Oracle NoSQL Database vs. Qubole

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Oracle NoSQL Database
Score 10.0 out of 10
N/A
Oracle offers a NoSQL Database.N/A
Qubole
Score 5.5 out of 10
N/A
Qubole is a NoSQL database offering from the California-based company of the same name.N/A
Pricing
Oracle NoSQL DatabaseQubole
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Oracle NoSQL DatabaseQubole
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Oracle NoSQL DatabaseQubole
Top Pros
Top Cons
Features
Oracle NoSQL DatabaseQubole
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Oracle NoSQL Database
-
Ratings
Qubole
8.3
1 Ratings
5% below category average
Performance00 Ratings7.01 Ratings
Availability00 Ratings6.01 Ratings
Concurrency00 Ratings8.01 Ratings
Security00 Ratings7.01 Ratings
Scalability00 Ratings10.01 Ratings
Data model flexibility00 Ratings10.01 Ratings
Deployment model flexibility00 Ratings10.01 Ratings
Best Alternatives
Oracle NoSQL DatabaseQubole
Small Businesses
IBM Cloudant
IBM Cloudant
Score 9.3 out of 10
IBM Cloudant
IBM Cloudant
Score 9.3 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 9.3 out of 10
IBM Cloudant
IBM Cloudant
Score 9.3 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 9.3 out of 10
IBM Cloudant
IBM Cloudant
Score 9.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Oracle NoSQL DatabaseQubole
Likelihood to Recommend
7.3
(1 ratings)
8.0
(1 ratings)
Likelihood to Renew
-
(0 ratings)
6.0
(1 ratings)
User Testimonials
Oracle NoSQL DatabaseQubole
Likelihood to Recommend
Oracle
Oracle NoSQL Database is well-suited for you if your data formats are not consistent, if you have limited hardware resources, if you higher data throughput (whether the database is on the cloud or running locally), and if you don't need a declarative query language to maintain a standardized schema of your data. If you need reduced data redundancy and require ACID compliance, you are better off finding an SQL database solution.
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Qubole
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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Pros
Oracle
  • Data-model flexibility. Unlike RDBMS solutions, Oracle NoSQL does not restrict you to a predefined set of data types.
  • Ability to Handle an Increased Amount of Traffic. As Oracle NoSQL can process queries much quicker than Oracle Database, Oracle NoSQL is able to respond to a lot more queries in the same amount of time.
  • Data-model simplicity. In SQL-oriented databases, there is a learning curve in learning the relationship between databases, tables, rows, and keys. On the other hand, Oracle NoSQL's key-value based storage is much easier to get the hang of.
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Qubole
  • 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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Cons
Oracle
  • Fewer analytical functions to choose from. When compared to Oracle Database, there is significant difference in the amount of built-in analytical functions.
  • Eventual data consistency. It is not guaranteed that a write or delete query will be immediately visible for subsequent queries.
  • Data redundancy. As there are no mechanisms that insure data integrity, users are more likely to have redundant data across their documents.
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Qubole
  • 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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Likelihood to Renew
Oracle
No answers on this topic
Qubole
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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Alternatives Considered
Oracle
I have not used any other types of NoSQL databases.
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Qubole
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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Return on Investment
Oracle
  • We pay less for computing resources, as Oracle NoSQL databases respond quicker than our previous SQL databases.
  • Our database administrators and software developers do not need to worry about "data massaging" and can focus on perfecting application logic.
  • Oracle NoSQL has built-in integration to other Oracle products, so we didn't not need to spend money on building custom integrators or higher additional developers.
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Qubole
  • 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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ScreenShots