Overview
What is Qubole?
Qubole is a NoSQL database offering from the California-based company of the same name.
Hadoop as a Service without vendor lock-in
Pricing
What is Qubole?
Qubole is a NoSQL database offering from the California-based company of the same name.
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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Alternatives Pricing
What is Amazon DynamoDB?
Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.
What is MongoDB?
MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format…
Product Demos
Real-Time Qubole Data Science Demo for Retail - Data Science Festival
LIVE DEMO: Stop The Cloud Cost Madness! Graviton, AWS and Qubole will Reduce Your Data Lake Costs
Qubole On-Demand - Ad-Hoc Analytics Demo
QUBOLE LIVE DEMO: Helping Data Engineers Operationalize Complex Streaming
Qubole On-Demand - Machine Learning Demo
Qubole On-Demand - Data Engineering Demo
Product Details
- About
- Tech Details
What is Qubole?
Qubole Video
Qubole Technical Details
Operating Systems | Unspecified |
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Mobile Application | No |
Comparisons
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Reviews and Ratings
(8)Community Insights
- Business Problems Solved
- Pros
- Cons
- Recommendations
Qubole has proven to be a valuable tool for users across various industries, enabling them to query and analyze massive amounts of data in a Hadoop environment with impressive speed and efficiency. Users have leveraged Qubole to access AWS BigData Cluster, allowing for insights and analysis of big data. The software has been widely used for production ETL jobs, machine learning projects, and end-to-end ML workflows, providing ease of use and seamless integration. Moreover, Qubole has helped users save costs and resources by efficiently maintaining and cleaning big data. It has also been utilized for click stream analytics, improving product offerings for customers. The software's success extends into machine learning, streaming, and ad-hoc analytics, where it has improved success rates in ML models. With Qubole, users have been able to seamlessly work with big data from various sources, making it available across the organization. Additionally, Qubole notebooks have been used extensively for data analysis on large sensory data sets, aiding in business decision-making. Overall, the software's use cases span analytics, data quality, data processing, and much more.
Transparent Culture: Users have praised Qubole for its transparent culture, with several reviewers mentioning this aspect. They appreciate the open and honest communication within the company, which fosters a sense of trust and transparency between employees and management.
Great Customer Focus: Many users have highlighted Qubole's great customer focus as one of its key strengths. They feel that the platform truly values its customers and goes above and beyond to meet their needs. Reviewers mention that they receive excellent support and prompt responses from the customer service team.
Innovative Platform: The innovative platform offered by Qubole has been highly regarded by users. Multiple reviewers have mentioned how impressed they are with the features and usability of the platform. They find it easy to use, thanks to its user-friendly interface, and appreciate the ability to manage big data programmatically.
Confusing and Not User-Friendly Interface: Many users have expressed frustration with the user interface of Qubole, stating that it is confusing and not user-friendly. They have found tasks like executing queries and using the notebook feature to be difficult and time-consuming.
Cluster Management Issues and Limited ETL Tools: Some users have found cluster management in Qubole to be problematic at times. Additionally, they have mentioned that Qubole has fewer ETL tools compared to its competitors, which can be a drawback for certain data processing tasks.
Unreliable Scheduling System: Users have reported that the scheduling system in Qubole is not reliable, leading them to host their own Airflow instance. This lack of reliability can cause disruptions in their workflow and impact overall productivity.
Users have made the following recommendations about Qubole:
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Take the time to learn and understand the Qubole platform. Users suggest using the trial period to become familiar with Qubole and its interface. Once users are comfortable with the platform, they find it very easy to use for data analysis and development. It is recommended to engage with Qubole's teams to understand the potential return on investment (ROI) and address any security requirements during implementation. Additionally, users advise doing thorough research to determine if a fully integrated platform is truly needed before considering Qubole.
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Consult with Qubole support and focus on business goals. Users recommend utilizing Qubole's support team at the beginning of their journey. They suggest having a Virtual Private Cloud (VPC) setup and choosing the appropriate scale for their needs. It is also suggested to allow Qubole to manage the infrastructure while focusing on business objectives. The user highly recommends consulting with Qubole's support team and considers them helpful in navigating the platform.
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Explore the features and consider specific use cases. Users recommend exploring all the features available in Qubole to leverage them in finding suitable solutions. They find Qubole particularly useful for handling Hive queries and suggest testing specific use cases for notebook use. Furthermore, it is advised to evaluate Qubole for real use cases and consider parallel processing and Spark workflows as prerequisites for adopting Qubole as a solution. Users also suggest utilizing the dashboards provided by Qubole for efficient data analysis.
Overall, users find value in using Qubole as a data lake platform, especially for financial analysis, and appreciate its emerging status that brings together various big data processing technologies under one roof. However, there is room for improvement in areas such as query processing speed, customer support, and documentation.
Attribute Ratings
Reviews
(1-1 of 1)Hadoop as a Service without vendor lock-in
From what I've seen, Qubole abstracts away the setup, scalability, and installation of many Hadoop services by providing an a la carte offering of big data processing services from query engines of Hive, Spark, and Presto to useful UI tools of the query editors and Zeppelin Notebooks.
- 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.
- 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.
- Performance
- 70%7.0
- Availability
- 60%6.0
- Concurrency
- 80%8.0
- Security
- 70%7.0
- Scalability
- 100%10.0
- Data model flexibility
- 100%10.0
- Deployment model flexibility
- 100%10.0
- 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.