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Data Mesh

Easier Data Marts with DreamFactory Data Mesh

Today’s IT teams are struggling to make sense of organizational data that has been compiled piecemeal and often stored within disparate storage solutions. Often this information needs to be aggregated and presented in a unified format, yet pulling data from multiple data sources and displaying it in a coherent way can be onerous and error-prone. The challenge is compounded when the data resides in different databases, and possibly within different clouds.

How Your API Strategy Is Fundamental to Any Data Mesh Strategy

The data mesh approach has gained popularity over the last couple of years as organizations look for reliable ways to break down data silos. At first, data lakes looked like a good way to improve data management and make information more discoverable. Unfortunately, data lakes — and data warehouses — don’t always conform to business needs. They’re often slow and even unresponsive to queries. Potentially even worse, they can still lead to data silos.

API Generation for Data Mesh: Accelerate Your Data Mesh Strategy

Data mesh, it’s one of the hottest data science topics among software engineering teams, data scientists, and anyone interested in building a more effective data infrastructure. This concept is a relatively new model for data management, helping large enterprises scale their data footprint to accelerate digital transformation. Many industries, like retail and banking, see how crucial data is, yet few have mastered ways to harness it. API generation for data mesh is one of the ways you can start.

Practical Data Mesh: Building Decentralized Data Architectures with Event Streams

Why a data mesh? Predicated on delivering data as a first-class product, data mesh focuses on making it easy to publish and access important data across your organization. An event-driven data mesh combines the scale and performance of data in motion with product-focused rigor and self-service capabilities, putting data at the front and center of both operational and analytical use-cases.

How Roche Securely Scales a Data Mesh on Snowflake

As the world's largest biotech company, Roche has goals of doubling patient access to novel diagnostics solutions and achieving medical advances at half the cost to society. Compliantly and securely delivering data products and democratizing data consumption in a decentralized environment are critical to reaching these goals. To support this ambition, Roche's Data & Analytics Team had to solve data access management, security, and governance at scale. Paul Rankin, Head of Data Mesh Platform at Roche, describes how they used Snowflake's security architecture and data marketplace to power Roche's self-service data and analytics platform stack.

API Platform and Data Mesh: Why Bring Them Together

Enterprises are investing in data mesh initiatives to accelerate how decisions are made and to create novel experiences based on machine learning models. Similarly, enterprises are investing in API platform initiatives to productize business domains (or bounded contexts in domain-driven design parlance) as self-service digital assets that accelerate innovation and improve business agility. Both initiatives are typically run as separate work streams.

How Qlik & Microsoft Data Mesh Transforms Supply Chain Management

Qlik Cloud Data Integration with Microsoft Data Mesh simplifies complex global supply chain data challenges. It can transform your supply chain operations and boost your business success, regardless of where the data is stored, from ERP and CRM applications to WMS and proprietary SAP data structures.

The Pros and Cons of Data Mesh vs Data Lake

Data has become the lifeblood of modern businesses, and organizations are constantly looking for ways to extract more value from it. While there isn’t a one-size-fits-all solution for data management, organizations tend to take some common approaches. Two popular approaches to managing data are Data Mesh and Data Lake. Data meshes and data lakes have recently become popular strategies for groups that want to avoid silos so they can make data-driven decisions.