Confluent Kafka Developer case studies
Confluent Kafka Developer · Put your knowledge to work in context.
The Confluent Certified Developer for Apache Kafka - also referred to as CCDAK or the Confluent Kafka Developer Certification - is a vendor credential for developers who design, build, and operate applications on Apache Kafka and the Confluent data streaming platform. It fits backend engineers, data engineers, and platform developers who already write producer and consumer code and now need to demonstrate they can do it correctly, safely, and at scale. The Confluent Kafka Developer case studies are 5 cases and about 3,302 words.
1Flash-Sale Hot Key vs. Repartitioning a Joined Stream
The orders topic has 6 partitions, RF=3, and producers key records by merchant id using the default partitioner. Peak traffic is 12k records/s.
689 words, about 3 min2Rebalance Loop Caused by a Synchronous Downstream Call
ledger-writer is a plain Kafka consumer (no Streams) in group ledger reading payments (24 partitions) with 8 instances. For each record it makes a synchronous HTTPS call to a ledger service: p50 700 ms, p99 2.8 s.
591 words, about 3 min3Exactly-Once Streams Job with an External Side Effect
A Kafka Streams app ( processing.guarantee=exactly once v2 ) reads clicks , computes 1-minute tumbling counts per campaign id with a 10 s grace period, calls peek() to POST each result to an external ranking API, and writes to clicks-per-minute . Cluster is 3 brokers, transaction.state.log.replication.factor=3 , min.insync.replicas=2 .
648 words, about 3 min4Multiple Event Types in One Topic and a Breaking Field Removal
Topic payments.events carries two Avro value types, PaymentAuthorized and PaymentCaptured , produced by different services. Schema Registry uses the default TopicNameStrategy , so both services try to register under subject payments.events-value and the second one fails with an incompatibility error.
657 words, about 3 min5CDC Pipeline That Silently Loses Rows
A Debezium PostgreSQL connector streams inventory.customers into a compacted topic with tombstone records on delete. A ExtractNewRecordState -style transform unwraps the before / after envelope, and an S3 sink connector lands JSON in a data lake.
717 words, about 4 min