Explore, build, and deliver data pipelines with ease. Avoid the typical bottlenecks of data ingestion and preparation with a single platform that meets all of your big data engineering requirements. Get intelligent data preparation to support the data needs of all users.
Connect and explore data from a variety of relational and non-traditional databases. Conduct data exploration on unstructured data sets residing on AWS S3, Microsoft Azure Storage, or Google Cloud.
Have a single view of all of your data sources with a single metastore— structured and unstructured — and query any data source using your preferred tool, Qubole notebooks, ANSI SQL, or via API calls.
Process your datasets and build business-critical pipelines consistently and reliably using the cloud data engineering tools and engines of your choice, whether Apache Hadoop, Hive, Spark, Presto, Airflow, or others.
Ingest and process continuously generated data. Execute a variety of time-sensitive applications such as location-based mobile tracking, fraud detection, and real-time customer service interactions with near real-time data.
Have automated repetitive execution of long-standing data preparation and ingestion tasks while allowing users to define custom success or failure criteria.
Schedule multiple commands execution, automate data preparation and ingestion with Qubole Scheduler. Author, schedule, and monitor data pipelines with Qubole Airflow as-a-service.
Review and refine data pipelines with new data and deliver on predefined schedules or on-demand.
Publish data through notebooks, templates, or downstream applications. Use seamless integrations with Github and AWS S3 to run data pipelines.
Free access to Qubole for 30 days to build data pipelines, bring machine learning to production, and analyze any data type from any data source.