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. Ensure security and access controls.
CIO
NOVEMBER 19, 2024
The requirements for the system stated that we need to create a test data set that introduces different types of analytic and numerical errors. This data would be utilized for different types of application testing.
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CIO
OCTOBER 30, 2024
To overcome those challenges and successfully scale AI enterprise-wide, organizations must create a modern data architecture leveraging a mix of technologies, capabilities, and approaches including data lakehouses, data fabric, and data mesh. Another challenge here stems from the existing architecture within these organizations.
CIO
APRIL 16, 2025
Jenga builder: Enterprise architects piece together both reusable and replaceable components and solutions enabling responsive (adaptable, resilient) architectures that accelerate time-to-market without disrupting other components or the architecture overall (e.g. compromising quality, structure, integrity, goals).
Speaker: Tamara Fingerlin, Developer Advocate
As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. With the 3.0 has to offer!
CIO
APRIL 23, 2025
This is where Delta Lakehouse architecture truly shines. Approach Sid Dixit Implementing lakehouse architecture is a three-phase journey, with each stage demanding dedicated focus and independent treatment. Step 2: Transformation (using ELT and Medallion Architecture ) Bronze layer: Keep it raw.
CIO
DECEMBER 11, 2024
The proposed model illustrates the data management practice through five functional pillars: Data platform; data engineering; analytics and reporting; data science and AI; and data governance. Not my original quote, but a cardinal sin of cloud-native data architecture is copying data from one location to another.
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Embedding analytics in your application doesn’t have to be a one-step undertaking. Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture.
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.
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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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. The primary architectural principles of a true cloud data lake, including a loosely coupled architecture and open file formats and table structures.
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Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. 9 questions to ask yourself when planning your ideal architecture.
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Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.
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Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital.
Speaker: Ahmad Jubran, Cloud Product Innovation Consultant
Many do this by simply replicating their current architectures in the cloud. Those previous architectures, which were optimized for transactional systems, aren't well-suited for the new age of AI. In this webinar, you will learn how to: Take advantage of serverless application architecture. And much more!
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