{"id":14148,"date":"2023-03-06T09:24:04","date_gmt":"2023-03-06T17:24:04","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=14148"},"modified":"2025-06-13T23:00:10","modified_gmt":"2025-06-14T06:00:10","slug":"data-mesh-strategy-couchbase","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/data-mesh-strategy-couchbase\/","title":{"rendered":"Supercharge your Data Mesh strategy with Couchbase Capella"},"content":{"rendered":"

\u201cData as a Product\u201d<\/span>\u00a0<\/span><\/h2>\n

In the face of challenging economics and intensified customer focus, organizations are trying to lower costs and maximize efficiency – and one way they hope to do it is by treating <\/span>data as a product<\/b>, that is, democratizing access to data across their business units.<\/span><\/p>\n

To help achieve this, many organizations are adopting a <\/span>data mesh<\/span><\/a> –\u00a0 a technical architecture that organizes and distributes data from operational sources into business domain specific data lakes without the need to centralize it first – for easier access and utilization, as well as speedier analytic insight. The expectation is that this will make business processes and operational applications more efficient and effective, and help drive better customer engagement.<\/span><\/p>\n

The challenge in achieving a successful data mesh lies in reducing complexity, speeding time to insight, and recirculating this insightful data back into the operational applications that feed the mesh.<\/span>\u00a0<\/span><\/p>\n

The mesh gets messy<\/span><\/h2>\n

The data mesh concept relies on a \u201cmesh layer\u201d that weaves operational data sources and domain specific data lakes together. But the more operational databases and systems that are in play, the more complicated the mesh becomes, and the harder it is to derive value.<\/span><\/p>\n

As the volume of operational data sources increases in the mesh, the SLOWER it becomes, which prohibits real-time analysis and utilization. In essence, the mesh gets clogged, which means more to manage, more complexity, and a bloated data footprint which drives up costs.<\/span><\/p>\n

But when you break them down, many organizations find the operational sources are a hodge-podge of databases that were adopted for specific functionality like transactions, search, profiles and caching, all of which contain overlapping and duplicated information.<\/span><\/p>\n

This data sprawl grew out of the organization’s attempt to serve specific requirements with specific database technologies – because there was no such thing as a multi-purpose database.\u00a0<\/span><\/p>\n

The insight-to-action gap<\/span><\/h2>\n

Another problem with the data mesh is that the underlying data lakes are typically read-only analytic systems that produce static insights which cannot be written back to operational systems. This means there’s still a <\/span>gap<\/b> between data-lake insights and operational action.\u00a0<\/span><\/p>\n

What’s ultimately needed is a way to reduce the complexity of too many operational data sources feeding the mesh, and a way to connect the intelligence from analytic insights back into the operational systems – organizations constantly struggle with closing this insight-to-action gap.<\/span><\/p>\n

Supercharge the data mesh with the right operational database<\/span><\/h2>\n

Reduce data sprawl with multi-model NoSQL<\/h3>\n

Couchbase Capella<\/span><\/a> is a multi-model, developer-friendly database with built in caching, JSON document storage, SQL support, search, eventing and mobile sync. With these combined capabilities, an organization can replace the myriad other operational database technologies with one solution, simplifying the data mesh by reducing operational inputs.\u00a0<\/span><\/p>\n

Get instant operational insights<\/h3>\n

Capella also provides a built-in analytics engine for real-time analysis on specific operational data, results of which can provide on-the-wire-insight without looping through the data mesh. This speeds the overall mesh, as Capella can be used for instant analysis on specific operational data, then feed those results to the mesh for deeper analysis and AI.<\/span><\/p>\n

Close the-insight-to-action gap<\/h3>\n

Capella provides eventing and user defined function features, bringing the ability to script routines that can capture analytic insights from the mesh back into the operational layers.<\/span><\/p>\n

This effectively enables action on insights – if machine learning algorithms on a mesh data-lake come up with a new customer classification based on historical data, you can pull that classification back into the sales app for targeted marketing.<\/span><\/p>\n

Accelerate development<\/h3>\n

Capella allows an organization to consolidate their operational data sprawl down to a database that is easy for developers to work with. SQL++ support, rich SDKs, backend managed services and a fully hosted DBaaS reduce development friction – there’s no server installation or maintenance headaches, and no new languages for developers to learn.<\/span><\/p>\n

The Capella Advantage<\/span><\/h2>\n

Couchbase Capella bridges the gap between operational and analytic sources in the data mesh, where operational insights are fed to the mesh, and derived insights from the mesh are fed back into operational applications – all the way from the cloud to the edge, which leads to more intelligent applications yet cleaner, less expensive architectures.<\/span><\/p>\n