Mario Meir-Huber
The Data Mesh as the end of the Datalake as we know it
#1about 4 minutes
Why large corporations struggle with managing their data
Large enterprises face significant data challenges due to distributed ownership, complex legacy systems, and pervasive data silos.
#2about 5 minutes
The historical evolution from data warehouses to data lakes
Centralized data warehouses proved too expensive and inflexible, leading to the rise of data lakes which introduced new problems with governance and complexity.
#3about 2 minutes
Understanding data mesh as a concept, not a technology
The data mesh is an organizational and cultural blueprint for data handling, not a specific software or platform you can install.
#4about 6 minutes
Addressing the core failures of traditional data approaches
Traditional data strategies often fail by focusing on ETL pipelines and monolithic platforms instead of solving actual business problems.
#5about 4 minutes
Building a distributed and domain-driven data architecture
Data mesh aligns data architecture with business domains using microservices principles, ensuring solutions are simple and tailored to specific needs.
#6about 3 minutes
Leveraging self-serve platforms to accelerate data work
Adopting a self-serve platform design using public cloud services allows teams to focus on solving data problems instead of managing infrastructure.
#7about 2 minutes
Shifting the mindset to treat data as a product
The data-as-a-product principle holds domain teams responsible for the quality, availability, and accessibility of their data for others to consume.
#8about 6 minutes
Defining the essential attributes of a data product
A data product must be discoverable, addressable via APIs, trustworthy, self-describing with metadata, interoperable, and secure.
#9about 1 minute
Data mesh as a solution for modern data challenges
While not a silver bullet, the data mesh framework provides a more effective approach for managing data in large, complex organizations.
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