Databricks Data Intelligence Platform vs. Qubole

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Databricks Data Intelligence Platform
Score 8.4 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Qubole
Score 5.1 out of 10
N/A
Qubole is a NoSQL database offering from the California-based company of the same name.N/A
Pricing
Databricks Data Intelligence PlatformQubole
Editions & Modules
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
No answers on this topic
Offerings
Pricing Offerings
Databricks Data Intelligence PlatformQubole
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
Databricks Data Intelligence PlatformQubole
Considered Both Products
Databricks Data Intelligence Platform

No answer on this topic

Qubole
Chose 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.
Top Pros
Top Cons
Features
Databricks Data Intelligence PlatformQubole
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Databricks Data Intelligence Platform
-
Ratings
Qubole
8.3
1 Ratings
6% 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
Databricks Data Intelligence PlatformQubole
Small Businesses

No answers on this topic

IBM Cloudant
IBM Cloudant
Score 8.3 out of 10
Medium-sized Companies
Snowflake
Snowflake
Score 9.0 out of 10
IBM Cloudant
IBM Cloudant
Score 8.3 out of 10
Enterprises
Snowflake
Snowflake
Score 9.0 out of 10
IBM Cloudant
IBM Cloudant
Score 8.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Databricks Data Intelligence PlatformQubole
Likelihood to Recommend
8.4
(17 ratings)
8.0
(1 ratings)
Likelihood to Renew
-
(0 ratings)
6.0
(1 ratings)
Usability
9.5
(3 ratings)
-
(0 ratings)
Support Rating
8.6
(2 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
8.0
(1 ratings)
-
(0 ratings)
Professional Services
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
Databricks Data Intelligence PlatformQubole
Likelihood to Recommend
Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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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
Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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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
Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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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
Databricks
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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Usability
Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
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Qubole
No answers on this topic
Support Rating
Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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Qubole
No answers on this topic
Alternatives Considered
Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
Read full review
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.
Read full review
Return on Investment
Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
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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