Using Qubole As The Data Lake For Programmatic Advertising (Adobe Advertising Cloud)

Dive into the transformative world of data engineering with Qubole, where scalability and efficiency meet innovation. Our enlightening session, led by Tom from Adobe, unveils the journey and strategies behind leveraging Qubole as a powerhouse for programmatic advertising through a data lake approach. Discover how we empower businesses to scale their data processing capabilities, ensuring agility and precision in the dynamic landscape of digital advertising.

What You’ll Learn:

  1. Understanding Data Lakes:
    • Grasp the essentials of data lakes, distinguishing between guided and managed approaches, and learn how to navigate the complexities of data management effectively.
  2. The Power of Programmatic Advertising:
    • Explore the intricacies of the advertising ecosystem, from the challenges of matching publisher inventory with advertiser demand to ensuring accurate billing and fraud detection.
  3. Leveraging S3 and Qubole for Scalability:
    • See how integrating Amazon S3 with Qubole facilitates immediate scalability, allowing for the efficient management of vast data volumes, and providing a competitive edge.
  4. Transition to Data Lake Analytics:
    • Understand the transition from traditional data warehousing to data lake analytics, highlighting the benefits of Presto and the importance of cluster capacity planning and tuning for custom reporting.
  5. Investing in Simplicity and Automation:
    • Learn the significance of simplicity in design and the critical role of automation in scaling operations, alongside strategies for effective monitoring and quality assurance of data products.
  6. Cultivating Platform Intelligence:
    • Discover how combining analytics, data engineering, BI, and DevOps within a platform intelligence team can enhance diagnostic capabilities and streamline the identification and resolution of customer issues.
  7. Embracing Change and Continuous Improvement:
    • Gain insights into the importance of adaptability, ongoing education, and the iterative improvement of data processes to keep pace with organizational growth and evolving data landscapes.

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