1. Amazon Kinesis Data Streams:

• Purpose:
  • Real-time Data Streaming Service: Kinesis Data Streams is designed for ingesting and processing real-time data streams.
  • High Scalability and Durability: It can handle large-scale data ingestion.
• Pros:
  • Low Latency: Provides low-latency streaming for near real-time processing.
  • Custom Consumers: Allows custom applications (e.g., running on EC2, EMR, or Lambda) to handle data transformation and processing.
  • Scalability: Scales horizontally to accommodate increased data volume.
• Cons:
  • Manual Provisioning: Requires manual capacity provisioning to meet anticipated needs.
  • Complexity: Setting up and managing custom consumers can be intricate.
• Use Cases:
  • Real-time Analytics: When you need to analyze data as it arrives (e.g., clickstreams, IoT telemetry).
  • Custom Processing: For custom applications that process data in near real-time.

2. Amazon Kinesis Data Firehose:

• Purpose:
  • Data Transfer Service: Kinesis Data Firehose focuses on loading streaming data into other services.
  • Simplified Data Delivery: It streamlines data delivery to Amazon S3, Splunk, ElasticSearch, and RedShift.
• Pros:
  • Ease of Use: Integrated with AWS data stores, making it straightforward to set up.
  • Automatic Elasticity: Scales automatically to meet demand.
  • Batching and Compression: Supports batching, encryption, and compression.
• Cons:
  • Global Scope: Data Firehose applies to all data within a stream.
  • Limited Customization: Less flexibility compared to custom consumers.
• Use Cases:
  • Data Archiving: Loading streaming data into Amazon S3 for long-term storage.
  • Log Aggregation: Sending logs to Splunk or ElasticSearch.
  • Data Warehousing: Loading data into Amazon RedShift for analytics.

In summary:

• Kinesis Data Streams is ideal for real-time analytics and custom processing.
• Kinesis Data Firehose simplifies data delivery to various destinations, but with less customization.