Kinesis Data Streams vs. Kinesis Data Firehose
| Feature | Kinesis Data Streams | Kinesis Data Firehose |
|---|---|---|
| Purpose | Real-time streaming data ingestion and custom processing. | Simplified, fully managed streaming data delivery to destinations. |
| Primary Use Case | Custom applications for real-time analytics or ETL. | Automated data delivery to storage or analytics services like S3, Redshift, etc. |
| Processing | Requires custom consumers (e.g., Lambda, EC2, KCL applications). | AWS automatically manages the data delivery process. |
| Data Retention | Up to 7 days of data retention for replay or reprocessing. | Data is buffered temporarily (1–15 minutes) before delivery. |
| Latency | Sub-second latency for real-time processing. | Near real-time (minimum 1-minute delivery interval). |
| Data Transformation | Requires custom code or Lambda for data transformation. | Built-in support for basic transformations via AWS Lambda. |
| Scalability | Scales with shards (user-configured). | Fully automatic scaling based on throughput. |
| Integration | Integrates with Lambda, DynamoDB, EMR, Elasticsearch, etc. | Integrates with S3, Redshift, Elasticsearch, and OpenSearch. |
| Customizability | Highly customizable, allowing you to build complex pipelines. | Limited customization; optimized for simplicity and delivery. |
| Monitoring | Provides shard-level metrics and detailed monitoring via CloudWatch. | Offers delivery metrics such as success/failure rates and throughput. |
| Cost | Pricing based on number of shards, data throughput, and retention. | Pricing based on the volume of data ingested and transformed. |
| Setup Complexity | Requires significant setup and configuration for custom applications. | Minimal setup with automatic data management. |
Amazon Kinesis vs Amazon MSK (Managed Streaming for Apache Kafka)
| Feature | Amazon Kinesis | Amazon MSK (Managed Streaming for Apache Kafka) |
|---|---|---|
| Description | Fully managed real-time data streaming platform by AWS. | Fully managed service for running Apache Kafka (open-source distributed messaging system). |
| Primary Use Case | Real-time streaming and analytics for applications. | Message queueing, pub/sub messaging, and distributed event streaming. |
| Supported APIs | AWS Kinesis API. | Apache Kafka API (e.g., Kafka Producer/Consumer API, Streams API). |
| Ease of Setup | Very easy, serverless, no infrastructure to manage. | Requires some knowledge of Kafka, including topic configurations and cluster setup. |
| Scalability | Automatically scales (serverless model). | Manually scales via broker instances and partitions. |
| Latency | Low latency for real-time processing. | Low latency for event streaming, but dependent on cluster configuration. |
| Data Retention | Default: 24 hours, extendable to 7 days (or longer). | Customizable retention policies per topic, often up to weeks/months. |
| Integration | Deep integration with AWS services like Lambda, S3, Redshift, and Elasticsearch. | Integrates with Kafka-compatible tools and AWS services (e.g., Lambda, MSK Connect). |
| Message Ordering | Ensures ordering per shard. | Ensures ordering per partition. |
| Replication | Native data replication across availability zones (highly available). | Kafka’s replication mechanism (configurable replication factor). |
| Management | Fully serverless and managed by AWS. | Fully managed Kafka, but still requires some operational knowledge. |
| Cost Model | Pay-per-use based on shards and data throughput (DPU). | Pay for EC2 instances (brokers), storage, and networking. |
| Key Strengths | – Simplified, serverless real-time streaming. – Seamless AWS integration. | – Apache Kafka compatibility. – Customizable for specific use cases. |
| Key Weaknesses | – No compatibility with Kafka ecosystem. – Limited message size (1 MB). | – More complex to manage compared to Kinesis. |