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@kittl/sdk-backend handles authentication on your server by verifying short-lived JWTs from Kittl users, keeping sensitive auth logic out of the browser sandbox. It gives you a `KittlSDK` class to validate tokens and optional external key storage for serverless/edge environments.
- Frontend calls `kittl.auth.getUserToken()` to get a JWT, sends it to your backend, where `KittlSDK.verifyUserToken()` validates it before processing the request
- Token verification returns `payload.sub`, a pseudonymous per-app user ID that stays consistent for each user across sessions
- External signing key caching is required for serverless, edge, and autoscaling runtimes where in-memory caches don't persist between requests
AWS Lambda now supports S3 Files, letting functions mount S3 buckets as file systems for direct read/write without object downloads. This simplifies stateful and AI/ML workflows by enabling shared workspaces and automatic checkpointing via durable functions.
- Lambda functions can now mount S3 buckets directly as file systems (S3 Files, built on EFS) instead of downloading/uploading objects in code
- Multiple Lambda functions can access the same S3 Files mount simultaneously for shared, real-time workspaces without custom locking
- Combined with durable functions for checkpointing, this avoids the 512 MB /tmp storage limit for stateful, multi-step workflows like AI/ML pipelines
- Setup works through the Lambda console, CLI, SDKs, CloudFormation, or SAM, with no extra fee beyond normal Lambda and S3 pricing
Amazon MWAA runs Airflow pipelines on AWS as a managed service with serverless or provisioned deployment options. It handles infrastructure provisioning, scaling, security, and integrates with SageMaker for end-to-end ML and data workflows.
- MWAA now offers two deployment modes: serverless (pay-per-execution, auto-scaling) and provisioned (full config control with AWS-managed infrastructure)
- It integrates directly with the next-gen SageMaker Unified Studio, letting Airflow run alongside Jupyter notebooks for unified data prep, training, and inference pipelines
- Both modes run inside your VPC with end-to-end encryption and IAM controls, with serverless adding workflow-level permissions and provisioned offering environment-wide team separation