Case Study

GAIA Case Study

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CASE STUDY Business Problem Overview Gaia's business model depends on engaging viewers with unique and innovative streaming content. So, recommending the right content, to the right viewers, is critical. But the company's legacy SQL rule-based recommendation engine "was very slow, very tedious, and not very accurate," says senior data engineer Patty Vonick. It did little to drive viewer engagement, Vonick added. The company also had another problem: its technology architecture—which centered around an on-premises single- instance server and a Postgres data warehouse—was not sufficiently flexible or robust. Specifically, it couldn't provide users access to data from different sources and could not handle the data workloads. In addition, modeling processes took too long, and frequent outages led to hours of delays and debugging. For example, some jobs had to be scheduled to process overnight, but these often ran long. So,when employees generated queries and About Gaia Gaia is a member-supported streaming video subscription service available in 185 countries around the world. Using a powerful combination of modern technology and ancient traditions, Gaia produces and curates transformational video content that includes guided yoga and meditation instruction, as well as series and films covering a wide variety of topics, from health and longevity to human transformation and science, all of which aim to empower the evolution of consciousness. CASE STUDY Gaia Boosts Subscriber Engagement and Data-Driven Decision-Making with Qubole Industry: Media reports the next day, they would compete with the overnight jobs for compute resources. As a result, the system would overload, adding to the backlog and even resulting in failures. And when jobs failed, it could take hours or even the whole day to troubleshoot them. Gaia needed to solve these problems. It also needed to adopt more state of the art practices like data analytics and machine learning. The company turned to Qubole for assistance implementing a data lake platform and migrating from the outdated data warehouse infrastructure.

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