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Being Data Driven At Stripe With Trino And Iceberg

Data Engineering Podcast

In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. Can you describe what role Trino and Iceberg play in Stripe's data architecture?

Data Lake 147
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What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

Monte Carlo

Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. Data Storage Solutions As we all know, data can be stored in a variety of ways.

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Data Fabric: The Future of Data Architecture

Monte Carlo

Today, as data sources become increasingly varied, data management becomes more complex, and agility and scalability become essential traits for data leaders, data fabric is quickly becoming the future of data architecture. If data fabric is the future, how can you get your organization up-to-speed?

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Data Fabric: The Future of Data Architecture

Monte Carlo

Today, as data sources become increasingly varied, data management becomes more complex, and agility and scalability become essential traits for data leaders, data fabric is quickly becoming the future of data architecture. If data fabric is the future, how can you get your organization up-to-speed?

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

Process 98
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Data Engineering Weekly #161

Data Engineering Weekly

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

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DataOps For Business Analytics Teams

DataKitchen

They need high-quality data in an answer-ready format to address many scenarios with minimal keyboarding. What they are getting from IT and other data sources is, in reality, poor-quality data in a format that requires manual customization. IT-created infrastructure such as a data lake/warehouse).