Data Mesh is a relatively new concept that was 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 that's 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.
Data Mesh is a journey that doesn't always start from 0. Many enterprise already have pieces in place that could contribute to a successful Data Mesh architecture. Before putting the pieces together, you have to know where you're at in your Data Mesh journey. Find out today, with the Data Mesh Readiness Assessment.
The Practical Guide to Data Mesh Implementations
This whitepaper explores how businesses can identify, avoid, and solve the most common obstacles for scaling a successful Data Mesh initiative.
It also covers how Witboost helps an organization solve these challenges, maintain a technology-agnostic approach and embrace all the core Data Mesh principles for a functional Data Mesh implementation delivered faster and with lower risk than other approaches.
Starting with the gradual implementation to demonstrate its potential and quick time-to-market and ending with consolidation across many years, organizations can use this 3-phased approach to implementing Data Mesh and assessing its value by themselves.
The whitepaper delves into:
-
The challenges of each obstacle
-
The impacts of each obstacle
-
The solution to each obstacle
Implementing Data Mesh Initiatives
Paolo Platter, CTO at Agile Lab discusses data mesh, from basic principles to use-case specifics, with Christina Stathopoulos, Data Whisperer and Data Expert.
- Who we are
- 4 Pillars of Data Mesh
- Challenges in implementing Data mesh for enterprises
- Data Mesh Benefits (vs. monolithic models)
- Client Use Case: Our first data mesh implementation
Data Product Flow – How To Discover and Create Your Data Products
Interviewed by Scott Hirleman, the founder of the Data Mesh Learning Community, Paolo Platter talks about Data Product Flow, the methodology that Agile Lab is currently using to help customers moving their organizations to Data Mesh.
Technology Abstraction in the Data Mesh
Scaling Data Mesh in Large Organizations
This contribution wants to shed a light on some of the limits that a Data Mesh implementation will experience sooner or later.
How to model Data Products
When it comes to modelling Data Products people go into panic. There are no clear rules, only some conceptual and not actionable indications.
Data Mesh Monoliths Integration Patterns
This article focuses on the technical issues of a company that is in the stage of breaking data monoliths to enable inbound and outbound integration with the Data Mesh.
Powerup Metadata, Automate Data Mesh
Dive into our practical approach when it comes to metadata. It's open source and used by many of our clients to enable much-needed automation for their Data Mesh journey.
