Discover and read the best of Twitter Threads about #Debezium
Most recents (5)
#ApacheKafka has integrations with most of the languages used these days.
@alexsotob covers its integration with #Java and discusses how to provision, configure & secure an Apache Kafka cluster on a #Kubernetes cluster: bit.ly/3HN0Tqw
#InfoQ
Series Contents 👇
@alexsotob covers its integration with #Java and discusses how to provision, configure & secure an Apache Kafka cluster on a #Kubernetes cluster: bit.ly/3HN0Tqw
#InfoQ
Series Contents 👇
2/6 ➡️ Apache Kafka is a stream-processing platform for storing, consuming, and processing data streams in real-time. Learn how to produce and consume data using Kafka and Quarkus: bit.ly/3HGby6x
#ApacheKafka #Quarkus #Java
#ApacheKafka #Quarkus #Java
3/6 ➡️ The Kafka Streams project consumes real-time streams of events as they are produced, apply transformations, join streams, etc. Learn how to use Kafka Streams and Quarkus: bit.ly/3RGUbqy
#KafkaStreams #Quarkus #Java
#KafkaStreams #Quarkus #Java
My blog post "Event Driven Architecture — 5 Pitfalls to Avoid" just crossed 45K views!
some important comments i've received in 🧵1/3
link: medium.com/wix-engineerin…
@WixEng
some important comments i've received in 🧵1/3
link: medium.com/wix-engineerin…
@WixEng
1. What about using #OutboxPattern?
While a good pattern to use, for @WixEng we decided that #WixGreyhound Resilient producer provides high enough evnt publishing guarantees that we can avoid the extra DB pressure and complexity of Outbox. We may go with #Debezium as well 2/3
While a good pattern to use, for @WixEng we decided that #WixGreyhound Resilient producer provides high enough evnt publishing guarantees that we can avoid the extra DB pressure and complexity of Outbox. We may go with #Debezium as well 2/3
2. Event-driven architecture #EDA and #EventSourcing are unrelated. one talks about services communication while the other on persistent layer
Yep, you're right! I should add a disclaimer here... but event-sourcing will affect how you communicate between services... 3/3
Yep, you're right! I should add a disclaimer here... but event-sourcing will affect how you communicate between services... 3/3
👋 Hey students, the JBoss community is part of #GoogleSummerOfCode, and @debezium is looking forward to your project proposals! Some ideas at spaces.redhat.com/display/GSOC/G… (e.g. a Debezium JDBC sink connector, ZOMG 🚀).
Interested? Get in touch via email: groups.google.com/g/debezium
Interested? Get in touch via email: groups.google.com/g/debezium
Project idea 1⃣: A stand-alone tool for compacting the schema history topic of Debezium connectors, allowing for faster start-up of connectors with large histories.
spaces.redhat.com/display/GSOC/G…
spaces.redhat.com/display/GSOC/G…
Project idea 2⃣: Porting the Debezium Cassandra connector to Debezium Server, allowing for a unified user experience across all the different connectors.
spaces.redhat.com/display/GSOC/G…
spaces.redhat.com/display/GSOC/G…
#Postgres as an event store -- Thanks a lot for all the super-insightful answers 🙏! It looks like using a jsonb[] for modeling an event stream isn't ideal performance-wise, but several great pointers to using #Postgres for event sourcing here. Mentioned solutions include... 1/4
- FactCast: docs.factcast.org
- Message DB: github.com/message-db/mes…
- @marten_lib: martendb.io
- @axonframework: axoniq.io
- crabzilla: github.com/crabzilla/crab… (an event sourcing exploration using @vertx_project)
2/4
- Message DB: github.com/message-db/mes…
- @marten_lib: martendb.io
- @axonframework: axoniq.io
- crabzilla: github.com/crabzilla/crab… (an event sourcing exploration using @vertx_project)
2/4
Two really read-worthy blog posts on the topic of using #Postgres as an event store:
- dev.to/kspeakman/even… by Kasey Speakman
- softwaremill.com/implementing-e… by @adamwarski
3/4
- dev.to/kspeakman/even… by Kasey Speakman
- softwaremill.com/implementing-e… by @adamwarski
3/4
Message transformations (SMTs) are an invaluable feature of @ApacheKafka Connect, enabling tons of use cases with a small bit of coding, or even just configuration of existing SMTs ready to use. Here are some applications in the context of change data capture: (1/7)
* Converting data types and formats: date/time formats are the most common example here, e.g. to convert milli-seconds timestamps into strings adhering to a specific date format (2/7)
* Creating an "anti-corruption layer", shielding consumers from legacy schemas or ensuring compatibility after schema changes; e.g. could use an SMT to choose more meaningful field names, or re-add a field using its old name after a column rename, easing consumer migration (3/7)
