• Home >

The increase in volume, velocity, and variety of data, combined with new types of analytics and machine learning is creating the need for an open data lake platform. The Open Data Lake platform provides a robust and future-proof data management paradigm to support a wide range of data processing needs including data exploration, ad-hoc analytics, streaming analytics, and machine learning.

Ad-Hoc Analytics

Develop and deliver advanced ad-hoc analytics to the team through our optimized ANSI/ISO-SQL (Presto) service, built-in integrations with Tableau and Looker, and pre-built Git integration for sharing reports and queries. Utilize built-in workbench to the author, save, template, and share reports and queries.

Streaming Analytics

Collect and process stateful events, replay and reprocess data, and integrate with monitoring and alerting solutions. Leverage the platform’s Assisted Pipeline Builder to build streaming data pipelines. Combine streaming data with other streaming or batch data sources to gain real-time insights – all built on an extremely fault-tolerant infrastructure that ensures the accuracy and consistency of your data.

Machine Learning

Build, share, and collaborate on predictive analytical models across the enterprise. Use offline editing, multi-language interpreter, and version control in Qubole to deliver faster results. Leverage the Jupyterlab notebook within Qubole to monitor application status, and job progress, use the integrated package manager, and visualize with Qviz.

Data Engineering

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 data engineering requirements.