Welcome to the second episode of Analytics Bytes: A video series where we explore common Startups analytics use cases and how to architect for them. This video introduces the concept of a serverless data lake, the foundation of a modern data architecture. A serverless data lake architecture enables agile and self-service data onboarding and analytics for all data consumer roles across a company.
Today we'll introduce the concept of Modern data architecture and how Startup customers can leverage the mo...
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Attend this session to learn how migration to next-generation processors can dramatically improve performance of your workloads.
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Join VCs and investors in blockchain technology as they explore the reasons you would choose to launch a Web3 startup and how you would apply Web3 technologies to an already existing startup.
Curious why blockchain is essential to enable startup scalability? Join Aptos Labs’ CTO and co-founder Avery Ching and CEO and co-founder Mo as they detail their company’s growth journey.
Founders and operators are utilizing blockchain and Web3 infrastructure tools to build more robust and successful startups—and you can, too.
One of the most exciting aspects of Web3 is its potential to democratize opportunity and ownership of the internet. But where exactly are we headed? Join this session to learn more.
Attend this session to hear their thoughts on the power of community, diversity, and why it’s important to be proactive when it comes to bringing diverse founders into Web3.
Today we'll introduce the concept of Modern data architecture and how Startup customers can leverage the modern data architecture to think big, start small and scale fast on their data journey
Stream processing allows analytical insights to be gathered and acted upon instantly on high volume, high velocity real-time data.
This video describes the need for transactional data lake and explores how to run successful transactional data lakes on AWS.
This video describes the usage of data warehouse in modern data architecture and discusses common use cases and reference architectures to ingest and query structured and semi-structured data.
In this session, learn how to use Amazon SageMaker service and features to do data preparation, feature engineering, and set up datasets for ML training.
In this episode we look into the vast amount of human generated and machine generated data, and the tremendous opportunity to gain valuable insights from them.
Learn how the new Hugging Face Deep Learning Containers make it easier than ever to train Transformer models on Amazon SageMaker.