Webinars

  • Succeeding with a Cloud Data Lake - from Architecture to Operations

    Succeeding with a Cloud Data Lake - from Architecture to Operations

    Learn best practices for building a cloud data lake operation, from people and tools to processes, in this webinar.

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  • UPCOMING: Mastering Data Governance on Cloud Data Lakes with Multiple Engines

    UPCOMING: Mastering Data Governance on Cloud Data Lakes with Multiple Engines

    Qubole data privacy and integrity experts cover how to maintain data integrity and privacy of data residing in data lakes using various open-source engines.

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  • Mastering Data Discovery on Cloud Data Lakes

    Mastering Data Discovery on Cloud Data Lakes

    Best practices for working with different datasets, and when to use Apache Spark, Presto and other engines

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  • Comcast, Fanatics and MediaMath at Data Platforms 2018

    Comcast, Fanatics and MediaMath at Data Platforms 2018

    Comcast, Fanatics and MediaMath discuss their successes and challenges creating a data-driven enterprises

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  • Introduction to Qubole: A Data Platform Built To Scale

    Introduction to Qubole: A Data Platform Built To Scale

    Qubole SVP of Product Mohit Bhatnagar shares how Qubole’s cloud-native platform helps companies scale their operations, activate petabytes of data, and reach admin-to-user ratios as high as 1:200

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  • Leveraging Streaming and Batch Data Sets for ML Applications

    Leveraging Streaming and Batch Data Sets for ML Applications

    Learn how to use Qubole to acquire and transform data sets for data science and analytics, make data sets available to different users, and fully leverage your data lake.

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  • Key Differences Between On-Prem and Cloud Data Platforms

    Key Differences Between On-Prem and Cloud Data Platforms

    Learn the key differences between on-premise and cloud solutions, benefits of cloud data lakes and data warehouses, and how to build the right architecture for your analytics and ML needs.

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  • How To Build Scalable Data Pipelines for Machine Learning

    How To Build Scalable Data Pipelines for Machine Learning

    Common challenges faced by data engineers when building pipelines for ML and how to address them

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  • Data Engineering Pitfalls and How to Avoid Them

    Data Engineering Pitfalls and How to Avoid Them

    Simple, practical solutions for common challenges faced by data engineering teams

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  • Speed to Value: How To Justify Your Big Data Investments

    Speed to Value: How To Justify Your Big Data Investments

    How best-in-class companies are generating rapid value from big data while also managing costs

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  • Succeeding with Big Data Analytics and Machine Learning in The Cloud (451 Research)

    Succeeding with Big Data Analytics and Machine Learning in The Cloud (451 Research)

    451 Research covers best practices such as using automation, enabling collaboration, and financial governance

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  • How To Increase Value from Machine Learning and Advanced Analytics on Azure

    How To Increase Value from Machine Learning and Advanced Analytics on Azure

    How to modernize your architecture with data lakes and data warehouses on the cloud

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  • Keeping Costs Under Control When Processing Big Data in the Cloud

    Keeping Costs Under Control When Processing Big Data in the Cloud

    How to iIdentify areas of cost optimization to drive maximum performance for the lowest TCO

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  • Best Practices for Moving Big Data from On-Prem To The Cloud

    Best Practices for Moving Big Data from On-Prem To The Cloud

    What Cloudera, Hortonworks or MapR customers should consider when moving to a cloud-native platform

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  • Modern Data Engineering and The Rise of Apache Airflow

    Modern Data Engineering and The Rise of Apache Airflow

    Brief introduction to Apache Airflow, its optimal use cases, and real-world examples

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  • Using Qubole Presto for Interactive and Ad-Hoc Queries

    Using Qubole Presto for Interactive and Ad-Hoc Queries

    Tips for when to use Presto versus Apache Spark, and how to enable self-service access to your data lake

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  • Running Apache Spark at Scale in the Cloud

    Running Apache Spark at Scale in the Cloud

    Deep dive into the use cases for Apache Spark on Qubole, including ETL and machine learning

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  • Migrating to a Modern Cloud-Native Data Lake with Microsoft Azure and Qubole

    Migrating to a Modern Cloud-Native Data Lake with Microsoft Azure and Qubole

    Benefits of migrating to a cloud-native data lake and how to choose the right data architecture

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  • Enterprise-Scale Big Data Analytics on Google Cloud Platform

    Enterprise-Scale Big Data Analytics on Google Cloud Platform

    Why a unified experience with native notebooks, a command workbench, and integrated Apache Airflow are a must.

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  • Why You Need a Cloud Platform to Succeed with Big Data

    Why You Need a Cloud Platform to Succeed with Big Data

    The benefits of a single cloud platform and centralized access to data

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