why we can not use database instead of using aws kinesis

Storing streaming data directly in a traditional database and then processing it has several limitations and challenges, which make it impractical or inefficient for certain use cases compared to using a dedicated streaming data service like AWS Kinesis. Here are some reasons why storing streaming data in a database may not be ideal:

1. Latency and Real-Time Processing

• Streaming Nature: Streaming data arrives continuously and in real time. Traditional databases are optimized for batch processing or transactions, where data is typically inserted or updated in larger batches rather than incrementally and continuously.

• Performance: Databases may not handle high-volume, rapid data ingestion efficiently, especially with the need for real-time processing and analysis. This can lead to latency issues and delays in data availability for analysis or decision-making.

2. Data Volume and Scalability

• Data Volume: Streaming data sources can generate large volumes of data rapidly. Databases may struggle to keep up with the high throughput and scalability requirements needed for real-time ingestion and processing of streaming data.

• Scalability: Scaling traditional databases horizontally (across multiple servers) to handle large volumes of streaming data can be complex and costly compared to scalable managed services like AWS Kinesis, which are designed for high throughput and elasticity.

3. Data Model and Schema Evolution

• Schema Flexibility: Streaming data often lacks a predefined structure or schema, which can evolve over time as new data sources are added or existing ones change. Traditional databases typically require a fixed schema, making it challenging to adapt to evolving data models without significant schema changes and downtime.

4. Operational Overhead

• Infrastructure Management: Setting up and managing infrastructure to handle streaming data ingestion, storage, and processing in a database requires significant operational effort. Managed services like AWS Kinesis abstract away much of this complexity, allowing developers to focus on application logic rather than infrastructure management.

5. Analytical and Processing Capabilities

• Real-Time Analytics: Streaming data services like AWS Kinesis offer built-in capabilities for real-time analytics, enabling immediate insights and actions based on incoming data streams. Traditional databases may not provide native support for real-time analytics without additional integration and customization.

Use Case Considerations

While storing streaming data in a traditional database may suffice for some use cases with lower throughput and latency requirements, using a dedicated streaming data service like AWS Kinesis offers several advantages:

• Real-Time Processing: Immediate processing and analysis of incoming data streams without storage latency.
• Scalability: Ability to handle large volumes of data and scale horizontally as needed.
• Integration: Seamless integration with other AWS services for data storage, analytics, and application workflows.
• Managed Service: Reduced operational overhead with managed services handling infrastructure provisioning, scaling, and maintenance.