How to deploy Datadog into Databricks and also i need logs display in Datadog dashboard.
Datadog has a Spark integration which involves cluster init scripts and environment variables. The setup for Databricks is documented on this page. It logs these metrics and these service checks.
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Is there a way through which we can send our custom spark application logs to ELK ?
I have checked this https://community.databricks.com/s/question/0D53f00001GHVj6CAH/how-to-push-cluster-logs-to-elastic-search
But I couldn't find a way through which we can specify the credentials to send the logs to ELK ?
Also, I have enabled Diagnostic settings on my Azure databricks workspace to write to Elastic search as partner solution. But its picking and writing only Workspace related logs and not custom application logs.
https://learn.microsoft.com/en-us/azure/partner-solutions/elastic/manage
I am looking for a way to create Terraform plans and execute them on a mock Azure environment without provisioning any resources.
Coming from an AWS background I could use something like LocalStack and run my Terraform code upon the API provided by the LocalStack container, without the need to provision any actual resources.
Is there an equivalent for Azure?
I have pushed a container to Container registry and am able to deploy it to kubernetes. Whats the best way to run this container to test if the deployment is working fine?
I have gone through the documentation and have seen that I can setup a endpoint but I am unable to figure out how to call the container once I have setup a post request to the endpoint. Note the container hosts a python script that basically runs a ml model and spits out a prediction. So I would like a way to do api calls to the cluster to run the container and a call to print the results of the container.
Or instead of setting a endpoint are there better ways to accomplish this?
• Setting an endpoint to the Kubernetes pod for accessing the container and executing the python script in the container is a good approach.
• As suggested in the Microsoft documentation, there are three options through which we can deploy API management in front of AKS. You can see the same in the picture provided in the document.
https://learn.microsoft.com/en-us/azure/api-management/api-management-kubernetes#kubernetes-services-and-apis
• Once you configure the API with Kubernetes cluster, you can deploy a model to Azure Kubernetes Service cluster, for that, you need to create a deployment configuration that describes the compute resources needed. For example, the number of cores and memory. You also need an inference configuration, which describes the environment needed to host the model and web service. For more information on creating the inference configuration, see how and where to deploy models.
For more information on how you can deploy and reference a python ML model, you can refer to this document below: -
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where?tabs=azcli
Deploying a multi-container application to Azure Kubernetes Services without using Azure DevOps
We have use case with Java Application (Spring ) with oracle Database as two containers .
We have to try the same in AKS ( Not using Azure DevOps).
Both App (8080) and DB (1521) runs on different Ports
Let me know if you have similar use case implemented.
The point of discussion here might be that whether you want to use a CI/CD Tool other than Azure Devops or not?
If yes, you'll need to setup a pipeline, write some Kubernetes Templates, Build Code, Push Image, and then deploy.
You can always refer Kubernetes Official Docs for more in depth knowledge of Multi-Container Pods, and Jenkins Official Docs for understanding CI/CD Process
Please help me with terraform script to run Azure databricks notebook(python)in other environment.Thank you
You should synchronise Databricks Notebooks via databricks_notebook and scheduling every quartz_cron_expression through databricks_job notebook_task. See example configuration here.
These are the supported developer tools help you develop Azure Databricks applications using the Databricks REST API, Databricks Utilities, Databricks CLI, and tools outside the Azure Databricks environment.
Reference: Azure Databricks - Developer Tools.
Hope this helps.