Discussing architecture and design of data applications using AWS

AWS offers a variety of services that can be used to build data applications. Some common architectural patterns for data applications on AWS include:

1. Data Lakes: A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store data in its raw, unprocessed form and then use tools like Amazon EMR, Apache Spark, and AWS Glue to process and analyze the data.

2. Data Warehouses: A data warehouse is a type of database designed for fast querying and analysis of data. AWS offers a fully managed data warehouse service called Amazon Redshift, which makes it easy to set up, operate, and scale a data warehouse.

3. Streaming Data Pipelines: AWS offers a number of services for building real-time streaming data pipelines. For example, you can use Amazon Kinesis to ingest and process streaming data in real time, and Amazon S3 or Amazon Redshift to store the processed data for analysis.

4. Machine Learning Workflows: AWS provides a number of services that can be used to build machine learning (ML) workflows. For example, you can use Amazon SageMaker to build, train, and deploy ML models, and Amazon ECS or AWS Fargate to run your ML workloads at scale.

5. Serverless Data Processing: AWS provides a number of serverless services that can be used to build data applications that automatically scale to meet demand. For example, you can use AWS Lambda to run your data processing code in response to events, and Amazon DynamoDB to store the processed data.

There are many other services available on AWS that can be used to build data applications, and the best architecture will depend on the specific requirements of your application.