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  • What is an Open Data Lake?

    With the rise in data lake popularity has come an explosion in the number of data lake vendors and types. Read on to learn why Open Data Lakes are ideal solutions for companies wishing to avoid vendor lock-in, develop additional in-house tools, and want to fully embrace continuous data engineering.

  • 3 Steps to Justify & Reduce the Cost of Your Data Lake

    Now, as business leaders begin to expect returns or justifications on data initiatives, it’s important to ensure you can justify and control the spending related to your data lake. Read on to learn 3 ways you can reduce the cost of your cloud data lake and prove its value.

  • Minimizing Job Failures

    Training and running a machine learning program requires a fast, reliable data architecture. If your data pipeline or data lake isn’t properly optimized, it can cause serious downtime and keep your ML initiative from reaching its KPIs. Check out this white paper to learn how Qubole is able to optimize your machine learning initiatives.

  • The Evolving Role of the Data Engineer

    Instead of preparing static reports for marketers or salesmen, data engineers are finding that their job is changing. Analytics has become a dynamic conversation, and this conversation increasingly involves the cloud, data lakes, and more. Read on to learn more about the new role of the data engineer—and where it’s heading next.

  • Qubole Cost Explorer

    Big data initiatives can be costly to start, maintain, and update. Check out the Qubole Cost Explorer to see how Qubole empowers their users with financial governance tools, ROI breakouts, spend visualizations, and more.

  • Cloud Data Lake Platforms: Buyer's Guide

    Companies seeking to improve their data architecture and prepare it for the changes that will happen in the coming years, should be seriously considering pairing a data lake with cloud technology. Check out this white paper to learn how cloud data lake architectures offer organizations scalable, agile, and adaptable storage for all their data.

  • Building a Modern Data Platform

    Today, industry leaders innovate with the power of data. But collecting data alone isn’t enough to make you an innovator. Read on to learn how you can build a cloud data platform that integrates your data across your ecosystem and creates environments ready for machine learning and analytics.

  • Big Data Engineering for Machine Learning

    Machine learning tools operate differently than other analytics or automated tools; they require access to massive sets of unstructured and structured data, something that traditional data pipelines and engines struggle with. Check out this white paper to learn more about building the right framework for your machine learning-driven enterprise.

  • Financial Governance for Data Processing in the Cloud

    Cloud platforms are powerful tools which allow companies to expand their data storage as they see fit and democratize data access across an organization, but often incur unexpected costs. Read this e-book to learn how your company can institute financial governance in the cloud, and control dangerous data growth and unnecessary resource usage.

  • Machine Learning at Enterprise Scale

    Machine learning presents some unique challenges—the most pertinent being the struggle to apply machine learning processes at scale. In this in-depth, 41-page guide, find everything you need to know about machine learning at enterprise scale.

  • Five Ways to Optimize Data Lake Costs for Ad-hoc Analytics, Streaming Analytics, and Machine Learning

    Open this white paper to find five ways to optimize data lake costs for ad-hoc analytics, streaming analytics, and machine learning.

  • Enabling SQL Access to Your Data Lake with Presto, Hive and Spark

    While SQL is the common langue of many data queries, not all engines that use SQL are the same—and their effectiveness changes based on your particular use case. So what engine is best for your business to build around? Check out this white paper comparing 3 popular SQL engines—Hive, Spark, and Presto—to see which is best for you.

  • Operationalizing the Data Lake

    Many organizations stumbled into having data lakes, erecting them in response to an overload of data and a lack of time to organize it instead of deliberately building a data lake with a specific use case in mind. Read this e-book, Operationalizing the Data Lake, to learn how you can take your data lake and start generating value with it.

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