DynamoDB Streams vs Kinesis: Choosing the Right AWS Streaming Service

When building real-time data processing applications on AWS, you might find yourself choosing between Amazon DynamoDB Streams and Amazon Kinesis Data Streams. While both services enable you to capture and process streaming data, they serve different purposes and have distinct characteristics that make each better suited for specific use cases.

This article provides a comprehensive comparison to help you understand when to use DynamoDB Streams, when to use Kinesis Data Streams, and how they might work together in your architecture.

Overview: DynamoDB Streams vs Kinesis at a Glance

Feature	DynamoDB Streams	Kinesis Data Streams
Primary Purpose	Capture data changes in DynamoDB	General-purpose data streaming
Data Source	DynamoDB tables only	Any custom source
Retention Period	24 hours (fixed)	24 hours to 365 days (configurable)
Scaling	Automatic, based on table activity	Manual shard provisioning or on-demand
Ordering	Per-partition key ordering	Per-shard ordering
Record Size	Up to 400KB (DynamoDB item size)	Up to 1MB
Common Consumers	Lambda, DynamoDB Streams Kinesis Adapter	Lambda, KCL, Firehose, Analytics
Pricing Model	No additional cost for Streams, pay for read operations	Pay per shard hour and PUT payload units

Service Overview: Understanding the Core Differences

DynamoDB Streams

DynamoDB Streams is a feature of Amazon DynamoDB that captures a time-ordered sequence of item-level modifications in any DynamoDB table. When enabled, each change to data items in the table is recorded in the stream in near real-time.

Key characteristics include:

Tied directly to a DynamoDB table
Captures item-level changes (inserts, updates, deletes)
Includes before and after images of modified items
Automatically scales with the table’s throughput
Primarily designed for reacting to database changes

Kinesis Data Streams

Amazon Kinesis Data Streams is a standalone service for collecting and processing large streams of data records in real time. It can handle various data sources, not just database changes, making it much more versatile.

Key characteristics include:

Independent service for general data streaming
Handles data from many different sources
Configurable retention and throughput
Requires explicit provisioning or on-demand capacity
Designed for broad real-time data processing needs

Data Sources and Capture Models

DynamoDB Streams Data Sources

DynamoDB Streams can only capture data changes from DynamoDB tables. When enabled on a table, it automatically captures:

New items added to the table (INSERT)
Updates to existing items (MODIFY)
Items removed from the table (REMOVE)

For each modification, the stream can include:

Keys Only: Only the key attributes of the modified item
New Image: The entire item, as it appears after it was modified
Old Image: The entire item, as it appeared before it was modified
New and Old Images: Both the new and the old images of the item

This model is optimized specifically for database change data capture (CDC).

Kinesis Data Streams Data Sources

Kinesis Data Streams can ingest data from virtually any source:

Application logs and events
Clickstream data from websites
IoT device telemetry
Financial transactions
Social media feeds
Custom application events
Infrastructure metrics
Output from other AWS services

Data is pushed to Kinesis via the PutRecord or PutRecords API calls, which can be made from any application or service capable of making AWS API calls.

Stream Processing and Consumers

DynamoDB Streams Processing

DynamoDB Streams are typically processed by:

AWS Lambda: The most common way to process DynamoDB Streams is through Lambda functions, which can be triggered automatically when new records appear in the stream.
DynamoDB Streams Kinesis Adapter: This library allows you to use the Kinesis Client Library (KCL) to consume and process records from DynamoDB Streams.
Custom applications: Using the AWS SDK to directly read from DynamoDB Streams.

Example of Lambda trigger configuration:

// Lambda function triggered by DynamoDB Stream
exports.handler = async (event) => {
  for (const record of event.Records) {
    console.log('DynamoDB Record: %j', record.dynamodb);
    
    // Process based on event type
    if (record.eventName === 'INSERT') {
      // Handle new item
      const newItem = record.dynamodb.NewImage;
      // Process new item...
    } else if (record.eventName === 'MODIFY') {
      // Handle updated item
      const oldItem = record.dynamodb.OldImage;
      const newItem = record.dynamodb.NewImage;
      // Compare and process changes...
    } else if (record.eventName === 'REMOVE') {
      // Handle deleted item
      const oldItem = record.dynamodb.OldImage;
      // Process deletion...
    }
  }
  return { status: 'success' };
};

Kinesis Data Streams Processing

Kinesis Data Streams offers more processing options:

AWS Lambda: Similar to DynamoDB Streams, Lambda functions can process Kinesis stream records.
Kinesis Client Library (KCL): A specialized library for building applications that process data from Kinesis.
Kinesis Data Analytics: Real-time SQL or Apache Flink applications on streaming data.
Kinesis Data Firehose: Automatically load streaming data into destinations like S3, Redshift, Elasticsearch, or Splunk.
AWS Glue: Process and transform streaming data for analytics.
Apache Spark Streaming: Process Kinesis streams using Spark.
Custom consumers: Any application using the AWS SDK.

This broader range of processing options makes Kinesis more versatile for complex streaming architectures.

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Scaling and Throughput Considerations

DynamoDB Streams Scaling

DynamoDB Streams scaling is managed automatically:

Throughput scales in proportion to the underlying DynamoDB table
No manual shard management required
Limited to two concurrent consumers per shard
Throughput implicitly limited by DynamoDB’s write capacity

This simplicity is both an advantage (less to manage) and a limitation (less control).

Kinesis Data Streams Scaling

Kinesis Data Streams offers two capacity modes:

Provisioned mode:
- Manually specify the number of shards
- Each shard provides 1MB/sec input, 2MB/sec output
- Up to 1000 records/sec for writes
- You’re responsible for monitoring and adjusting shard count
On-demand mode:
- Automatically adjusts capacity based on throughput needs
- No provisioning or management of shards required
- Higher cost per data volume compared to provisioned

This flexibility allows Kinesis to handle very high throughput scenarios but requires more active management in provisioned mode.

Retention and Data Access

DynamoDB Streams Retention

DynamoDB Streams has fixed retention characteristics:

Records are retained for 24 hours (not configurable)
After 24 hours, records are automatically removed
No ability to extend retention period
Designed for near-real-time processing, not historical analysis

Kinesis Data Streams Retention

Kinesis Data Streams offers flexible retention:

Default retention period of 24 hours
Can be extended up to 365 days (for additional cost)
Longer retention enables replay of historical data
Supports multiple applications reading from different positions

This flexibility makes Kinesis more suitable for scenarios where you might need to reprocess historical data or have multiple applications consuming at different rates.

Integration with AWS Services

DynamoDB Streams Integrations

DynamoDB Streams integrates primarily with:

AWS Lambda (native event source)
Amazon Aurora (for DynamoDB to Aurora replication)
Custom applications via AWS SDK

Kinesis Data Streams Integrations

Kinesis Data Streams has broader integration with the AWS ecosystem:

AWS Lambda
Kinesis Data Firehose
Kinesis Data Analytics
Amazon EMR
AWS Glue
Amazon SageMaker
Amazon CloudWatch Logs
AWS IoT Core
Amazon MSK (via Kafka Connect)
Third-party services via AWS Marketplace

This extensive integration makes Kinesis more versatile for complex data processing pipelines.

Use Cases: When to Choose Each Service

Ideal Use Cases for DynamoDB Streams

DynamoDB Streams is best for:

Reacting to database changes:
- Updating search indexes when data changes
- Sending notifications on record updates
- Maintaining derived data in other storage systems
Implementing the CQRS pattern:
- Command Query Responsibility Segregation
- Write to DynamoDB, propagate changes to read-optimized stores
Cross-region replication:
- While Global Tables is preferred, custom replication is possible
Auditing and compliance:
- Capturing all data modifications for audit trails
- Recording who changed what and when
Simple event-driven microservices architectures:
- Triggering workflows when data changes
- Service-to-service communication via data

Ideal Use Cases for Kinesis Data Streams

Kinesis Data Streams is better for:

High-volume data ingestion:
- Clickstream analysis
- IoT device telemetry
- Log and event data collection
- Financial transaction processing
Real-time analytics:
- Gaming telemetry analysis
- Ad tech and marketing analytics
- Real-time dashboards
- Anomaly detection
Multiple consumers of the same stream:
- When multiple applications need the same data
- Different processing requirements for the same events
Complex stream processing:
- When you need sophisticated stream processing
- Integration with Apache Flink or Spark
- Time-windowed analytics
Long retention requirements:
- When data might need to be reprocessed
- Applications that process at different speeds

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Combining DynamoDB Streams and Kinesis

In many advanced architectures, it makes sense to use both services together:

DynamoDB Streams to Kinesis Data Streams

A common pattern is to capture changes from DynamoDB Streams and forward them to Kinesis Data Streams:

DynamoDB Streams captures database changes
Lambda function processes these changes
Lambda forwards the processed data to Kinesis
Multiple consumers process the Kinesis stream

This approach provides:

Database change capture via DynamoDB Streams
Extended retention through Kinesis
Multiple processing applications on the same change data
Integration with services that work with Kinesis but not DynamoDB Streams

Example architecture:

DynamoDB Table → DynamoDB Stream → Lambda → Kinesis Data Stream → Multiple Consumers
                                                               ↓
                                                    Kinesis Firehose → S3/Redshift
                                                               ↓
                                                    Kinesis Analytics

Cost Comparison

DynamoDB Streams Pricing

DynamoDB Streams itself doesn’t incur additional charges, but you pay for:

Read requests to the stream (in Read Capacity Units)
Lambda invocations if using Lambda to process the stream
Any additional AWS resources used in processing

This can make DynamoDB Streams cost-effective for moderate change volumes, especially if you’re already using DynamoDB.

Kinesis Data Streams Pricing

Kinesis Data Streams costs include:

Provisioned mode:
- Shard hours ($0.015 per shard hour)
- PUT payload units ($0.014 per million)
- Extended retention (if beyond 24 hours)
- Data retrieval (for enhanced fan-out)
On-demand mode:
- Data ingested ($0.08 per GB)
- Data retrieved ($0.05 per GB)
- Extended retention (if applicable)

For high-volume or continuous streams, these costs can be significant, but the flexibility and scalability often justify the expense.

Development and Operational Considerations

DynamoDB Streams Considerations

When working with DynamoDB Streams:

Shard management: Handled automatically based on table partitioning
Monitoring: Use CloudWatch metrics like IteratorAge
Error handling: Implement proper retry logic in Lambda or KCL consumers
Testing: Use DynamoDB Local for local development
Limits: Be aware of limits on concurrent Lambda executions per stream
Ordering: Guaranteed order only within the same partition key

Kinesis Data Streams Considerations

For Kinesis Data Streams:

Shard management: In provisioned mode, monitor and adjust shard count
Monitoring: Track metrics like WriteProvisionedThroughputExceeded
Consumer design: Choose between standard and enhanced fan-out
Hot shards: Design partition keys to avoid uneven data distribution
Error handling: Implement checkpointing to handle failures
Cost optimization: Right-size shards based on actual throughput needs

Tools for Working with Streams

DynamoDB Streams Tools

AWS Management Console for enabling and monitoring streams
AWS CLI for stream management
AWS CloudFormation for infrastructure as code
AWS Lambda console for function configuration
CloudWatch for monitoring stream metrics

Kinesis Data Streams Tools

AWS Management Console for stream management
AWS CLI for advanced operations
Kinesis Producer Library (KPL) for efficient data production
Kinesis Client Library (KCL) for consumer applications
Kinesis Agent for log file ingestion
Third-party connectors and tools

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Conclusion: Making Your Decision

The choice between DynamoDB Streams and Kinesis Data Streams depends on your specific requirements:

Choose DynamoDB Streams when your primary need is to react to changes in DynamoDB tables, when you prefer automatic scaling, and when you’re mainly concerned with processing database events in near real-time.
Choose Kinesis Data Streams when you need a general-purpose streaming solution, when you have diverse data sources beyond DynamoDB, when you require longer retention periods, or when you need more sophisticated stream processing capabilities.
Use both together when you want to capture DynamoDB changes but need the additional capabilities of Kinesis for downstream processing, retention, or integration with other AWS services.

By understanding the strengths and limitations of each service, you can design an architecture that efficiently meets your real-time data processing needs while optimizing for cost, scalability, and operational complexity.

When working with DynamoDB and its streams, tools like Dynomate can significantly improve your development experience by providing intuitive interfaces for visualizing your data, monitoring performance, and managing your DynamoDB resources more effectively.