Confluent’s Post

Operational and analytical use cases all face the same problem: they can’t reliably access relevant, complete, and trustworthy data from across their organization. In my latest article in InfoQ, I explore a new approach to data processing that rethinks the multi-hop architecture and can help you develop the basis of healthy data communication across your organization. Read on to get started: https://lnkd.in/gjDXF6Px

A must read for every organization that faces the limitations of traditional ETL/ELT mechanisms in analytical and event stream environments!

From Lakehouse architecture to data mesh - Adevinta: Therefore, this structure allows for greater confidence in data quality, improving interoperability between domains and facilitating data reuse – ...

Join Neal Ford and his guest Pramod S. on May 9 at 12 p.m. ET to get an expert perspective on what the future holds for data-driven architecture. Big data is getting bigger all the time, so you know you’ll have to support a data-driven strategy at some point, but what will that entail? https://bit.ly/3Qd6wU8 #softwarearchitecture

A distributed database paired with a shared-nothing architecture provides various advantages, empowering businesses to efficiently manage large volumes of data. Here are some key points to consider.

Multi-hop architectures have been around for decades—and they're starting to show their age. It's time to move beyond this inherently slow, expensive approach. Read this article by Adam Bellemare to learn how shifting data processing upstream can bring clean, reliable, and accessible data to the teams that need it while cutting down on complexity and break-fix work!

Multi-hop architectures have been around for decades—and they're starting to show their age. It's time to move beyond this inherently slow, expensive approach. Read this article by Adam Bellemare to learn how shifting data processing upstream can bring clean, reliable, and accessible data to the teams that need it while cutting down on complexity and break-fix work!

Multi-hop architectures have been around for decades—and they're starting to show their age. It's time to move beyond this inherently slow, expensive approach. Read this article by Adam Bellemare to learn how shifting data processing upstream can bring clean, reliable, and accessible data to the teams that need it while cutting down on complexity and break-fix work!

"Medallion Architecture, with its Bronze, Silver, and Gold layers, offers a systematic framework for data organization, transformation, and consumption. It emphasizes incremental enhancement, flexibility, and governance, paving the way for advanced analytics and machine learning endeavors. Medallion Architecture is a data design pattern for better data organization. It is not a data model. It is neither dimensional, nor data vault, nor relational. Instead, a system built under the medallion architecture can hold files/tables designed on any of these modeling principles. Similarly, medallion architecture can fit into many data management ecosystems, such as data warehouses, data lakes, and/or lake houses." Check out Jun Shan's Medium article where he describes what 'Medallion Architecture' is, why it's relevant, and how it's used: https://lnkd.in/eEX-w_vz

In the world of data warehousing, Kimball's approach is considered one of the most influential and practical methodologies. In this article, I've outlined the key components of Kimball’s architecture, based on the book - "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition". https://lnkd.in/gqwJREX7