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Data Mesh


Is Data Mesh the Right Framework for Your Data Ecosystem?

With the ever-increasing volume of data being generated from a highly diverse set of data sources, organizations have started to increasingly direct their focus on solutions that can help them with data management more efficiently and effectively. Indeed, in the current decade, having a robust data infrastructure is key to an organization’s success, and timely data-driven decision-making is what every management is striving for today.


Integrating Your Data Warehouse and Data Mesh Strategies

Data warehousing requires data centralization, whereas data mesh enables a decentralized approach to data access. Organizations might think that the solution to their data management strategy requires a choice between the two, but the reality is that both approaches can and should co-exist.


Data Management and the Four Principles of Data Mesh

A relatively new term in the world of data management, data mesh refers to the process of creating a. This process can happen in several ways, giving business users easy access to the data they require for decision-making. Several principles guide data mesh design and implementation. This article will discuss the principles of data mesh and how they can help your business get the most out of its data.

Modern Data Architectures | Data Mesh, Data Fabric, & Data Lakehouse

For years, companies have viewed data the wrong way. They see it as the byproduct of a business interaction and this data often ends up collecting dust in centralized silos governed by data teams who lack the expertize to understand its true value. Cloudera is ushering in a new era of data architecture by allowing experts to organize and manage their own data at the source. Data mesh brings all your domains together so each team can benefit from each other’s data.

Driving Business Value from a Data Mesh Approach

Irrespective of what it’s called, the market has talked about what amounts to data mesh for several years. The concept of decentralized data management that is driven by business domains helps support the need for business-focused data outcomes. It also helps place value on where the value of data projects should be - on business needs. Data driven organizations need to look at business domains as a way of organizing the various desired outcomes of analytics and data movement initiatives.


From Data Lake to Data Mesh: How Data Mesh Benefits Businesses

Current data architecture is going through a revolution. Enterprises are starting to shift away from the monolithic data lake towards something less centralized: data mesh. Data mesh is a relatively new concept, first coined in 2019, that addresses potential issues with data warehouses and data lakes that can cause businesses to be slow, unresponsive, or even suffer from data silos. Data mesh benefits are able to provide a wealth of advantages to your business.


Choosing The Best Approach to Data Mesh and Data Warehousing

Data mesh is being talked about a lot to describe the way data is managed across the organization. But what does it really mean for your organization’s data management strategy and how can its framework support your business needs and drive data pipeline success? On a high level, data mesh is about connecting and enabling data management across distributed systems.


Data Mesh Architecture: Understanding the Four Key Components

Organizations worldwide put their best foot forward to create a centralized database where information is gathered, stored, and managed. Their data engineers transform difficult-to-decipher datasets into data pipelines that can be used by data scientists, analysts, and consumers. However, the new data mesh concept championed by Zhamak Dehghani, the director of technology for IT consultancy firm ThoughtWorks, allows domain teams to conduct cross-domain data analysis independently.


The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Data teams have the impossible task of delivering everything (data and workloads) everywhere (on premise and in all clouds) all at once (with little to no latency). They are being bombarded with literature about seemingly independent new trends like data mesh and data fabric while dealing with the reality of having to work with hybrid architectures. Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem.


Data Mesh Architecture Through Different Perspectives

We previously wrote how the data mesh architecture rose as an answer to the problems of the monolithic centralized data model. To recap, in the centralized data models, ETL or ELT data pipelines collect data from various enterprise data sources and ingest it into a single central data lake or data warehouse. Data consumers and business intelligence tools access the data from the central storage to drive insights and inform decision-making.