Percona Server vs. Qubole

Percona Server for MySQL covers 100% of our requirements in terms of our OLTP traffic and provides the High Availability levels required through a typical primary and 2 replica's using semi-synchronous replication. We're able to scale our writes using sharding, but a true distributed database would be a great option on top of this. In terms of OLAP traffic, we leverage columns stores available, but Percona Server fairs well

I find Qubole is well suited for getting started analyzing data in the cloud without being locked in to a specific cloud vendor's tooling other than the underlying filesystem. Since the data itself is not isolated to any Qubole cluster, it can be easily be collected back into a cloud-vendor's specific tools for further analysis, therefore I find it complementary to any offerings such as Amazon EMR or Google DataProc.

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• Faster than other server solutions
• Installation and configuration process is easy from the user's standpoint
• Easy to learn and good support

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• From a UI perspective, I find Qubole's closest comparison to Cloudera's HUE; it provides a one-stop shop for all data browsing and querying needs.
• Auto scaling groups and auto-terminating clusters provides cost savings for idle resources.
• Qubole fits itself well into the open-source data science market by providing a choice of tools that aren't tied to a specific cloud vendor.

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• Better documentation
• A better UI

• Providing an open selection of all cloud provider instance types with no explanation as to their ideal use cases causes too much confusion for new users setting up a new cluster. For example, not everyone knows that Amazon's R or X-series models are memory optimized, while the C and M-series are for general computation.
• I would like to see more ETL tools provided other than DistCP that allow one to move data between Hadoop Filesystems.
• From the cluster administration side, onboarding of new users for large companies seems troublesome, especially when trying to create individual cluster per team within the company. Having the ability to debug and share code/queries between users of other teams / clusters should also be possible.

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Personally, I have no issues using Amazon EMR with Hue and Zeppelin, for example, for data science and exploratory analysis. The benefits to using Qubole are that it offers additional tooling that may not be available in other cloud providers without manual installation and also offers auto-terminating instances and scaling groups.

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It is easy to install and use. Using it along with Percona Monitoring and Management makes it even easier to use.

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We are using opensource so we have not used customer support.

Performance and stability sets Percona Server for MySQL apart from the rest. Percona embraces the upstream version and contributes towards the upstream version making MySQL stronger on two fronts and thus having a much larger community. Many quality contributions have been made by Percona into the base code. Percona provides great tools to support Percona Server for MySQL and this makes the monitoring and management easier

Qubole was decided on by upper management rather than these competitive offerings. I find that Databricks has a better Spark offering compared to Qubole's Zeppelin notebooks.

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• At the performance level, it maintains and even improves other open-source databases such as MariaDB or some commercial ones such as Oracle.
• If we compare it with MySQL, we could have up to 50% improvement in performance.
• For some small companies or startups, the price of the complete solution and the support can be a bit high.

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• We like to say that Qubole has allowed for "data democratization", meaning that each team is responsible for their own set of tooling and use cases rather than being limited by versions established by products such as Hortonworks HDP or Cloudera CDH
• One negative impact is that users have over-provisioned clusters without realizing it, and end up paying for it. When setting up a new cluster, there are too many choices to pick from, and data scientists may not understand the instance types or hardware specs for the datasets they need to operate on.

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