Data Architecture Related Topics 
DataKitchen
JULY 22, 2025
TL;DR: Functional, Idempotent, Tested, Two-stage (FITT) data architecture has saved our sanity—no more 3 AM pipeline debugging sessions. The alternative—maintaining three to five copies of data in every environment and spending entire weekends debugging why Level 1 data differs from Level 3 data—is unsustainable.
CIO Business Intelligence
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 Business Intelligence
NOVEMBER 27, 2024
However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture.
DataKitchen
JUNE 18, 2025
Taking Ownership of Time The solution isn’t to abandon modern data architectures, but to explicitly own the timing aspects of data quality. Document not just what data moves where, but when it moves and what depends on that timing. This means: Treating schedules as first-class design artifacts.
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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. This new open data architecture is built to maximize data access with minimal data movement and no data copies.
CIO Business Intelligence
OCTOBER 30, 2024
The path to achieving AI at scale is paved with myriad challenges: data quality and availability, deployment, and integration with existing systems among them. Another challenge here stems from the existing architecture within these organizations. Building a strong, modern, foundation But what goes into a modern data architecture?
Teradata
JUNE 30, 2025
Learn more Check out Teradata AI Factory close Home Resources Data architecture Article Building a Trusted AI Data Architecture: The Foundation of Scalable Intelligence Discover how AI data architecture shapes data quality and governance for successful AI initiatives. What is AI data architecture?
Speaker: Veena Vasudevan and Jason Hughes
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. In this webinar, Dremio and AWS will discuss the most common challenges in data architecture and how to overcome them with an open data lakehouse architecture on AWS.
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Unfortunately, data replication, transformation, and movement can result in longer time to insight, reduced efficiency, elevated costs, and increased security and compliance risk.
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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.
Speaker: speakers from Verizon, Snowflake, Affinity Federal Credit Union, EverQuote, and AtScale
Using predictive/prescriptive analytics, given the available data. The impact that data literacy programs and using a semantic layer can deliver. Avoiding common analytics infrastructure and data architecture challenges. Thursday, July 29th, 2021 at 11AM PDT, 2PM EDT, 7PM GMT.
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