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How Sendoso is using Kafka for Event-Driven Architecture
3 projects | dev.to | 31 Aug 2021
Event sourcing is an effective architectural pattern to record changes to the application state. Event sequence is important - we need changes as they were originally applied. incoming events are first persisted into Kafka and then processed by services independently. Kafka, hence, becomes our source of truth (SOT), a data source that gives a complete picture of the data object as a whole. This, however, meant dramatic changes in our core application. Our source of truth is still the core database, but we generate events in Kafka when data gets persisted. To ensure transactional behavior, we employed Transactional Outbox pattern. Essentially, we a) created a new events table in the database, b) wrote event data in the same transaction when we update our SOT table. Kafka Connect is subsequently used to read this table and insert records in relevant Kafka topics. This ensures that we never have an inconsistent situation where data was inserted in the database but the event is not added to Kafka topic or vice versa. We evaluated a few connectors for sourcing data from Mysql (JdbcSourceConnector, and Debezium). Our scenario was supported out of the box in JdbcSourceConnector, making it possible to have one event table in Mysql where different rows could be routed to a relevant topic based on the topic field.
Data Pipeline between PostgreSQL and Cassandra using Kafka Connect
3 projects | dev.to | 18 Jan 2021
This is different compared to the "polling" technique adopted by the Kafka Connect JDBC connector
We haven't tracked posts mentioning kafka-rest yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
examples - Apache Kafka and Confluent Platform examples and demos
nri-prometheus - Fetch metrics in the Prometheus metrics inside or outside Kubernetes and send them to the New Relic Metrics platform.
Apache Kafka - Mirror of Apache Kafka
kafka-connect-elasticsearch - Kafka Connect Elasticsearch connector
ksql-udf-deep-learning-mqtt-iot - Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data
firehose - Firehose is an extensible, no-code, and cloud-native service to load real-time streaming data from Kafka to data stores, data lakes, and analytical storage systems.
schema-registry - Confluent Schema Registry for Kafka
kafdrop - Kafka Web UI
fast-data-dev - Kafka Docker for development. Kafka, Zookeeper, Schema Registry, Kafka-Connect, Landoop Tools, 20+ connectors
data-mesh-demo - A Data Mesh prototype built on Confluent Cloud