Apache Kafka vs Databricks Lakehouse Platform

• Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
• It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
• The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.

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• Extremely Flexible in Data Scenarios
• Fantastic Performance
• DB is always updating the system so we can have latest features.

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• Still a bit inmature, some clients have required recoding in the last few versions
• New feaures coming very fast, several upgrades a year may be required
• Not many commercial companies provide support

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• The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
• Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.

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• Positive impact on ROI since now we can use one large deployment of Apache Kafka that can be used for multiple scenarios ( storage systems, log aggregate, messaging queue ).
• It is open-source so there are no licenses or subscription fees reducing the cost of deployment.
• Data can now be ingested and analyzed in real-time making it easy to fine-tune the customer experience and decision making for internal IT.

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• Rapid growth of analytics within our company.
• Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
• Platform is easy to learn and Databricks provides excellent support and training.
• Platform does not require a large DevOPs investment

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