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Qubole vs Snowflake: What are the differences?
Introduction
Qubole and Snowflake are two popular cloud data platforms that offer different capabilities for organizations looking to manage and analyze their data efficiently.
Architecture: Qubole is a cloud-based data platform that offers a managed Hadoop environment, along with support for other big data processing frameworks like Spark and Presto. Snowflake, on the other hand, is a cloud data warehouse that is designed for high-performance querying and analytics on structured data. Snowflake's architecture separates storage and compute, allowing users to scale resources independently.
Job Scheduling: Qubole provides advanced job scheduling and workflow management capabilities, allowing users to automate and orchestrate data processing tasks more effectively. In contrast, Snowflake does not natively support job scheduling, and users often rely on external tools or services to manage job execution.
Data Processing Flexibility: Qubole is optimized for processing large volumes of unstructured and semi-structured data using various distributed computing frameworks. Snowflake, on the other hand, is a SQL-based data warehouse that excels at processing structured data for analytics and reporting purposes.
Cost Model: Qubole pricing is based on usage metrics like the number of compute hours and storage consumed, making it suitable for organizations with fluctuating workloads. Snowflake offers a usage-based pricing model as well, but also provides options for fixed-cost annual subscriptions, which may be more cost-effective for organizations with predictable data processing needs.
Data Sharing: Snowflake provides built-in support for secure data sharing between different accounts and organizations, allowing users to easily collaborate and exchange data with external parties. Qubole does not offer the same level of built-in data sharing capabilities, requiring users to implement custom solutions for sharing data securely.
Performance Optimization: Snowflake's architecture is optimized for query performance and can automatically scale compute resources based on workload demands. Qubole requires users to fine-tune resource allocations manually, which may require more expertise and effort to achieve optimal performance.
In Summary, Qubole and Snowflake differ in architecture, job scheduling, data processing flexibility, cost model, data sharing capabilities, and performance optimization.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
Pros of Qubole
- Simple UI and autoscaling clusters13
- Feature to use AWS Spot pricing10
- Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters7
- Real-time data insights through Spark Notebook7
- Hyper elastic and scalable6
- Easy to manage costs6
- Easy to configure, deploy, and run Hadoop clusters6
- Backed by Amazon4
- Gracefully Scale up & down with zero human intervention4
- All-in-one platform2
- Backed by Azure2
Pros of Snowflake
- Public and Private Data Sharing7
- Multicloud4
- Good Performance4
- User Friendly4
- Great Documentation3
- Serverless2
- Economical1
- Usage based billing1
- Innovative1
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