Welcome to Cloud-Native Architecture! Remember how we containerized our data pipelines? Now we'll learn to deploy those containers on cloud platforms for maximum scalability and cost-effectiveness.
Cloud-native means building applications designed specifically for cloud environments, not just moving existing code to the cloud.
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What's one advantage you'd expect from running containerized data pipelines in the cloud?
Cloud platforms offer managed services that handle infrastructure complexity for you. Instead of managing servers, you focus on your data logic while AWS, Azure, or Google Cloud handle scaling, patching, and availability.
Think of it like ordering food delivery instead of cooking everything from scratch.
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What's an operational task you'd prefer a cloud provider to handle?
Managed services are particularly powerful for data workloads. Cloud providers offer managed databases, message queues, workflow orchestrators, and container services.
Your containerized pipeline can use Amazon RDS for storage, AWS Lambda for lightweight processing, and Amazon ECS for container orchestration.
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In one sentence, how does using managed services shift your team's focus?
Serverless architecture takes this further—you only pay for actual compute time, not idle servers. Your data transformation runs for 5 minutes? You pay for 5 minutes.
This is perfect for sporadic data jobs that don't need always-on infrastructure.
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Name one type of data processing job that would benefit from serverless pricing?
Cloud-native architectures are built for failure. Services automatically restart failed containers, replicate data across regions, and scale based on demand.
Your data pipeline becomes more resilient than any single server could be.
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