Mario Meir-Huber

The Data Mesh as the end of the Datalake as we know it

The era of the centralized data lake is over. Learn how a decentralized data mesh empowers business domains by treating data as a first-class product.

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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Matching moments

Understanding the data mesh as a concept, not a product
04:29 MIN

Understanding the data mesh as a concept, not a product

A Data Mesh needs Open Metadata

Solving centralization bottlenecks with Data Mesh
03:11 MIN

Solving centralization bottlenecks with Data Mesh

Modern Data Architectures need Software Engineering

The evolution from data warehouses to data lakes
02:22 MIN

The evolution from data warehouses to data lakes

Modern Data Architectures need Software Engineering

Deployment models and the vision for a data mesh
03:57 MIN

Deployment models and the vision for a data mesh

GraphQL Mesh – Why GraphQL between services is the worst idea and the best idea at the same time!

Establishing clear data ownership with data mesh
02:13 MIN

Establishing clear data ownership with data mesh

Data Governance in the Era of AI

Understanding traditional data architecture before Microsoft Fabric
02:08 MIN

Understanding traditional data architecture before Microsoft Fabric

Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents

Using DataWorks as a unified IDE for big data
05:05 MIN

Using DataWorks as a unified IDE for big data

Alibaba Big Data and Machine Learning Technology

The challenges of a centralized data lake architecture
04:59 MIN

The challenges of a centralized data lake architecture

A Data Mesh needs Open Metadata

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