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Amazon DynamoDB: redis/key-pais store, no sql
Amazon EMR (Elastic MapReduce): Apache Hadoop, Apache Spark, Apache Hive, and Presto
Amazon Redshift: is based on PostgreSQL

Amazon Athena: analysis data in S3 using basic SQL, use HiveQL
Amazon Kinesis: similar to Kafka, good for small team with no DevOps

AWS Glue: ETL pipeline, similar to airflow ?


This system will recommend movies to users based on their historical preferences and behavior. We’ll leverage several AWS services to accomplish this:

  1. Data Collection and Storage:
    • Amazon S3: Store raw data such as user interactions (e.g., clicks, ratings) and movie metadata (e.g., titles, genres) in S3 buckets.
  2. Data Processing:
    • AWS Glue: Use Glue for data preparation and ETL tasks. Create Glue jobs to clean, transform, and enrich raw data from S3 before loading it into a data warehouse.
    • Amazon Redshift: Store the processed data in Redshift, a fully managed data warehouse, optimized for querying large datasets. Design schema to support efficient querying for recommendation generation.
  3. Model Training:
    • Amazon SageMaker: Utilize SageMaker for training machine learning models. Implement collaborative filtering algorithms such as matrix factorization or deep learning models for recommendation.
    • Amazon EMR: Optionally, use EMR to perform large-scale data processing tasks or train complex models using distributed computing frameworks like Apache Spark or TensorFlow.
  4. Model Deployment:
    • Amazon SageMaker: Deploy the trained model as an endpoint on SageMaker for real-time inference. This endpoint will receive user requests and generate personalized recommendations on-the-fly.
    • AWS Lambda: Alternatively, deploy the model using Lambda functions for serverless execution, which can be cost-effective for low-traffic applications.
  5. Scalability and Real-time Processing:
    • Amazon Kinesis: Stream user interactions in real-time using Kinesis Data Streams. This allows the system to continuously update user preferences and adapt recommendations accordingly.
    • Amazon Athena: Query real-time data in S3 using Athena for ad-hoc analysis or to generate personalized recommendations based on the latest user behavior.
  6. User Interface:
    • Amazon API Gateway and AWS Lambda: Create APIs using API Gateway, backed by Lambda functions, to expose recommendation endpoints. These endpoints will provide recommendations to client applications or interfaces.
    • AWS Amplify: Build a front-end application using Amplify, which provides tools and libraries for web and mobile app development. Interface with the recommendation API to display personalized movie recommendations to users.
  7. Monitoring and Analytics:
    • Amazon CloudWatch: Monitor system metrics, logs, and alarms to ensure the health and performance of the recommendation system.
    • Amazon QuickSight: Visualize usage patterns, recommendation effectiveness, and user engagement metrics using QuickSight dashboards for continuous improvement and optimization.

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