What is Amazon Kinesis?
Amazon Kinesis is a platform for streaming data on AWS, offering powerful services to make it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Kinesis enables you to ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications.
With Kinesis, you can process and analyze data as it arrives and respond immediately instead of having to wait until all your data is collected before processing can begin. It's used by Netflix for real-time video analytics, by Spotify for music recommendation engines, and by Tesla for processing millions of vehicle telemetry events.
Kinesis Performance & Cost Calculator
Shards Needed: 1 (recommended)
Storage Cost: $1.9/month
Total Storage: 82.4 GB
Kinesis Service Family
Kinesis Data Streams
Real-time data streaming with custom processing applications.
• 1,000 records/sec per shard
• Multiple consumer applications
• 24h to 365 days retention
• Enhanced fan-out capability
Kinesis Data Firehose
Fully managed delivery to data lakes and analytics services.
• Automatic scaling
• Built-in data transformation
• Format conversion (Parquet/ORC)
• No infrastructure management
Kinesis Data Analytics
SQL-based real-time analytics on streaming data.
• Windowing and aggregation
• Pattern matching (MATCH_RECOGNIZE)
• Automatic scaling
• Built-in ML functions
Kinesis Video Streams
Secure video streaming for analytics and ML.
• Rekognition integration
• WebRTC support
• Time-indexed storage
• Automatic durability
Real-World Kinesis Implementations
Netflix
Processes 500+ billion events daily for real-time personalization, A/B testing, and operational monitoring.
- • 500+ billion events per day
- • Real-time recommendation updates
- • Video quality optimization
- • Operational monitoring and alerting
Spotify
Uses Kinesis for real-time music recommendation engine and user behavior analytics across 400M+ users.
- • Real-time playlist generation
- • Music discovery algorithms
- • User behavior pattern analysis
- • Content recommendation optimization
Tesla
Collects and processes millions of vehicle telemetry events for autonomous driving improvements.
- • Vehicle sensor data streaming
- • Real-time fleet monitoring
- • Autonomous driving training data
- • Predictive maintenance alerts
Capital One
Processes financial transactions in real-time for fraud detection and risk management.
- • Real-time fraud detection
- • Transaction monitoring
- • Risk scoring algorithms
- • Compliance reporting automation
Common Kinesis Architecture Patterns
Real-time Analytics Pipeline
Stream processing for immediate insights and automated actions.
Data Sources → Kinesis Data Streams → Lambda/KCL Consumer → Real-time Dashboard
↓
Kinesis Analytics (SQL) → Alerts & ActionsData Lake Ingestion
Continuous delivery of streaming data to data lakes with transformation.
Applications → Kinesis Data Firehose → S3 Data Lake (Parquet)
↓ ↓
Lambda Transform Athena/Glue AnalyticsIoT Data Processing
Scalable ingestion and processing of IoT sensor data with anomaly detection.
↓
Kinesis Analytics (Anomaly Detection)
↓
SNS Alerts
Kinesis Best Practices
✅ Do
- • Use meaningful partition keys for even distribution
- • Implement exponential backoff for retry logic
- • Monitor shard-level metrics and hot shards
- • Use Enhanced Fan-Out for multiple consumers
- • Batch records when possible (up to 500 per call)
- • Set appropriate data retention periods
- • Use dead letter queues for failed processing
- • Implement idempotent record processing
❌ Don't
- • Use sequential partition keys (creates hot shards)
- • Ignore ProvisionedThroughputExceeded errors
- • Create too many small records (high overhead)
- • Process records synchronously without parallelism
- • Forget to handle duplicate record delivery
- • Use Kinesis for small-scale data (under 100 MB/hour)
- • Mix different data types in same stream
- • Ignore consumer lag monitoring