Data Mesh

Banner tagline: Explore the true potential of your enterprise data with decentralized and scalable data architecture.

Enterprise data environments are constantly evolving and growing in complexity. Siloed data warehouses and data lake architecture have limited capabilities with real-time stream processing and data availability.

The process of ingesting, enhancing, transforming, and serving data from a centralized platform has its own shortcomings like:

Lack of ownership
Lack of ownership
Lack of quality
Lack of quality
Scalability and flexibility
Scalability and flexibility

Eliminate bottlenecks of the centralized architecture with Data Mesh

Harness the power of a domain-driven decentralized architecture for the effective, efficient delivery of impactful data. Facilitate your data-driven organization with enhanced and real-time analytics, including diagnostic, predictive, and prescriptive on your distributed data.

architecture with Data Mesh

Enable data analytics at scale rapidly

Decentralization of data ownership: Business domains such as supply chain, finance, HR, sales, marketing, customer service, etc., should manage the data closest to them. Also, domain teams are responsible for facilitating relevant data to other teams.

Self-serve data infrastructure: Data mesh architecture comes with a self-serve data platform that supports workflows and eliminates friction when connecting different infrastructure data sets. It connects siloed data that stitches together data story and generate impactful analytics.

Cross-functional domain teams: Compared to traditional data architecture methods that promote skill teams isolation that often have long backlogs, Data Mesh proposes a fix whereby domain experts and owners are in charge. It results via increased domain knowledge, closer business, and IT teams, plus agile virtual teams.

Interoperability and governance: Underlying this decentralized architecture is an interoperable layer symbolizing domain agnostic standard. Enabling domain teams to correlate their products, unify them, or perform other operations to balance decisions made locally and imposed globally on all domains.

Faster data delivery: Data specialists need no long waits for getting their requests served. Owing to the governable and centralized infrastructure with the underlying complexity hidden away, faster data delivery with real-time analytics is a possibility.

Data as a product: Data is handled as a high-quality product, meaning the data should be of data quality, formats, and interfaces. Zhamak Dehghani, who introduced the concept of data mesh, also recommends including a domain data product owner responsible for developing, transforming, and serving the domain’s data products in the domain teams.