This is second part of the series on developing and deploying Azure Functions 2.0 where I will
Create a function triggered by Azure Cosmos DB
Create Azure SignalR Service bindings for Azure Functions 2.0
Publish Docker Image to Docker Hub
Create Function App from Docker Image in Azure Portal
Deploy functions to Azure Kubernetes Service from VS Code
The first part of the series provides details on creating functions triggered by Azure Blob storage and Event hub in Visual Studio Code along with deploying Azure Functions to Azure Kubernetes Service (AKS).
Dev tools used to develop these components are Visual Studio Code for macOS and Docker. The complete source code for this article can be downloaded from GitHub.
This is third article on the series on deploying Elasticsearch, Logstash and Kibana (ELK) in Azure Kubernetes Service (AKS) cluster. The first article covered deploying non-SSL ELK to AKS and consuming messages from Azure Event Hub. The second article described how to secure communications in ELK and use Azure AD SAML based SSO for Kibana and Elasticsearch. In this article I am going to share steps needed to ingest Azure Redis Cache messages into Elasticsearch using Logstash’s Redis plugin.
Azure Redis Cache is based on the popular open-source Redis cache. It is typically used as a cache to improve the performance and scalability of systems that rely heavily on backend data-stores. Logstash’s Redis plugin will read events from Redis instance. I will create a Logstash event processing pipeline where I will define Redis as input and Elasticsearch as output. The component diagram has been updated to add Azure Redis Cache integration.
The dev tools used to develop these components are Visual Studio for Mac/VS Code, AKS Dashboard, kubectl, bash and openssl. The code snippets in this article are mostly yaml snippets and are included for reference only as formatting may get distorted thus please refer to GitHub repository for formatted resources.
This is second part of the series on deploying Elasticsearch, Logstash and Kibana (ELK) to Azure Kubernetes Service cluster. In this article I am going to share steps needed to enable Azure AD SAML based single sign on to secure Elasticsearch and Kibana hosted in AKS. I will also go through steps needed to secure communications in ELK cluster. The first part describes steps needed to deploy ELK to AKS and consume messages from Azure Event Hub
Using SAML SSO for Elasticsearch with AAD means that Elasticsearch does not need to be seeded with any user accounts from the directory. Instead, Elasticsearch is able to rely on the claims sent within a SAML token in response to successful authentication to determine identity and privileges. I have referred to this article to enable SAML based single sign on for Elasticsearch.
Kibana, as the user facing component, interacts with the user’s browser and receives all the SAML messages that the Azure AD sends to the Elastic Stack Service Provider. Elasticsearch implements most of the functionality a SAML Service Provider needs. It holds all SAML related configuration in the form of an authentication realm and it also generates all SAML messages required and passes them to Kibana to be relayed to the user’s browser. It finally consumes all SAML Responses that Kibana relays to it, verifies them, extracts the necessary authentication information and creates the internal authentication tokens based on that. The component diagram has been updated to add Azure AD SAML based SSO integration.
The dev tools used to develop these components are Visual Studio for Mac/VS Code, AKS Dashboard, kubectl, bash and openssl. The code snippets in this article are mostly yaml snippets and are included for reference only as formatting may get distorted thus please refer to GitHub repository for formatted resources.
Azure Functions is a serverless compute service that enables you to run code on-demand without having to explicitly provision or manage infrastructure. You can read about Azure Functions 2.0 general availability @ Introducing Azure Functions 2.0. Runtime 2.0 runs on .NET Core 2, which means it can run on all platforms supported by .NET Core, including macOS and Linux. This enables cross-platform development and hosting scenarios.
In this article I am going to
Create Azure Functions triggered by Azure Blob storage and Event hub in Visual Studio Code
Debug locally in Visual Studio Code
Deploy Azure Functions to Azure Kubernetes Service
Dev tools used to develop these components are Visual Studio Code for macOS, Docker, AKS Dashboard and kubectl.
This article is part of the series of blogs on Azure Kubernetes Service (AKS). In this article I am going to share my experience of setting up Elasticsearch, Logstash and Kibana cluster in Azure Kubernetes Service (AKS) and consume messages from Event Hub. After the end of this article, we are going to have fully functional ELK stack with Azure Event Hub integration.
A sample client App (e.g. IOT device) will be publishing messages to Event Hub and these messages will be ingested into Elasticsearch using ‘Azure Event Hub’ plugin of Logstash. This article needs x-pack features of Elasticsearch thus I will show steps needed to activate trial license.
The second part of this series goes through steps needed to enable Azure AD SAML based single sign on to secure Elasticsearch and Kibana hosted in AKS. The third part of this series goes through steps needed to ingest Azure Redis Cache messages into Elasticsearch using Logstash’s Redis plugin.
The dev tools used to develop these components are Visual Studio for Mac/Visual Studio 2017, AKS Dashboard as well as kubectl commands are used to create/manager Kubernetes resources in AKS.
