DATA MESH IN ACTION
Discover the newest paradigm in the data world and see how your company can benefit from it.
DATA MESH is a relatively new concept that became one of the fastest-growing trends during 2020. It extends the paradigm shift that was introduced by the microservices architectures and applies it to data architectures, enabling agile and scalable analytics and machine learning or artificial intelligence.
DATA MESH provides an alternative to the “centralized” organizational and architectural pattern of the data lake with a distributed and decentralized architecture designed to help enterprises to:
- Enable agility and business scalability
- Reduce the time-to-market of the business initiatives
- Lower maintenance costs
- Allow a fair and transparent internal cost allocation.
Even in a rapidly evolving scenario, we have already developed a concrete experience with Data Mesh projects, that we are sharing in the following contents.
Go further and discover more!
Data Mesh Explanation
How and why successful data-driven companies are adopting Data Mesh
The article gives an overview on Data Mesh, its principles, main elements and characteristics.
Reading the text you will understand the basis of the paradigm shift requested for organizations that want to move towards a Data Mesh architecture and how data management needs to change.
Tech Talk #1
Overview and real use cases
In this first episode, Paolo Platter, Agile Lab CTO, talks about some major aspects of the Data Mesh journey.
WATCH THE VIDEO and find the answer to some key questions:
- What is Data Mesh?
- What problems can it solve?
- How to take the first steps?
- Data Lake vs Data Mesh
Tech Talk #2
Organizational changes in the Data Mesh Journey | Part 1
In the second episode, Henrik Göthberg, CEO and Founder at Dairdux Alliance and Organizational Management Expert, and Alberto Firpo, Agile Lab CEO, focus on the main pain-points that organizations have to deal with when starting the journey towards Data Mesh.
WATCH THE VIDEO to discover:
- Which are the internal and external pain points
- How you can work with data integration
- Why a company should choose Data Mesh
- If distributed data are unavoidable
Tech Talk #3
Organizational changes in the Data Mesh Journey | Part 2
In this episode, Henrik Göthberg, CEO and Founder at Dairdux Alliance and Organizational Management Expert, and Alberto Firpo, Agile Lab CEO, focus on Change Management, discussing in depth which are the main challenges for both the IT and Business side.
WATCH THE VIDEO and discover:
- What is the identikit of a company that could start a Data Mesh journey?
- What role does central IT need to play?
- How can a company boost adoption?
Tech Talk #4
Semantic knowledge in the Data Mesh
In this episode, with Juan Sequeda, Principal Scientist at data.world, and Paolo Platter, CTO & Co-founder of Agile Lab, we discuss synergies between the emerging paradigms of Knowledge Graphs and Data Mesh, looking at how they can work together and the main differences in terms of ownership, accountability and data observability.
Tech Talk #5
How Data Mesh enables organizational changes
In this episode, Andrea Faré, Change Choreographer at Leapfrog and Alberto Firpo, Agile Lab CEO and Co-Founder, focus on how companies can leverage Data Mesh to start a radical organizational change and how a Data Mesh Blueprint could help this transformation.
WATCH THE VIDEO and learn:
- What has changed for Enterprise Organizations in the last years, from the organizational and data point of view?
- How a Data Mesh Blueprint could support the chance?
Tech Talk #6
A smooth transition from Data Lake to Data Mesh
In this episode, Jon Cooke, Dataception CTO and Paolo Platter, Agile Lab CTO and Co-Founder, discuss the journey to adopt the Data Mesh paradigm when a company comes from a Data Lake or a Data Warehouse landscape.
WATCH THE VIDEO and learn:
- Which are the main steps that companies need to move?
- What is the difference between data-as-a-product, data product and data-as-a-service?
- How does the cost charging model change?
