Both Qubole and Databricks are solving the same problem – enabling analytics and machine learning on data lakes. Moreover, if you are here because you are evaluating Qubole vs. Databricks or looking for Databricks alternatives, you are at the right place. There are many who love us more than Databricks due to choice and openness, we bring to the table.
“The biggest difference is our approach. Qubole is built on the foundation of openness and flexibility of choice. This manifests in the form of the choice of cloud, hardware, data processing engines, and tools. For example, while we love Apache Spark (and Qubole runs some of the largest Apache Spark clusters in the world), we also incorporate other data processing engines for workload variety and efficiency. More than 75% of our customers use Qubole for multiple workloads ranging from ETL, Streaming Analytics, and Machine Learning.”
Whether you are migrating your on-premises data lake to the cloud or building a new data lake, Qubole offers:
If you are augmenting your cloud data warehouse with a data lake platform, Qubole provides:
Whatever the reason is for replacing your data lake, Qubole has the capability to deliver:
Enable end-to-end feature engineering at enterprise scale.
Address data wrangling, exploration, and model development needs.
Integrate with leading ML workflows, and model deployment tools.
Manage data pipelines efficiently and provide the flexibility of preferred programming language and data processing frameworks (Apache Spark, Presto, Hive, Airflow).
Provide fully automated and optimized infrastructure for SQL and Programmatic (Python, Scala) pipelines
Provide the relevant datasets to have baseline consistency with your analyst peers.
Ensure easy and single-click collaboration with your analyst peers by sharing your findings and model outputs for trend and pattern analysis.
Have ACID compliance and data masking across open source frameworks and public clouds.
Leverage IAM controls of cloud providers to give access rights to users
“Because we let Qubole manage the scaling of our clusters, we also have the ability to specify using spot instances, rather than just everything on demand, and we can tune that. That saves a lot of money.” -Nathan McIntyre, Data Engineer, Ibotta
“The savings from Qubole makes our data engineering team much more productive. Our data engineering team moved away from doing routine maintenance and management work to focusing on serving our customers’ needs and road safety.” -Lei Pan, Director of Engineering, Cloud Infrastructure, Nauto
“We are looking to increase our investment in machine learning-based products multifold, and due to our early partnership with Qubole, we have the data and infrastructure ready to enable that.” -Barkha Saxena, VP Data Science and Analytics, Poshmark
“We were able to scale up quite a lot on Qubole. The reason why is we are able to use Spot instances a lot more with Qubole than with other platforms.” -Dan Peterson, VP of Systems Engineering, Neustar