DynamoDB Streams Vs Kinesis Data Streams (KDS) | Deep Dive.

Amazon DynamoDB Streams  Vs  Kinesis Data Streams (KDS) -  Deep Dive.

Scope:

• Intro,
• The Concept: DynamoDB Streams,
• How DynamoDB Stream Processing Works,
• Common Consumers (target),
• Typical Processing Pattern (Diagram),
• Key Features,
• Use Cases,
• Design Considerations,
• Advanced Pattern – Enhanced Fan-Out,
• Deep comparison between DynamoDB Streams and Amazon Kinesis Data Streams (KDS),
• Core Purpose comparative table,
• Data Flow for DynamoDB Streams & Kinesis Data Streams,
• Key Capabilities table,
• Performance table,
• Integration Patterns When to Use DynamoDB Streams & Kinesis Data Streams,
• Architecture Relationship,
• Decision Matrix,
• DynamoDB CDC (capturing and reacting to data changes),
• Kinesis Data Streams for DynamoDB (options),
• Sample Real-time analysis of sensor data from transportation vehicles,
• Best practices.

Intro:

• A deep dive into  DynamoDB Stream Processing & Kinesis Data Streams,.
• Including:
• What it's all about,
• Steps to design a robust streaming architecture.

1. The Concept:  DynamoDB Streams

• Change data capture (CDC) feature for DynamoDB.
• Emits a time-ordered sequence of item-level modifications in a table.
• Streams contain information about:
• INSERT (new items)
• MODIFY (updated items)
• REMOVE (deleted items)
• Retention: 24 hours from the time of change.
• Can be enabled per table and configured to capture:

1. Keys only
2. New image
3. Old image
4. Both old & new images

2. How DynamoDB Stream Processing Works

1. Change occurs in DynamoDB table.
2. Change event is recorded in the stream.
3. Consumers (Lambda, Kinesis, custom apps) read from the stream.
4. Consumer processes event for analytics, indexing, replication, or triggering workflows.

3. Common Consumers (target)

• AWS Lambda (serverless, fully managed)
• Triggered automatically by new stream records.
• At-least-once processing.
• Batch size, window, and parallelism configurable.
• Amazon Kinesis Data Streams
• For fan-out to multiple consumers.
• Lower latency and higher throughput.
• Custom Stream Processing
• Using KCL (Kinesis Client Library) to build twtech own consumers.

4. Typical Processing Pattern (Diagram)

5. Key Features

• Exactly-once delivery?
  Not guaranteed — it’s at-least-once, so consumers must be idempotent.
• Ordering: Guaranteed per partition key.
• Throughput: Stream shares the table’s write capacity.
• Latency: Typically under 1 second for Lambda triggers.
• Parallelism: Scales with number of shards (1 shard per 1MB/s write throughput or 1000 writes/s).

6. Use Cases

• Event-driven microservices
• Materialized views for query optimization
• Full-text search with OpenSearch
• Real-time analytics pipelines
• Cross-region replication
• Data archiving to S3

7. Design Considerations

• Idempotency: Make stream processors safe to re-run.
• Error handling: Use DLQs (Dead Letter Queues) with Lambda or retry logic.
• Backpressure: High write volumes can overwhelm consumers — scale appropriately.
• Security: Restrict IAM permissions for stream reading/writing.
• Fan-out: Use DynamoDB Streams + Kinesis Data Streams for multiple independent consumers.

8. Advanced Pattern – Enhanced Fan-Out

If twtech has multiple consumers that need low-latency access without sharing throughput:

• Enable DynamoDB Streams → Kinesis Data Streams Enhanced Fan-Out  (EFO).
• Each consumer gets 2MB/s/shard dedicated throughput.

 Deep comparison between DynamoDB Streams and Amazon Kinesis Data Streams (KDS) 

•    Architecture,
•    Capabilities,
•    Limits,
•   When to use each.

1. Core Purpose comparative table

Feature	DynamoDB Streams	Kinesis Data Streams (KDS)
What it is	Change Data Capture (CDC) stream for DynamoDB table changes only.	General-purpose, high-throughput, low-latency streaming data platform.
Data Source	Automatically generated by DynamoDB table writes.	Any producer (applications, IoT devices, logs, etc.).
Scope	Narrow — tied to a single DynamoDB table.	Broad — can ingest any kind of event data.

2. Data Flow for DynamoDB Streams

1. Item change in table.
2. Event is added to the stream.
3. Consumers (Lambda, KCL app, Kinesis) read events.
4. Events expire after 24 hours.

Data Flow for Kinesis Data Streams

1. Producers write arbitrary records (max 1MB/record).
2. Records stored in shards.
3. Consumers (Lambda, KCL apps, analytics services) read.
4. Retention: 24 hours – 365 days (configurable).

3. Key Capabilities table

Capability	DynamoDB Streams	Kinesis Data Streams
Data Retention	Fixed 24 hours	Configurable (1 day – 365 days)
Ordering	Guaranteed per partition key	Guaranteed per shard
Data Payload	Item keys, old/new images, metadata	Arbitrary byte payload
Throughput Scaling	Matches table WCU	Scales by shard count
Fan-out	Standard read or Enhanced Fan-Out (via Kinesis integration)	Standard read or Enhanced Fan-Out
Trigger Lambda	Native	Native
Multi-Source Data	❌ (table-specific)	✅
Cross-Region Replication	Via Global Tables	Via Kinesis Data Streams + Data Firehose

4. Performance table

Metric	DynamoDB Streams	Kinesis Data Streams
Latency	~<1s for Lambda triggers	~<70ms for Enhanced Fan-Out
Max Record Size	~400 KB (DynamoDB item size limit)	1 MB per record
Scaling Granularity	Per table (WCU-based shards)	Per shard (manual or auto-scaling)

5. Integration Patterns When to Use DynamoDB Streams

• Event-driven actions only from DynamoDB changes.
• Automatic integration with Lambda (no producers to manage).
• Building materialized views, audit logs, cross-region replication.
• Real-time search indexing (e.g., OpenSearch updates from table writes).

Integration Patterns When to Use Kinesis Data Streams

• Multiple heterogeneous producers (IoT, logs, API events).
• Complex streaming analytics (via Kinesis Data Analytics, Flink, Spark).
• Longer retention or replay needs.
• Very high throughput ingestion (millions of records/sec).
• Multi-consumer pipelines (fan-out processing for different purposes).

6. Architecture Relationship 

NB:

They’re not mutually exclusive — in fact:

• DynamoDB Streams → Kinesis Data Streams is a common pattern.
• DynamoDB table emits change stream.
• Kinesis fan-outs to multiple independent consumers without competing for read throughput.

7. Decision Matrix

Question	Use DynamoDB Streams	Use Kinesis Data Streams
Need changes from DynamoDB table only?	✅	❌
Need multiple, unrelated data sources?	❌	✅
Need >24h retention?	❌	✅
Need to replay old data easily?	❌	✅
Require exactly-once processing?	❌ (at-least-once)	❌ (at-least-once)
Want lowest operational overhead for DynamoDB CDC?	✅	❌

Insights:

DynamoDB CDC (reacting to Change data capture)

• DynamoDB Change Data Capture (CDC) enables applications to capture item-level changes in a DynamoDB table in near real-time as a stream of data records. 
• Change Data Capture allows applications to efficiently process and respond to data modifications, facilitating use cases like: 
• Real-time data integration, 
• keeping operational data in sync across systems, 
• Warehouses, and other applications.
• Building responsive applications that react instantly to data changes (e.g., sending welcome emails upon new user signups).
• Mirroring data between DynamoDB tables, potentially across regions,
• Maintaining an audit trail of data modifications for record-keeping and compliance purposes.

•   Real-time analytics: feeding DynamoDB changes into analytics platforms for near real-time insights (e.g., updating leaderboards in a gaming application). 

DynamoDB CDC (options)

DynamoDB offers two streaming models for CDC:
     DynamoDB Streams:

•  Provides a time-ordered log of item-level modifications for a DynamoDB table, accessible for up to 24 hours.

         Features:

•    Preserves the exact order of modifications.
•    Each modification generates exactly one stream record (no duplicates).
•    Automatically ignores operations that do not modify data.

   Considerations:

•    Data accessible for only 24 hours.
•    Default limit of 2 simultaneous consumers per shard (more with enhanced fan-out in Kinesis).

    Sample use case:

•  Tracking inventory changes for an e-commerce platform.

     Kinesis Data Streams for DynamoDB (options): 

• Replicates item-level modifications to an Amazon Kinesis data stream.

      Features:

•    Longer data retention (up to 365 days).
•    Higher throughput and more simultaneous consumers (up to 20 with enhanced fan-out).
•    Integration with other Kinesis services (Data Firehose, Data Analytics) for advanced analytics and delivery to destinations like Amazon S3, Redshift, or OpenSearch Service.

    Considerations: 

• Record ordering and deduplication may require client-side implementation.

:  Real-time analysis of sensor data from transportation vehicles. 

• Implementing DynamoDB CDC
•  Enable streaming on your DynamoDB table (either DynamoDB Streams or Kinesis Data Streams) using the AWS Console, AWS SDK, or AWS CLI.
•  Configure stream record content: choose to include only keys, the new item image, the old item image, or both.

     NB:

• AWS Lambda: automatically triggered by new stream records for processing and workflow invocation.
• DynamoDB Streams Kinesis Adapter: allows applications to use the Kinesis Client Library (KCL) to process records from DynamoDB Streams, offering more control over stream processing.
• Kinesis Data Streams (if enabled): integrate with other Kinesis services and downstream destinations like S3 or Redshift.
• Third-party tools like: Estuary Flow, which offer simplified CDC implementation with features like backfill and deduplication. 

Best practices

•  twtech Chooses the appropriate streaming model based on its needs for data retention, throughput, and consumer fan-out.
•  Optimize KCL or Lambda consumers for efficient processing and to avoid issues like Lambda cold starts.
•   Monitor costs associated with stream processing.
•   Implement proper error handling for stream processing to ensure data consistency. 
•   By leveraging DynamoDB CDC, twtech shoould build a robust and scalable applications that react to data changes in real time, integrate seamlessly with other services, and support various use cases from real-time analytics to auditing and compliance.