API Gateway Integration with Kinesis Data Streams, Kinesis Data Firehose & S3 | Deep Dive.

Here is twtech Deep Dive into API Gateway integration with Kinesis Data Streams, Kinesis Data Firehose, and Amazon S3.

Scope:

•        Data flow,
•        Components,
•        Use cases, and
•        Best practices.

Breakdown:

•        Overview,
•        Components & Flow,
•        Amazon API Gateway,
•        Kinesis Data Streams (Real-Time, Low-Latency Stream Processing),
•        Kinesis Data Firehose (Fully Managed Ingestion → Storage/Analytics),
•        Amazon S3 (Long-Term Durable Storage),
•        End-to-End Architecture,
•        API Gateway → Kinesis Integration Details,
•        Choosing Between Data Streams vs Firehose,
•        Using both Data Streams & Firehose,
•        Security Considerations,
•        Best Practices.

Overview

• API Gateway integration with Kinesis Data Streams, Kinesis Data Firehose, and Amazon S3 is used when twtech wants to capture high-volume, real-time API data (events, logs, telemetry, metrics, IoT payloads, analytics data) then store the data or stream it with low latency, high durability, and real-time processing capabilities.

 Components & Flow

1. Amazon API Gateway

API Gateway provides:

•         A fully managed public REST/HTTP endpoint
•         Authentication (IAM, Cognito, Lambda authorizer, API keys)
•         Request validation & throttling
•         Payload transformation (VTL/Passthrough)
•         Direct service integration with:
•    Kinesis Data Streams
•    Kinesis Data Firehose

Two integration modes:

1.      Direct AWS Service Integration (no Lambda)
2.      Lambda Proxy Integration → then Kinesis

2. Kinesis Data Streams (Real-Time, Low-Latency Stream Processing)

NB:

API Gateway can push requests directly into a Kinesis Data Stream using AWS service integration.

 Flow:

1.      API Gateway receives request
2.      API Gateway signs the request using SigV4
3.      Record is inserted into a specific Kinesis Stream shard
4.      Consumers process records in real time:

•    AWS Lambda consumer
•    Kinesis Client Library apps (EC2/ECS)
•    Kinesis Data Analytics
•    Kinesis → Firehose → S3

 Use cases:

•         High-volume clickstream ingestion
•         IoT telemetry
•         Financial tick data
•         Live metrics/analytics pipelines
•         Machine-learning data ingestion

3. Kinesis Data Firehose (Fully Managed Ingestion → Storage/Analytics)

NB:

Firehose is a simpler option when twtech doesn’t want custom stream processing.

API Gateway → Kinesis Firehose →S3/Data Lake Store/OpenSearch/Custom endpoints via Lambda

•         S3
•         Redshift
•         OpenSearch
•         Data Lake storage
•         Custom endpoints (via Lambda transformation)

 Flow:

1.      API Gateway receives an event
2.      Writes JSON payload into Firehose
3.      Firehose buffers (size/time-based)
4.      Delivers compressed/collated files to S3 (or other destination)

 Optional: Lambda transformations

Firehose can invoke a Lambda function for:

•         PII redaction
•         JSON → Parquet conversion
•         Data normalization
•         Data flattening

4. Amazon S3 (Long-Term Durable Storage)

Kinesis Firehose typically stores data into S3 in:

•         Snappy-compressed Parquet
•         GZIP JSON
•         ORC

Ideal for:

•         Data lakes
•         Audit logs
•         API analytics
•         Cost-efficient retention

End-to-End Architecture

Flow 1: API Gateway → Kinesis Data Streams → Consumers → Optional S3

Flow 2: API Gateway → Kinesis Firehose → S3

API Gateway → Kinesis Integration Details

API Gateway Request Template Sample (PutRecord)

# json

{

  "StreamName": "twtech-stream",

  "Data": "$util.base64Encode($input.body)",

  "PartitionKey": "$context.requestId"

}

IAM role:

API Gateway must assume a role with:

kinesis:PutRecord

kinesis:PutRecords

firehose:PutRecord

firehose:PutRecordBatch

 Choosing Between Data Streams vs Firehose

Feature	Kinesis Data Streams	Kinesis Firehose
Latency	~70 ms	1–5 min buffer
Real-time processing	Yes	No
Custom compute	Lambda, KCL	Optional Lambda
Storage destinations	Flexible	Mostly S3/Redshift/OpenSearch
Throughput scaling	Manual shard scaling	Fully managed
Ideal for	Real-time apps	Data ingestion and storage

 Using both Data Streams & Firehose

Security Considerations

•         API Gateway IAM auth + SigV4 request signing
•         KMS encryption for Streams & Firehose
•         S3 bucket encryption + Block Public Access
•         Private API Gateway with VPC endpoint optional
•         WAF integration to filter malicious calls

 Best Practices

•         Use partition keys that evenly distribute data
•         Use Firehose for S3-only pipelines (simpler, cheaper)
•         Use Streams for real-time consumer apps
•         Enable DLQs (dead-letter queues) in Firehose
•         For heavy workloads → enable API Gateway throttling