Why Data Mesh? ft. Ben Stopford

With experience in data infrastructure and distributed data technologies, author of the book “Designing Event-Driven Systems” Ben Stopford (Lead Technologist, Office of the CTO, Confluent) explains the data mesh paradigm, differences between traditional data warehouses and microservices, as well as how you can get started with data mesh.   Unlike standard data architecture, data mesh is about moving data away from a monolithic data warehouse into distributed data systems. Doing so will allow data to be available as a product—this is also one of the four principles of data mesh: Data ownership by domainData as a productData available everywhere for self-serviceData governed wherever it isThese four principles are technology agnostic, which means that they don’t restrict you to a programming language, Apache Kafka®, or other databases. Data mesh is all about building point-to-point architecture that lets you evolve and accommodate real-time data needs with governance tools.Fundamentally, data mesh is more than a technological shift. It’s a mindset shift that requires cultural adaptation of product thinking—treating data as a product instead of data as an asset or resource. Data mesh invests ownership of data by the people who create it with requirements that ensure quality and governance. Because data mesh consists of a map of interconnections, it’s important to have governance tools in place to identify data sources and provide data discovery capabilities. There are many ways to implement data mesh, event streaming being one of them. You can ingest data sets from across organizations and sources into your own data system. Then you can use stream processing to trigger an application response to the data set. By representing each data product as a data stream, you can tag it with sub-elements and secondary dimensions to enable data searchability. If you are using a managed service like Confluent Cloud for data mesh, you can visualize how data flows inside the mesh through a stream lineage graph. Ben also discusses the importance of keeping data architecture as simple as you can to avoid derivatives of data products.EPISODE LINKSData Mesh 101 courseData Mesh 101 with Live Walkthrough ExerciseIntroduction and Guide to Data MeshThe Definitive Guide to Building a Data Mesh with Event StreamsWhat is Data Mesh, and How Does it Work? ft. Zhamak DehghaniDesigning Event-Driven SystemsWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)

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Streaming Audio features all things Apache Kafka®, Confluent, real-time data, and the cloud. We cover frequently asked questions, best practices, and use cases from the Kafka community—from Kafka connectors and distributed systems, to data mesh, data integration, modern data architectures, and data mesh built with Confluent and cloud Kafka as a service. Join our hosts as they stream through a series of interviews, stories, and use cases with guests from the data streaming industry. Apache®️, Apache Kafka, Kafka, and the Kafka logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks.