Data Mesh in a Nutshell Data Mesh reimagines data architecture by decentralizing data ownership and focusing on making data available as a product. Instead of centralizing all data into a monolithic organization and process, it distributes responsibility across data domains—teams or departments that treat their own data as a product and are accountable for its quality, accuracy, and availability. The Four Key Pillars of Data Mesh Domain-Oriented Data Ownership: Each domain (business unit or team) manages its own data, ensuring that the experts closest to the data are responsible for it, which fosters accountability and relevance. Data as a Product: Data is treated like a product, with clear documentation, APIs, and service-level agreements (SLAs) to ensure it is usable, discoverable, and reliable for consumers within and outside the domain. Self-Service Data Infrastructure: Data Mesh promotes self-service tools and platforms, empowering domains to easily publish, access, and consume data without heavy reliance on a central IT team, thus boosting agility and scalability. Federated Computational Governance: To maintain security, compliance, and quality across decentralized domains (data silos), Data Mesh implements federated governance—a set of policies, agreements, and standards enforced automatically through code (computational governance). This ensures consistency across the organization without compromising domain autonomy. Microservice Architecture: A Brief Overview Microservices are built around several key principles: ...
Beyond the Lakehouse
Why Organizational Evolution is Key to Data Success Why Data Projects Fail: A Call for Change in Organizational and Process Thinking In the race to solve data challenges, many companies rush to adopt new technologies such as Lakehouse architectures, believing they are the magic bullet. However, the frequent failure of data projects shows that technology alone cannot fix organizational and process issues that are deeply entrenched in outdated, centralized structures. While enterprises often focus on the latest tools and platforms, they overlook the importance of evolving their organizational culture and processes to be agile and distributed—essential traits for thriving in today’s data-driven landscape. ...
Iceberg Catalog
The TIP of your Lakehouse TL;DR The Iceberg Catalog landscape is evolving rapidly with significant announcements from Snowflake and Databricks. Adding to this vibrant ecosystem, HANSETAG introduces TIP, a Rust-native Iceberg REST Catalog that prioritizes data quality, governance, and flexibility. It innovates with change events, contract validation, and multi-tenancy — all in a lightweight, customizable solution. In recent days, seismic shifts have reverberated through the data landscape. Notably, Snowflake announced their open-source Iceberg REST catalog, Polaris , while Databricks acquired , Tabular.io - the pioneering company behind Apache Iceberg. Not stopping there, Databricks also open-sourced Unity Catalog – with Iceberg REST (read) support. It seems that these days, to be taken seriously as a Data Company, you need to open-source your Iceberg Catalog implementation. ...