Apache Kafka + AWS Kinesis
Connect Apache Kafka and AWS Kinesis for Real-Time Data Streaming
Bridge Kafka's messaging backbone with AWS Kinesis so your event-driven pipelines work as one.
Why integrate Apache Kafka and AWS Kinesis?
Apache Kafka and AWS Kinesis are two of the most widely used real-time streaming platforms around, and plenty of organizations need both running together. Whether you're running on-premises Kafka clusters alongside AWS workloads or moving legacy event pipelines into Kinesis-powered analytics stacks, tray.ai makes it straightforward to route, transform, and sync streams between the two. Connecting Kafka and Kinesis means engineering and data teams can stop babysitting data silos and make sure every downstream consumer — from analytics dashboards to ML models — gets timely, consistent event data.
Automate & integrate Apache Kafka & AWS Kinesis
Use case
Mirror Kafka Topics to Kinesis Data Streams
Automatically forward every message published to a Kafka topic into a corresponding Kinesis Data Stream in real time. This pattern is common for organizations migrating workloads to AWS or running hybrid architectures where on-premises producers must feed cloud-based consumers. tray.ai handles partition-to-shard mapping and preserves message ordering end-to-end.
Use case
Ingest Kinesis Stream Data Back into Kafka Topics
Consume records from Kinesis Data Streams and publish them into designated Kafka topics, so on-premises or non-AWS consumers can process cloud-originated events. This reverse flow matters for organizations that generate data in AWS services like IoT Core or CloudWatch and need it available within their Kafka ecosystem. tray.ai polls Kinesis shards, batches records efficiently, and writes them to Kafka with configurable retry logic.
Use case
Real-Time Clickstream Analytics Pipeline
Capture user clickstream events from web and mobile apps via Kafka, then route enriched event records into Kinesis Data Firehose for delivery to Amazon Redshift or S3. tray.ai applies lightweight transformations — user-agent parsing, session enrichment — between the two systems so your analytics warehouse always receives clean, structured data. Marketing and product teams get near-instant visibility into user behavior without waiting for nightly batch loads.
Use case
Fraud Detection Event Routing
Stream financial transaction events from Kafka into Kinesis, where AWS-native ML services like SageMaker or Fraud Detector can score them in real time. Results publish back to Kafka so downstream services — alert systems, case management tools, compliance dashboards — can act on fraud signals immediately. tray.ai orchestrates the full round-trip with built-in error handling and dead-letter queue support.
Use case
IoT Sensor Data Aggregation and Forwarding
Aggregate IoT device telemetry arriving in Kinesis Data Streams and forward processed sensor records into Kafka topics consumed by operations dashboards and alerting systems. This pattern shows up in manufacturing, logistics, and smart infrastructure where devices emit data to AWS IoT Core but operational teams use Kafka-connected monitoring tools. tray.ai handles the aggregation window logic and routes alerts based on configurable threshold rules.
Use case
Log and Metrics Pipeline Unification
Collect application logs and infrastructure metrics published to Kafka and relay them into Kinesis Data Streams for ingestion by Amazon CloudWatch, OpenSearch, or third-party observability platforms. Engineering teams running multi-environment deployments get a single tray.ai workflow that normalizes log formats and routes data to the right Kinesis stream based on log level or service tag — no more fragmented observability tooling or slow time-to-detect.
Use case
Event-Driven Microservices Coordination Across Platforms
Let microservices deployed in AWS react to domain events published by on-premises Kafka producers, and vice versa, without requiring a shared messaging bus across cloud boundaries. tray.ai consumes Kafka events and publishes them to Kinesis for AWS Lambda subscribers, then captures Lambda-produced results and writes them back to Kafka for on-premises service consumption. It's a natural fit for teams doing a strangler-fig migration toward AWS.
Get started with Apache Kafka & AWS Kinesis integration today
Apache Kafka & AWS Kinesis Challenges
What challenges are there when working with Apache Kafka & AWS Kinesis and how will using Tray.ai help?
Challenge
Schema and Data Format Incompatibilities
Kafka and Kinesis serialize and structure data differently. Kafka ecosystems commonly use Avro or Protobuf with a Schema Registry, while Kinesis records are raw byte blobs with no native schema enforcement. Translating between these formats without data loss or corruption requires careful transformation logic that's hard to build and maintain in custom code.
How Tray.ai Can Help:
tray.ai has a built-in data transformation engine with JSON, Avro, and Protobuf support, so schema translation happens at the integration layer without touching producers or consumers. Teams can define reusable transformation templates and version them independently of their streaming pipelines, which cuts the risk of breaking changes.
Challenge
Message Ordering and Exactly-Once Delivery Guarantees
Kafka's partition-based ordering model and Kinesis's shard-based sequencing model have subtle but important differences that make it easy to accidentally reorder messages or introduce duplicates when bridging the two systems. Forwarding records without accounting for these semantics can corrupt downstream processing logic that depends on strict event ordering.
How Tray.ai Can Help:
tray.ai's Kafka-Kinesis connector templates map Kafka partition keys directly to Kinesis partition keys, preserving ordering semantics end-to-end. Built-in idempotency controls, sequence number checkpointing for Kinesis consumers, and configurable exactly-once delivery settings prevent duplicates during failure recovery and workflow restarts.
Challenge
Operational Overhead of Managing Consumer Offsets and Shard Iterators
Maintaining Kafka consumer group offsets and Kinesis shard iterator states across workflow runs is a real burden. Without careful state management, restarting a pipeline can either re-process millions of old records or skip new ones entirely — both cause downstream data quality problems that are annoying to diagnose.
How Tray.ai Can Help:
tray.ai automatically persists Kafka consumer group offsets and Kinesis sequence number checkpoints between workflow executions, so every run picks up exactly where the last one left off. Teams can also configure replay windows to intentionally reprocess historical records when needed, without writing any state management code.
Challenge
Handling Kinesis Shard Splits and Kafka Topic Rebalances
Both platforms change their partition topology over time — Kinesis streams get resharded as throughput requirements change, and Kafka topics go through partition reassignment during cluster scaling. Pipelines built with static partition assumptions break silently when these topology changes happen, causing data loss or stalled consumers that are hard to spot.
How Tray.ai Can Help:
tray.ai's connector layer dynamically discovers Kinesis shard topology and Kafka partition assignments at runtime, automatically adapting to resharding and rebalancing events without manual reconfiguration. Alerts fire whenever topology changes are detected, so engineering teams stay informed without constant monitoring.
Challenge
Throughput Throttling and Backpressure Management
Kinesis Data Streams enforces strict per-shard write limits of 1 MB/s and 1,000 records/s, while Kafka clusters can produce millions of messages per second. Without intelligent backpressure handling, high-throughput Kafka-to-Kinesis pipelines quickly hit ProvisionedThroughputExceededException errors, causing data loss or cascading retry storms that make performance worse.
How Tray.ai Can Help:
tray.ai implements adaptive rate limiting and intelligent batching for Kinesis PutRecords calls, automatically throttling ingest rates to stay within provisioned shard limits. Exponential backoff with jitter handles throttled requests, and the platform surfaces real-time throughput metrics so teams can provision additional Kinesis shards before limits become a problem.
Start using our pre-built Apache Kafka & AWS Kinesis templates today
Start from scratch or use one of our pre-built Apache Kafka & AWS Kinesis templates to quickly solve your most common use cases.
Apache Kafka & AWS Kinesis Templates
Find pre-built Apache Kafka & AWS Kinesis solutions for common use cases
Template
Kafka Topic to Kinesis Data Stream Sync
Continuously listens for new messages on a specified Kafka topic and forwards each record to a target Kinesis Data Stream, preserving message keys as Kinesis partition keys to maintain ordering. Includes configurable batch sizes, compression settings, and a dead-letter Kafka topic for failed Kinesis put operations.
Steps:
- Trigger: New message arrives on a configured Kafka topic
- Transform: Map Kafka record key and value to Kinesis PutRecord payload, applying optional JSON schema validation
- Action: Put record to the target Kinesis Data Stream using the Kafka key as the partition key
- Error Handler: On Kinesis put failure, publish failed record to a configured dead-letter Kafka topic and emit an alert
Connectors Used: Kafka, AWS Kinesis
Template
Kinesis Shard to Kafka Topic Consumer
Polls one or more Kinesis Data Stream shards on a configurable interval, batches the retrieved records, and publishes them to a designated Kafka topic. Supports multi-shard fan-out and automatically checkpoints Kinesis sequence numbers to avoid duplicate processing after restarts.
Steps:
- Trigger: Scheduled poll of Kinesis Data Stream shard iterators at a configurable interval
- Fetch: Retrieve record batch from each active shard, tracking sequence number checkpoints
- Transform: Deserialize Kinesis record data and construct Kafka ProducerRecord with appropriate topic and partition routing
- Action: Publish transformed records to the target Kafka topic in batched produce calls
Connectors Used: AWS Kinesis, Kafka
Template
Bidirectional Kafka-Kinesis Event Bridge
A two-way event bridge between a Kafka cluster and Kinesis Data Streams, routing messages from Kafka to Kinesis and from Kinesis back to Kafka based on configurable topic and stream name mapping rules. Good for hybrid cloud deployments where both platforms need to stay in sync without circular message loops.
Steps:
- Configure: Define topic-to-stream and stream-to-topic routing rules in the tray.ai workflow configuration
- Inbound Flow: Consume Kafka topic messages and publish to mapped Kinesis streams, stamping records with an origin header to prevent loop re-processing
- Outbound Flow: Poll Kinesis streams for new records, filter out origin-stamped records, and publish to mapped Kafka topics
- Monitor: Emit lag metrics for both flows to a shared CloudWatch custom metrics namespace
Connectors Used: Kafka, AWS Kinesis
Template
Real-Time Transaction Events to Kinesis for Fraud Scoring
Listens on a Kafka topic for financial transaction events, enriches each record with customer risk profile data fetched from an external API, and writes the enriched event to a Kinesis stream consumed by AWS Fraud Detector. Fraud scoring results returned via a separate Kinesis stream are then published back to a Kafka results topic for downstream action.
Steps:
- Trigger: New transaction event consumed from the Kafka payments topic
- Enrich: Call external risk profile API to append customer risk score to the transaction payload
- Action: Write enriched transaction record to the Kinesis fraud-input stream
- Response: Poll Kinesis fraud-results stream for scoring outcomes and publish results to Kafka fraud-alerts topic
Connectors Used: Kafka, AWS Kinesis
Template
IoT Kinesis Stream to Kafka Alerting Pipeline
Consumes IoT telemetry records from a Kinesis Data Stream, evaluates each record against configurable threshold rules, and routes anomalous readings to a high-priority Kafka alert topic while writing normal readings to a bulk Kafka telemetry topic. Downstream Kafka consumers can then trigger operational dashboards or on-call notifications.
Steps:
- Trigger: New IoT telemetry records detected in the Kinesis sensor-data stream
- Evaluate: Apply threshold rule logic to each record to classify it as normal or anomalous
- Route: Publish anomalous records to the Kafka iot-alerts topic and normal records to the Kafka iot-telemetry topic
- Notify: Trigger a downstream Kafka consumer workflow to page on-call engineers for critical anomalies
Connectors Used: AWS Kinesis, Kafka
Template
Application Log Forwarding from Kafka to Kinesis Firehose
Subscribes to a Kafka log-aggregation topic, normalizes log records into a standardized JSON schema, and delivers batched records to an Amazon Kinesis Data Firehose delivery stream configured to land data in S3 and trigger an OpenSearch ingestion pipeline. Supports dynamic routing to different Firehose streams based on log level or application tag.
Steps:
- Trigger: Batch of log records consumed from the Kafka application-logs topic
- Normalize: Parse and transform raw log strings into a standardized JSON schema, extracting log level, service name, and timestamp
- Route: Evaluate log level and application tag to select the appropriate Kinesis Firehose delivery stream
- Deliver: Put normalized log batch to the selected Firehose stream for S3 landing and downstream OpenSearch indexing
Connectors Used: Kafka, AWS Kinesis