Cloud-based data lakes provide best ROI, enabling auto-scaling depending on workloads. Qubole provides an integrated, highly resilient environment, and reduces many problems of on-premises approaches.

How big data engines are used for exploring and preparing data, building pipelines, and delivering data sets to ML applications

Tips on how to choose the best SQL Engine for your use case and data workloads

Rapidly deploy analytics and machine learning with Apache Spark, Hadoop, and Presto with Qubole on the Google Cloud Platform.

Learn more about Workload-Aware-Auto-Scaling-- an alternative architectural approach to Auto-Scaling that is better suited for the Cloud and applications like Hadoop, Spark and Presto.

How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.

Key criteria to consider when evaluating cloud data platforms

Security strategies to protect your information and how our security model works with AWS

Security strategies to protect your data on Microsoft Azure

TDWI drills into the data, tools, and platform requirements for machine learning