What do a boutique shop and data mesh have in common? Boutique shops carry specific niche items catered to targeted clientele. They know their products well and consistently deliver a personalized shopping experience for their customers.
In the event of product problems, you know exactly who to address for a resolution. You may even request new products or product customization.You will have a unique and tailored buying experience shopping at a boutique compared to a big-box retailer.
Data mesh mimics a lot of these ideas about a tailored customer experience and enterprise customers can use this idea in how they share and consume data. These days many customers prefer boutique shops over large retailers because they provide exactly what they want with a great buying experience. Similarly, data mesh is focused on moving away from a centralized approach of data sharing, to a decentralized approach of data sharing.
Boutique shops excel in providing details about their products’ source of raw materials – for instance, if their goods are responsibly sourced. In a data mesh, a data product is produced by a department and made available for users with all the metadata required for review and comprehension before subscribing to use it. Data mesh is an approach to build data products by the team who specialize in that data, provide detailed metadata and use software technology to look up data products in a self service fashion.
Honing in on “boutique” data mesh
Digitization is growing, and we’re producing data at a much faster rate than systems are prepared for. Unless the data is analyzed to help your business grow, storing large amounts of structured and unstructured data can prove fruitless. AI modeling language algorithms require large amounts of data from various sources, but it’s not just AI that is hungry for data – analytics and business intelligence tools rely heavily on data for generating metrics, dashboards, and reports.
Because of this, it’s important to have an approach that revolves around sharing data across your organization in a way where it’s self-service for users and provides seamless accessibility. Faster access to data means better, faster decision-making, catapulting you past your competitors. But for all this to happen, data must be secured, trustworthy, from viable sources, with accurate datasets, and have the necessary metadata available to access as a self-service method. Sounds like a boutique shop for data, doesn’t it?
Point-to-point integrations are bad for data mesh
Creating data products requires data collection, cleansing, and curation. That said, raw data is generally spread across numerous data sources. The data may have to hop through various security boundaries before it is ready to be processed by an AI or an analytics platform.
The general approach to get the data required for building a data product is to use point-to-point integrations, which often relies heavily on vendor-provided cloud storage. Many integrations written this way end up as essentially cloning the data to the data source. Doing this means you’ll end up with point-to-point integrations that are brittle, have little or no reusability, and require a plethora of cloud storage buckets to keep track of. Integrations become hard to manage and tough to troubleshoot – adding significant operational and maintenance costs on top of development and cloud storage costs.
How can you build flexible, reusable, and maintainable integrations?
Many MuleSoft customers have successfully built API-based application networks, allowing them to plug in and out of any data source, perform data ingestion, reuse APIs, and support resilient operations. By doing so, users can easily extend applications networks to various stages of building, exposing, and consuming data products.
In the data ingestion stage, data must be sourced from disparate applications. On Anypoint Exchange, you can find MuleSoft greater than 150+ connectors to connect to various data sources.
The various out-of-the-box functions for data cleansing, transformation, and aggregation simplifies the data ingestion process to create data products. Data products can be created in diverse formats and accessed through various means, including real-time data streams via RESTful APIs or event-based queues, as well as batch-style products stored in files or relational/non-relational databases.
MuleSoft offers a comprehensive solution for exposing data products as APIs. With MuleSoft Anypoint Exchange, you gain access to an API catalog, a collaborative platform for metadata capture, and self-service capabilities for API exploration. While security policy enforcement and computational governance are central to this stage, their influence may extend to both data ingestion and data product creation phases.
Complexity is overrated; simplicity is key
At the core of the data mesh is the data. Data integrations become complex if you let them become that way. To lean into simplification, MuleSoft’s Anypoint Platform offers a unified, end-to-end API lifecycle solution, equipped with all the essential features needed to construct API-based application networks.
Anypoint Platform empowers users with enhanced capabilities and flexibility to swiftly create data products. Why spend valuable time and effort with point-to-point integrations when you can leverage a low-code platform to build agile, reusable integrations for creating and exposing data products?
