Qubole Analysis
What is Qubole?
Open data lake platform for analytics and machine learning
Employees
201-500
Founded
2011
Companies Using Product
Expedia (https://www.expedia.com/)
Merkle (https://www.merkleinc.com/)
Acxiom (https://www.acxiom.com/)
Deployment Type
Cloud
Implementation Complexity
Complex
Initial Price
$0.168 (Enterprise Edition), $0.24 (On-Demand)
Product Features & Capabilities
- Open data lake platform for machine learning
- Streaming analytics for real-time insights
- Ad-hoc analytics for faster decision-making
- Data engineering tools for scalable pipelines
- Cloud-native architecture supporting AWS and GCP.
Use Cases
Build and deploy machine learning models at scale; Create real-time streaming data pipelines; Conduct ad-hoc analytics for immediate insights; Automate data engineering processes; Optimize data governance and security.
Other Considerations
Raised significant funding to support growth; Serves a diverse range of industries; Offers a free trial for new users.
Ongoing Support Cost
$0.168 per QCU per hour (Enterprise Edition), $0.24 per QCU per hour (On-Demand), $108 per user per month (On-Demand)
Key Disadvantages
- Fewer ETL tools compared to competitors.
- Cluster management issues reported by users.
Market Position
Qubole positions itself as a leader in the data lake and analytics industry by offering a cost-efficient, open, and secure platform that integrates various data engines for machine learning, streaming, and ad-hoc analytics. The company emphasizes its ability to reduce cloud data lake costs by over 50%, making it an attractive option for organizations looking to optimize their data operations. Qubole's platform supports multiple cloud environments, including AWS and Google Cloud, which helps avoid vendor lock-in and caters to a diverse range of users, including data analysts, engineers, and scientists.
The platform is designed to handle unpredictable big data workloads with features like workload-aware autoscaling and real-time spot buying, enhancing its appeal in a competitive market. Additionally, Qubole's focus on providing a unified data environment that integrates with traditional data warehouses and NoSQL databases further strengthens its market positioning as a comprehensive solution for organizations aiming to leverage big data effectively.
The platform is designed to handle unpredictable big data workloads with features like workload-aware autoscaling and real-time spot buying, enhancing its appeal in a competitive market. Additionally, Qubole's focus on providing a unified data environment that integrates with traditional data warehouses and NoSQL databases further strengthens its market positioning as a comprehensive solution for organizations aiming to leverage big data effectively.
Key Advantages
- Achieve 50% cost savings with built-in TCO optimizations.
- Accelerate continuous data engineering on a single platform.
- Offers a choice of cloud and data processing engines (Apache Spark, Presto, Hive).
- Provides 10 times higher administrative efficiency and 50% lower cloud costs.
- Supports end-to-end feature engineering for data scientists and efficient data pipeline management for data engineers.
- Self-service platform designed for multiple workloads, enabling 3 times faster time to value and 10 times more users per administrator.
- Scalability for unlimited data analysis, processing, and storage.
- High-performance with virtually infinite resources from cloud providers.
- Built-in security leveraging cloud provider expertise.
- Cost efficiencies with pay-as-you-go compute and automation technologies for reduced operational costs.
- Self-service analytics for discovery, ad hoc querying, visualization, and collaboration.
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