Heroku Postgres vs Kafka: What are the differences?
Developers describe Heroku Postgres as "Heroku's Database-as-a-Service. Based on the most powerful open-source database, PostgreSQL". Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management. On the other hand, Kafka is detailed as "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.
Heroku Postgres belongs to "PostgreSQL as a Service" category of the tech stack, while Kafka can be primarily classified under "Message Queue".
Some of the features offered by Heroku Postgres are:
- High Availability
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)
"Easy to setup" is the primary reason why developers consider Heroku Postgres over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.
Kafka is an open source tool with 12.5K GitHub stars and 6.7K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
According to the StackShare community, Kafka has a broader approval, being mentioned in 501 company stacks & 451 developers stacks; compared to Heroku Postgres, which is listed in 74 company stacks and 38 developer stacks.
What is Heroku Postgres?
What is Kafka?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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.
Stores the admin database for the SRX apps - includes an audit log, error tracking, and SRX admin message log.
Will also store PRS rules when refactor is complete.
Rock solid transactional storage of user, purchase and course activity data. During development database dumps were easy to create and download locally for testing.
We use heroku PostgreSQL databases for testing alongside our sandboxed application(s) in heroku.
Extremely simple, practically a one-click setup.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
4 years of experience using Heroku Postgres for data storage and management.
Created several tables for users, brands, deals, campaigns, and tracking.