Adopt the next stage of data mesh with Fyrefuse

Modern companies recognize that the way they manage data benefits their entire enterprise. This asset has a direct value to the individual business that produces it, but data’s additional potential can be released when it’s shared among business functions.

Unlike most resources, you can use the same data in many places, to obtain more value and craft more accurate AI and machine learning (ML) predictions. Organizations that are good at sharing data internally, fully managing risk associated with it, can realize more value from their data resources than organizations that aren’t.

This is the new starting point companies must focus on: to make data easy to share across the organization, while maintaining appropriate control over it.

What is Data Mesh?

By data mesh we mean a new approach to designing and developing data architectures. Unlike a traditional centralized and monolithic architecture based on a data warehouse/lake, a data mesh is highly decentralized.

Data mesh is used to stitch together data held across multiple data silos, ensuring that data is highly available, easily discoverable, secure within the applications that need access to it.

With data mesh, data is treated as a product, with each source having its own reference manager (who are part of a cross-functional team of data engineers) becoming the fundamental building blocks of a mesh, leading to a domain-driven distributed architecture. Each domain should be discoverable and self-describing across the whole business ecosystem and in many cases will also have a copy of some of the data in a relational database.

The main benefits include cost savings, increase business value and enhance data reuse. The data mesh is capable of addressing all the shortcomings of data lakes by facilitating greater flexibility and autonomy in ownership of data. Final benefits of data mesh translate into a definitive competitive edge over monolithic data architectures.

Fyrefuse and Data Mesh

Fyrefuse is an end-to-end processing and delivery platform that automates and connects stakeholders, tools and policies to improve data life-cycle in a Big data organisation without data duplication.

Our product can help you leverage data mesh in different ways:

• Automated data discovery and exploration for visualising data from various sources to facilitate enterprise-level data visibility and enrichment;
• Simple and reliable data processing through reusable data pipelines using native code-free jobs or reusing custom code, written in multiple programming languages to process data in batches, micro-batches or streams;
• Scalability and performance generated by unleashing the power of Apache Spark on a managed Kubernetes cluster;
• Team management and data ownership providing multi-expert teams with a single source of truth on available data;
• Collaboration, encouraging agility and continuous feedback across people, technology and environments;
• Rules and governance to automatically protect private or sensitive information by erasing or encrypting identifiers that connect personal stored data to support regulatory compliance.

Conclusion

Connecting organizations and data teams to the most relevant data when they need it, without silos or complexity, is what Fyrefuse is designed to do. Data mesh brings domain-ownership to analytical data, giving control back to the teams that create the data. Regardless of the implementation, a data mesh can result in better decisions and a better customer experience for your company.