CouchDB VS ApacheKafka

CouchDB is a json based database for simple projects. The fork pouchdb offers lots of support for offline.

Apache CouchDB belongs to the family of NoSQL databases. It is a document store with a strong focus on replication and reliability. One of the most significant differences between CouchDB and a relational database (besides the absence of tables and schemas) is how you query data. Relational databases allow their users to execute arbitrary and dynamic queries via SQL. Each SQL query may look completely different than the previous one. These dynamic aspects are significant for use cases where you work exploratively with your dataset but don't matter as much in a web context. Additionally, defining an index for a specific table is optional. Most developers will define indices to boost performance, but the database does not require it.

I just turns out you can't always do that in a real codebase. For example see here:

  https://github.com/apache/couchdb/blob/23efd8e5b1aa96ef01640fec03a5fedc945ba8b9/src/couch_mrview/src/couch_mrview_http.erl#L228

Example: Apache Cassandra, CouchDB.

For non-SQL-based databases, consider MongoDB, or CouchDB, which are very easy to get started with.

The Apache CouchDB server. It stores Annotation data.

CouchDB

You can implement the sync algorithm from scratch, or you can use tools like CouchDB and turtleDB to help you.

Senden an Kafka (d. h. Senden der Daten an Apache Kafka)

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.

*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)

RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.

Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.

also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.