• AWS Kinesis Stream: Streams events (JSON objects) continuously.
• AWS Lambda Function: Processes events from Kinesis, transforms them, and stores them into AWS S3.
• AWS S3 Bucket: Stores the processed data in a data lake architecture.

• AWS Lambda: Chosen for its serverless architecture, which automatically scales with incoming event load, eliminating the need for managing infrastructure. Lambda functions are event-driven and integrate seamlessly with AWS Kinesis for stream processing.

• AWS Kinesis: Selected for its capability to handle high volumes of streaming data reliably and durably. Kinesis streams allow real-time processing of data from multiple sources and can scale elastically based on incoming data rates.

• AWS S3: Used for storing the processed data in a data lake architecture. S3 provides high durability, scalability, and low-cost storage, making it ideal for storing large volumes of data in various formats (JSON, Parquet, etc.).

Duplicate events can be handled using the following approach:

• Event Deduplication: Utilize event UUIDs (event_uuid) to uniquely identify events. Before processing an event, check if its UUID already exists in a persistent store (e.g., DynamoDB table or S3 object metadata). If the UUID is found, skip processing; if not, proceed with event processing and store the UUID to mark it as processed.

To ensure good querying performance and scalability in AWS S3:

• Partitioning Strategy: Partition the data in S3 by event_type and optionally by created_datetime. This allows for efficient data retrieval using S3's partition pruning capabilities. For example, store data in a structure like event_type/year/month/day/hour/data.json.

Data can be stored in JSON or Parquet format in AWS S3:

• JSON Format: If flexibility and human readability are important.
• Parquet Format: If performance, compression, and columnar storage benefits are preferred, especially for large-scale data analytics and querying.

• Unit Testing: Test individual functions within the Lambda function (e.g., event transformation) using Python's unittest framework.

• Integration Testing: Test the end-to-end flow of processing events from Kinesis, transforming them with Lambda, and storing them in S3. Use sample events and verify data integrity in S3.

• Infrastructure as Code (IaC): Use tools like Terraform to define and provision AWS resources (Kinesis stream, Lambda function, S3 bucket) in a reproducible manner.

• Configuration Management: Store configuration variables (e.g., S3 bucket names, AWS region) in parameter files (terraform.tfvars) and environment variables to adapt to different environments (development, staging, production).

Yes, the proposed solution is scalable and can handle varying amounts of events:

• Scalability: AWS Lambda and Kinesis scale automatically based on incoming event load. Adjustments in configuration (e.g., shard count for Kinesis, memory allocation for Lambda) can optimize performance for different event volumes.

If the requirement for transforming data changes or additional fields are no longer needed:

• Simplify Lambda Function: Modify the Lambda function (lambda_function.py) to directly store raw events from Kinesis to S3 without transformation. Remove unnecessary code for data transformation and focus on handling raw event ingestion.

This README provides a comprehensive overview of the architecture, technologies chosen, and how various design considerations are addressed in the solution for processing events with AWS Lambda, Kinesis, and S3. Adjustments can be made based on specific project requirements and scalability needs.