Overview
Apache Kafka is the industry-standard platform for building real-time data pipelines and streaming applications. Aiven for Apache Kafka takes care of all operational aspects so you can focus on building applications.Cluster Types
Aiven offers two Kafka cluster types to match different workload requirements:- Inkless Kafka
- Classic Kafka
Inkless Kafka stores topic data in cloud object storage through diskless topics, enabling elastic scaling and long-term retention without managing disk capacity.Key Features:
- Diskless topics that store data in object storage
- Classic topics with managed remote storage
- Elastic storage scaling
- Cost-efficient for high-throughput workloads
- Ideal for BYOC deployments
- High-throughput workloads
- Long-term data retention requirements
- Storage elasticity is important
- Cost optimization is a priority
Key Features
Tiered Storage
Store data indefinitely by moving older segments to cost-effective cloud object storage (S3, GCS, Azure Blob) while keeping recent data on fast local disks.
Kafka Connect
Managed connectors for integrating with databases, storage systems, and data platforms. Available on Professional tier.
MirrorMaker 2
Cross-cluster replication for disaster recovery, multi-region architectures, and migration between Kafka clusters.
Schema Registry
Centralized schema management with support for Avro, JSON Schema, and Protobuf. Ensure data compatibility across producers and consumers.
Kafka REST API
HTTP interface for producing and consuming messages without native Kafka clients.
Kafka Quotas
Control resource usage with quotas on throughput, request rates, and client connections.
Getting Started
Create a Kafka Service
Choose between Inkless or Classic Kafka based on your requirements:
- Inkless Kafka
- Classic Kafka
- Select Inkless as the service type
- Choose Aiven Cloud or BYOC deployment
- Provide expected ingress, egress, and retention
- Deploy the service
Generate Sample Data
Test your Kafka service with the built-in sample data generator from the Aiven Console to verify connectivity.
Connection Examples
- Python
- Java
- Node.js
- Go
Advanced Features
Tiered Storage
Tiered storage decouples storage and compute, allowing indefinite data retention:How Tiered Storage Works
How Tiered Storage Works
- Recent data stays on fast local disks for low-latency access
- Older segments automatically move to cloud object storage (S3, GCS, Azure)
- Configure per-topic retention policies
- Significant cost savings for long retention periods
- Available on Classic Kafka (optional) and built into Inkless Kafka
Kafka Connect
Data Integration with Connectors
Data Integration with Connectors
Kafka Connect provides managed source and sink connectors:Popular Connectors:
- JDBC (PostgreSQL, MySQL, SQL Server)
- S3, GCS, Azure Blob Storage
- Elasticsearch, OpenSearch
- MongoDB, Cassandra
- Debezium CDC connectors
MirrorMaker 2
Cross-Cluster Replication
Cross-Cluster Replication
Replicate data between Kafka clusters for:
- Disaster recovery
- Multi-region architectures
- Migration between clusters
- Active-active deployments
Schema Registry
Centralized Schema Management
Centralized Schema Management
Schema Registry ensures data compatibility:
- Support for Avro, JSON Schema, Protobuf
- Schema evolution with compatibility checking
- Schema versioning and history
- Integration with producers and consumers
Performance and Scaling
Horizontal vs Vertical Scaling
Horizontal vs Vertical Scaling
Horizontal Scaling:
- Add more brokers to increase throughput
- Distribute partitions across brokers
- Handle more concurrent connections
- Upgrade to larger instance types
- Increase CPU and memory per broker
- Improve single-partition throughput
Partition Strategy
Partition Strategy
- More partitions = higher parallelism
- Balance partition count with consumer group size
- Consider replication factor for durability
- Typical: 3-10 partitions per broker
Consumer Groups
Consumer Groups
- Each consumer in a group processes different partitions
- Scale consumers up to number of partitions
- Monitor consumer lag to identify bottlenecks
Monitoring and Operations
Key Metrics to Monitor
- Throughput: Messages per second in/out
- Latency: End-to-end message delivery time
- Consumer Lag: How far behind consumers are
- Disk Usage: Local and remote storage consumption
- Replication: Under-replicated partitions
Integration with Observability Tools
Security
Authentication
- SASL/SSL authentication
- Certificate-based auth
- ACL-based authorization
- User and permission management
Encryption
- TLS encryption in transit
- Encryption at rest
- Separate encryption keys per service
Network Security
- VPC peering
- AWS PrivateLink
- IP allowlisting
- Private connectivity options
Compliance
- ISO 27001:2013
- SOC 2 Type II
- GDPR compliant
- HIPAA available
Use Cases
- Event-Driven Architecture
- Data Pipelines
- Log Aggregation
- Metrics and Monitoring
Build microservices that communicate through events:
- Decouple services with event streams
- Enable real-time processing
- Maintain event history
- Support event sourcing patterns
Best Practices
Topic Design
Topic Design
- Use meaningful topic names
- Plan partition count for expected throughput
- Set appropriate retention based on use case
- Enable compression (snappy or lz4)
- Consider topic naming conventions
Producer Configuration
Producer Configuration
- Set
acks=allfor durability - Enable idempotence for exactly-once semantics
- Batch messages for throughput
- Implement proper error handling
- Use async sends with callbacks
Consumer Configuration
Consumer Configuration
- Choose appropriate consumer group IDs
- Set proper
auto.offset.reset - Monitor consumer lag
- Implement graceful shutdown
- Handle rebalancing properly
Operational Excellence
Operational Excellence
- Monitor key metrics continuously
- Set up alerts for critical issues
- Plan capacity for peak loads
- Test disaster recovery procedures
- Document runbooks for common operations
Related Resources
Apache Flink
Stream processing on Kafka data
PostgreSQL
Sink Kafka data to PostgreSQL
OpenSearch
Search and analyze Kafka logs
ClickHouse
Real-time analytics on streaming data
Next Steps
- Create your first Kafka service
- Kafka Connect documentation
- Schema Registry guide
- Migration to Aiven Kafka
Free Tier Available: Try Aiven for Apache Kafka with no payment method required. Perfect for development and testing.