Percona Server for MongoDB vs. Qubole

It offers good support for the implementation of solutions in the public and on-premises cloud and integration with other services such as Hashicorp Vault for data encryption. One of the main advantages is the ease of configuration, in addition to offering transaction support for the different operations and scalability of the servers.

Incentivized

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.

Incentivized

• High performance
• Integration with other tools and services
• Big data projects oriented
• High availability and scalability

Incentivized

• 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.

Incentivized

• The learning curve makes it a bit tricky to use at first.
• Documentation is an aspect to improve, especially for people who are just starting out.
• At the interface level, the user experience could be improved.

Incentivized

• 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.

Incentivized

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.

Incentivized

One aspect to improve is the user experience since sometimes the steps to take are not clear and the user may need to review some of the actions before continuing with the next ones. Another aspect to improve is the documentation and support for developers who want to know the tool.

Incentivized

It offers good support for the implementation of solutions in the public and on-premises cloud and integration with other services such as Hashicorp Vault for data encryption. Also, it offers support for different compatible programming languages such as C, C ++, Java, as well as offering good support for the persistence of schema-free data and the possibility of saving data in memory.

Incentivized

At the performance level, it is similar to other solutions such as MongoDB and Percona Server for MySQL. and at the customization level, it offers better support for the development of specific solutions that seek good performance in transactions.

Incentivized

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.

Incentivized

• It offers a good alternative solution compared with other open source databases.
• We have many resources at the documentation level and other tools that help the integration with different programming languages.

Incentivized

• 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.

Incentivized