Azure Cosmos DB vs Kafka: What are the differences?
Azure Cosmos DB: A fully-managed, globally distributed NoSQL database service. Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development; Kafka: Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Azure Cosmos DB belongs to "NoSQL Database as a Service" category of the tech stack, while Kafka can be primarily classified under "Message Queue".
Some of the features offered by Azure Cosmos DB are:
- Fully managed with 99.99% Availability SLA
- Elastically and highly scalable (both throughput and storage)
- Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes
On the other hand, Kafka provides the following key features:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
"Best-of-breed NoSQL features" is the primary reason why developers consider Azure Cosmos DB over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.
Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas Azure Cosmos DB is used by Microsoft, Property With Potential, and Rumble. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Azure Cosmos DB, which is listed in 24 company stacks and 24 developer stacks.
What is Azure Cosmos DB?
What is Kafka?
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Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
If you need a document-based database with geo-redundancy (imagine AU-HU distance), this is the way to go.