What is Reinforcement Learning (RL)? Definition, Algorithms, Examples
In traditional data architecture, data was always centralised in a data warehouse or a data lake and governed by a centralised team.
However, with the rise of distributed systems, microservices, and agile methodologies, this approach has proven to be inadequate for managing data at scale. Data Mesh proposes a different approach, where data is treated as a product and owned by the teams that produce it.
In other words, it's a way of thinking about data management that focuses on decentralisation and autonomy. But how can companies use this to their advantage?
In this episode of the EM360 Podcast, Analyst Christina Stathopoulos spoke to Paolo Platter, Co-Founder and CTO at Agile Lab, about:
- Successful data mesh strategies
- Benefits vs monolithic data lakes
- Customer success stories
Agile Lab works with an array of clients across the globe, creating business vertical solutions to address specific challenges and needs in their field of activity. To hear about their success stories and the ways in which they exceed their clients' expectations, skip to 19:40.
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Meet the panel
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