kafka-ml
debezium
| kafka-ml | debezium | |
|---|---|---|
| 1 | 80 | |
| 149 | 9,907 | |
| 6.7% | 1.3% | |
| 7.9 | 9.9 | |
| 2 months ago | 4 days ago | |
| Python | Java | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
kafka-ml
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What are your favorite tools or components in the Kafka ecosystem?
For example, CLIs, UIs, monitoring tools / integrations, cluster administration, stream processing libraries (Flink, Kafka Streams, smaller / newer libs), etc? Anything in the ML / AI space (e.g. a quick Google search came up with https://github.com/ertis-research/kafka-ml).
debezium
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
They manage data in the application layer and your original data stays where it is. This way data consistency is no longer an issue as it was with streaming databases. You can use Change Data Capture (CDC) services like Debezium by directly connecting to your primary database, doing computational work, and saving the result back or sending real-time data to output streams.
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Generating Avro Schemas from Go types
Both of these articles mention a key player, Debezium. In fact, Debezium has had a place in the modern infrastructure. Let's use a diagram to understand why.
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debezium VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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How the heck do I validate records with this kind of data??
This might be overkill, but you could use an extra tool like https://debezium.io to capture logs about all creates, updates, and deletes in your table
- All the ways to capture changes in Postgres
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Managed Relational Databases with AWS RDS and Aurora
If you're considering a relational database for an event-driven architecture, check out Debezium. It lets you stream changes to relational databases, and subscribe to change events.
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Real-time Data Processing Pipeline With MongoDB, Kafka, Debezium And RisingWave
Debezium
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Postgresql to hadoop in real time
https://debezium.io/ comes to mind as an open source product, but there are a gazillion of these tools out there.
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ClickHouse Advanced Tutorial: Apply CDC from MySQL to ClickHouse
Contrary to what it sounds, it’s quite straightforward. The database changes are captured via Debezium and published as events on Apache Kafka. ClickHouse consumes those changes in partial order by Kafka Engine. Real-time and eventually consistent.
- Debezium: Stream Changes from Your Database
What are some alternatives?
conduktor-poc-kafka-protocol - POC to demonstrate how to alter incoming/outgoing records in Kafka. It's a toy, don't use it in production.
maxwell - Maxwell's daemon, a mysql-to-json kafka producer
kloadgen - KLoadGen is kafka load generator plugin for jmeter designed to work with AVRO, JSON and PROTOL-BUFFERS schema Registries.
kafka-connect-bigquery - A Kafka Connect BigQuery sink connector
console - Redpanda Console is a developer-friendly UI for managing your Kafka/Redpanda workloads. Console gives you a simple, interactive approach for gaining visibility into your topics, masking data, managing consumer groups, and exploring real-time data with time-travel debugging.
realtime - Broadcast, Presence, and Postgres Changes via WebSockets
bytewax - Python Stream Processing
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
python-fake-data-producer-for-apache-kafka - The Python fake data producer for Apache Kafka® is a complete demo app allowing you to quickly produce JSON fake streaming datasets and push it to an Apache Kafka topic.
hudi - Upserts, Deletes And Incremental Processing on Big Data.
materialize - The data warehouse for operational workloads.
RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.