Data Mesh Principles and Logical Architecture
Martin Fowler
DECEMBER 3, 2020
Last year, my colleague Zhamak Dehghani introduced the notion of the Data Mesh , shifting from the notion of a centralized data lake to a distributed vision of data.
Martin Fowler
DECEMBER 3, 2020
Last year, my colleague Zhamak Dehghani introduced the notion of the Data Mesh , shifting from the notion of a centralized data lake to a distributed vision of data.
CIO
DECEMBER 20, 2024
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects.
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CIO
NOVEMBER 27, 2024
From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.
Martin Fowler
SEPTEMBER 5, 2024
Decentralized data management requires automation to scale governance effectively. Fitness functions are a powerful automated governance technique my colleagues have applied to data products within the context of a Data Mesh.
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There’s no getting around it: selecting the right foundational data-layer components is crucial for long-term application success.
CIO
NOVEMBER 19, 2024
In our real-world case study, we needed a system that would create test data. This data would be utilized for different types of application testing. The requirements for the system stated that we need to create a test data set that introduces different types of analytic and numerical errors.
CIO
OCTOBER 29, 2024
In an era marked by heightened environmental, social and governance (ESG) scrutiny and rapid artificial intelligence (AI) adoption, the integration of actionable sustainable principles in enterprise architecture (EA) is indispensable. Training a single AI model emits as much as five average cars over their lifetimes.
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From understanding its distributed architecture to unlocking its incredible power for industries like healthcare, finance, retail and more, experience how Cassandra® can transform your entire data operations.
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Apache Cassandra is an open-source distributed database that boasts an architecture that delivers high scalability, near 100% availability, and powerful read-and-write performance required for many data-heavy use cases.
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In an effort to be data-driven, many organizations are looking to democratize data. However, they often struggle with increasingly larger data volumes, reverting back to bottlenecking data access to manage large numbers of data engineering requests and rising data warehousing costs.
Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri
Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. Yet they are continually challenged with providing access to all of their data across business units, regions, and cloud environments.
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While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.
Speaker: Veena Vasudevan and Jason Hughes
With data stored in vendor-agnostic files and table formats like Apache Iceberg, the open lakehouse is the best architecture to enable data democratization. By moving analytic workloads to the data lakehouse you can save money, make more of your data accessible to consumers faster, and provide users a better experience.
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Data fuels the modern enterprise — today more than ever, businesses compete on their ability to turn big data into essential business insights. Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data.
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An organization’s data is copied for many reasons, namely ingesting datasets into data warehouses, creating performance-optimized copies, and building BI extracts for analysis. Read this whitepaper to learn: Why organizations frequently end up with unnecessary data copies.
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