My Kafka

My Kafka projects

Project 1

Built a Java Spring Boot and Kafka application for a bank client.

• Defined business requirements in collaboration with product owners to ensure alignment with project goals.
• Designed the application architecture, focusing on key criteria including data source identification, data model categorization, Kafka topic schema design, partition strategy, retention policy selection, replica factor definition, and scalability planning.
• Set up the Kafka cluster using Confluent Cloud to ensure reliable and scalable data streaming infrastructure.
• Developed microservices with Java Spring Boot and implemented Kafka producers and consumers to facilitate seamless data processing.
• Conducted comprehensive testing of microservices using Postman and curl to validate functionality and performance.
• Deployed microservices to Google Cloud Platform using Docker and Kubernetes, ensuring a robust and scalable deployment pipeline.
• Leveraged Kafka's capabilities to handle real-time data streaming and processing, enhancing the bank's data management and processing capabilities.

Info

What is Kafka

Install Kafka On Windows

Kafka for Microservice

How to add Kafka in AWS or Azure or GCP?

You can add Kafka to AWS, Azure, or GCP using several options depending on whether you want to manage the Kafka cluster yourself, use a managed service, or integrate with a Confluent Cloud offering. Below is a guide on how to deploy Kafka in each of the major cloud platforms:

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1. Deploying Kafka on AWS:

Option 1: Use Amazon MSK (Managed Streaming for Apache Kafka)

1. Create an MSK Cluster:

   • Go to the Amazon MSK Console.
   • Click Create Cluster.
   • Choose a Cluster Name and set broker configurations (e.g., instance types, number of brokers).
   • Configure the networking settings (VPC, subnets, security groups).
   • Define storage settings and monitoring options.
   • Launch the cluster and wait until it’s in an Active state.

2. Connect to the MSK Cluster:

   • Use a client such as Kafka Console Producer/Consumer to connect.
   • Use the bootstrap servers provided in the MSK configuration to send and receive messages.

3. Add Authentication & Authorization:

   • Use IAM or SASL/SCRAM for Kafka client authentication.
   • Set up Kafka ACLs for topic-level authorization.

Option 2: Set Up Kafka on EC2 Instances

1. Launch EC2 instances with the desired instance type and operating system.
2. Install Kafka on each EC2 instance manually or using configuration management tools like Ansible or Chef.
3. Configure Kafka brokers, Zookeeper nodes, and network settings.
4. Deploy Kafka Manager or another monitoring tool to manage the cluster.

Option 3: Use Confluent Cloud

1. Create a Confluent Cloud Account.
2. Deploy a Kafka Cluster in the desired AWS region.
3. Integrate with AWS services such as Lambda, S3, or Redshift for data processing.

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2. Deploying Kafka on Azure:

Option 1: Use Azure Event Hubs for Kafka (Managed Kafka Service)

1. Create an Azure Event Hub Namespace:

   • Go to the Azure Portal and create an Event Hub Namespace.
   • Enable the Kafka protocol in the namespace settings.

2. Create an Event Hub (Equivalent to a Kafka Topic):

   • Inside the namespace, create an Event Hub for Kafka producers and consumers.

3. Connect Kafka Applications:

   • Use the Kafka-compatible endpoint provided by Azure.
   • Point your Kafka clients to the Event Hub namespace with the Kafka protocol enabled.

Option 2: Set Up Kafka on Azure Virtual Machines (VMs)

1. Create Virtual Machines for each Kafka broker and Zookeeper node.
2. Install Kafka and Zookeeper on the VMs.
3. Configure Kafka broker properties, Zookeeper nodes, and networking.
4. Use Azure Load Balancer for scaling and Azure Disk Storage for data storage.

Option 3: Use Confluent Cloud on Azure

1. Create a Confluent Cloud Account.
2. Choose Azure as the cloud provider and deploy a Kafka cluster in the desired Azure region.
3. Integrate Kafka with Azure services like Azure Functions or Azure Blob Storage.

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3. Deploying Kafka on Google Cloud Platform (GCP):

Option 1: Use Confluent Cloud for Kafka

1. Create a Confluent Cloud Account.
2. Choose Google Cloud as the cloud provider and select a GCP region.
3. Launch a Kafka cluster.
4. Configure client credentials and connect Kafka producers and consumers.

Option 2: Set Up Kafka on GCP Compute Engine

1. Launch GCP Compute Engine instances for each Kafka broker and Zookeeper node.
2. Install Kafka and Zookeeper on each instance.
3. Configure Kafka broker properties and Zookeeper nodes.
4. Use GCP internal load balancer for high availability and persistent disks for storage.

Option 3: Use Google Cloud Pub/Sub as a Kafka Alternative

1. Create a Google Cloud Pub/Sub topic, which serves as an alternative to Kafka topics.
2. Use Kafka Connect or Google Cloud connectors to integrate existing Kafka applications with Pub/Sub.
3. Use Google Cloud Dataflow for real-time stream processing.

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Key Considerations:

1. Security: Implement the appropriate security measures such as SSL/TLS encryption, SASL authentication, or IAM permissions.
2. Monitoring & Management: Use native cloud monitoring solutions like CloudWatch (AWS), Azure Monitor, or Stackdriver (GCP) for real-time Kafka metrics.
3. Integration with Cloud Services:
   • On AWS: Integrate with Lambda, Kinesis, and S3.
   • On Azure: Integrate with Azure Functions, Event Grid, and Blob Storage.
   • On GCP: Integrate with Dataflow, BigQuery, and Cloud Functions.

Recommendation:

For ease of use and managed services, consider using Amazon MSK, Azure Event Hubs for Kafka, or Confluent Cloud. If you need more control, deploying on VMs or compute instances in each cloud is an alternative.

I choose Kafka because it is free. Otherwise I will use Confluent Cloud or built-in message solutions from AWS/Auzre/GCP. So basically to save my cost, I just use the vm in AWS/Auzre/GCP?

Yes, using Kafka on VMs in AWS, Azure, or GCP is a cost-effective approach if you want to leverage Kafka’s capabilities without the expenses of managed services like Confluent Cloud, Amazon MSK, or Azure Event Hubs. Setting up Kafka on VMs allows you to avoid licensing and subscription fees, giving you more control over the infrastructure. Here’s how you can optimize this setup for cost savings:

Why Deploy Kafka on VMs:

1. No Licensing Costs: Kafka is an open-source tool, and deploying it on cloud VMs eliminates the need for licensing or subscription costs.
2. Greater Control: You have full control over the configurations, tuning, and scaling, which means you can optimize resource usage.
3. Custom Deployments: You can tailor the number of brokers, Zookeeper nodes, and configurations based on the workload and scale dynamically to minimize costs.

Cost-Saving Tips for VM-Based Kafka Deployments:

1. Choose the Right Instance Types:

   • Select VM sizes that match your workload. For development and small-scale use, start with lower-cost instances (e.g., t3.medium in AWS or B-series burstable VMs in Azure).
   • For production, consider using compute-optimized or storage-optimized instances.

2. Use Auto-Scaling and Spot Instances:

   • Enable auto-scaling for brokers based on CPU and memory usage to optimize costs.
   • In AWS, use Spot Instances or Reserved Instances for cost savings.
   • In Azure, consider Azure Spot VMs, and in GCP, use Preemptible VMs for non-critical workloads.

3. Leverage Storage Efficiently:

   • Use network-attached storage (e.g., EBS volumes in AWS, Managed Disks in Azure, or Persistent Disks in GCP) for Kafka logs.
   • Configure retention policies carefully to avoid excessive storage costs.

4. Optimize Networking Costs:

   • Minimize data transfer costs by deploying Kafka and clients in the same region or availability zone.
   • For inter-region communication, consider setting up VPC peering or using internal load balancers.

5. Monitor and Optimize:

   • Use cloud monitoring tools (e.g., AWS CloudWatch, Azure Monitor, or GCP Stackdriver) to track resource usage and tune the setup.
   • Implement auto-shutdown policies for dev/test clusters during off-hours.

By carefully selecting VM types, optimizing resource allocation, and leveraging cloud-specific discounts, you can run Kafka on VMs cost-effectively across AWS, Azure, or GCP.