3. Install the required Python packages: pip install -r requirements.txt

Go to the Create a monitoring rule in Kensu



Updated 03 Mar 2023
Did this page help you?
Yes
No
PREVIOUS
Financial data report
NEXT
Create a monitoring rule in Kensu
Docs powered by archbee