Cloudera Distribution Hadoop (CDH) vs. Starburst Enterprise

Cloudera Distribution Hadoop (CDH) does a lot of things really well - especially on the analytical front. That being said the product is quite expensive. There are seemingly numerous applications that do the same thing on the functional level that are much more cost effecient for enterprise teams. If I were recommending this to a colleague I would let them know the product will absolutely be able to get the job done for their use case, but there are more efficient options

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If you run a SQL query, Starburst Presto can help you track efficiently the status of SQL query. It can also track how many resources have been allocated for execution and what is the optimal way to run the query without hindrance. It also provides good information on worker nodes and how parallel threads are running together.

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• Solid and robust set of integrations
• Easy to use and easy to deploy across the enterprise
• Reliability - never lost any info
• Simple and clean interface

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• Query tracking.
• Resource allocation.
• Parallelizing query execution.

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• The price is quite high competitively speaking
• Hard to learn more robust functions and custom options without experience

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• Pricing
• Platform can be made more intuitive.
• Sometimes the platform hangs while aborting the query.

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In terms of functionality there's not much difference, both get the job done. Amazon was more cost-efficient for our team, but this could vary depending on the size of the business. One thing I did notice was that Cloudera seemed to management and spit out our deployments faster than AWS.

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Most of our systems were compatible with Starburst Presto. The dashboard which they provide was fairly intuitive and easy to use. The learning curve wasn't that much. Also, the parallel processing part was an additional feature that we didn't find in many competitive products. The pricing was a little higher but it was worth the trade-off.

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• Saves time by automating typically manual processes (data management, lifecyle AI etc)
• Quick deployments and analytics allow for faster time-to-value

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• Neutral impact.
• ROI on saving time for query execution.
• Parallel processing saves time too.

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