Fanatics Ingests Streaming Data to a Data Lake on AWS

Fanatics, a popular sports apparel website and fan gear merchandiser, needed to ingest terabytes of data from multiple historical and streaming sources  transactional, e-commerce, and back-office systems to a data lake on Amazon S3. Once ingested, the data would be analyzed to better identify, predict, and fulfill customer needs related to the products Fanatics offers in over 300 online and offline stores.
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Spotlight

OTHER ON-DEMAND WEBINARS

DataOps and Cloud Data Ecosystems: Overcoming the 3 Biggest Hurdles

The cloud is changing the way we use data and at the same time, the way we use data is changing the cloud. Organizations are adopting multiple cloud data platforms to maximize data’s value, but in doing so are often increasing complexity for data engineering and DataOps teams that may stifle the very reason for investing in those platforms.
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Democratizing Data Science With No-Cod

The pace at which the field of data science is expanding makes it impossible for the enterprises to catch up to the state of the art solutions. To avoid falling short of the demands of burgeoning digital revolutions, solutions like APIs, drag and drop analytics, low code and even no-code have been developed. For instance, no-code tools bridge the gap between domain expertise and niche skills of the developers. Know more about why enterprises are increasingly leaning on no code solutions in the upcoming webinar being organised by Analytics India Magazine in association with HP.
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Logical Data Fabric Powered by Data Virtualization: An Overview

According to a recent Forrester survey, while 85% of organizations want to improve their use of data insights in their decision-making, 91% of the respondents report that they are not able to improve upon their data-driven decision-making.
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Empowering Data Scientists with MLOps

Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of cnvrg.io will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.
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