Possible patterns and
Models created already with the
Incorporated 50 years ago, it is one of the major streaming companies with over 50MM+ subscribers, continuously improving its products and services over the generations.
The client’s Global Data & AI (“DAI”) is a cross-vertical, integrated full-stack data and analytics platform-based organization. We primarily focus on the end-to-end data pipelines to products and services that influence decisions based on analytical modeling and probabilistic and deterministic directions and tendencies.
DAI Mission: Build best-in-class data & AI products and solutions to enhance storytelling & experiences for the client’s audiences globally.
One of the main reasons for choosing AWS platform is that AWS offers native streaming services that enable the enterprise scale with ease. And the company already had its data lake built on AWS S3, so enabling SNS, SQS, S3 Notifications, and Delta-Lake was easy.
Also, AWS provides unmatched breadth and depth of machine learning, big data, and analytics capabilities, enabling clients to choose the right tools for their marketing technology stack. It removes heavy lifting and complexity, and enables to achieve greater cost-efficiency than any other cloud provider.
Architected the solution with best practices and well architect framework.
For ingestion, data pipelines are built corresponding to each ingestion source. This data pipeline will download all the data dumps to S3, transform them using Spark jobs, and load relevant data to snowflake tables.
Once data is loaded into Snowflake, various algorithms are applied to create a unified data set
The unified data set is then loaded into Neptune Graph DB for visualization and query purposes.
CKG has become the go-to source for all the data science teams to get unified content in one place and use it for their machine learning algorithms.
Also, with a large media & entertainment company and the client’s merger in place, CKG has a vital role in managing all the first- party and third-party content metadata and providing stakeholders with descriptive and statistical metadata into a unified schema with the help of an ontology.
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