The GCP platform enables data scientists and data engineers to collaborate on building scalable data pipelines using native interfaces, built-in tools, and optimized data processing engines. Find out how Qubole on GCP can provide you with a:
- Unified and rich experience with built-in end-user tools such as native notebooks, an integrated workbench for commands, and in-built connectors to multiple data sources.
- Highly optimized versions of data processing engines such as Apache Spark, Hive, and Airflow for better performance and efficiency.
- Enterprise support for data processing engines such as Apache Spark, Hive, and Airflow by specialized engineering teams focused on each engine.
- Advanced automation and cluster lifecycle management that enables cloud-scale deployments within fixed budgets while maintaining low administrative overhead.
In addition to this, watch a demonstration on how to:
- Drive down the costs of your big data workloads by 50%
- Utilize Qubole for your data engineering, analytics, and data science use cases
- Increase efficiency by leveraging cloud technologies that match your workflows
- Extract more value with Machine Learning and AI
- Optimize Apache Spark, Presto, Hadoop/Hive, and Airflow usage
- Use Jupyter and/or Zeppelin Notebooks with Qubole
Explore Qubole to get answers to the big data, analytics, and machine learning questions that are important to you.