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

Qubole's buyer guide about how cloud data lake platform helps organizations to achieve efficiency & agility by adopting an open data lake platform and why data lakes are moving to the cloud

A Whitepaper of Qubole that how it passionate about making data easily accessible for open data lake platforms while using Amazon AWS for our customer's data with proper security measures & compliance

How to position the data lake expenditure to finance.

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

Three part journey of how customer implemented spot nodes and now uses as part of daily routine causing reliability issues, delays, and troubleshooting problems

Every organization can benefit from data, used adeptly in coordination with the organization’s goals

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