| 1 |
Explosion of Data Volume |
Need to handle billions of events at scale |
When your system ingests continuous, massive streams |
Scale horizontally with partitions |
Topics + Partitions |
LinkedIn handling profile views, job clicks, ad impressions |
| 2 |
Multiple Consumers, One Data Source |
Multiple teams need the same data |
When analytics, fraud detection, and reporting all need identical streams |
Use independent consumer groups |
Consumer Groups |
Payment service → Fraud team + Analytics + Data Warehouse |
| 3 |
Replay for Debugging & ML |
Debugging & ML need data replay |
When you need to reprocess past data |
Enable log retention & control offsets |
Offsets + Retention |
Replaying 1 year of user clicks to re-train an ML model |
| 4 |
One Pipeline for Real-time + Batch |
Must support both real-time + batch |
When you want one pipeline for dashboards + ETL |
Mix stream consumers & batch consumers (via Connect) |
Kafka Connect + Stream APIs |
E-commerce system: live inventory + nightly sales reports |
| 5 |
Fault Tolerance by Design |
Prevent data loss on failures |
When uptime and durability are mission-critical |
Replicate partitions across brokers |
Replication + Leader/Follower |
Bank processing transactions with 0 tolerance for loss |
| 6 |
Decoupling Producers & Consumers |
Avoid producer-consumer coupling |
When new consumers must join without disturbing producers |
Producers write once, consumers subscribe freely |
Decoupled Pub/Sub |
IoT sensor data → analytics, monitoring, ML pipelines |
| 7 |
Guaranteed Event Ordering |
Need event ordering |
When events per user/session must stay in order |
Key-based partitioning |
Partition + Ordering Guarantee |
Chat app → messages for same user land in order |
| 8 |
Stream Processing in Motion |
Process streams in motion |
When you must enrich/filter/aggregate data on the fly |
Use Kafka Streams or KSQL |
Stream Processing |
Ride-sharing: calculate ETA with live traffic data |
| 9 |
Plug-and-Play Integrations |
Integrate with existing systems |
When data must flow to DBs, cloud storage, or other clusters |
Use Kafka Connect connectors |
Connectors |
Push logs to Elastic, load sales into Snowflake |
| 10 |
Global Data Distribution |
Build geo-distributed systems |
When you need multi-DC/cloud active-active setups |
Replicate topics across clusters |
MirrorMaker / Cluster Linking |
Global SaaS syncing events across regions |