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The Race For Data Quality in a Medallion Architecture

DataKitchen

Data is typically organized into project-specific schemas optimized for business intelligence (BI) applications, advanced analytics, and machine learning. This involves setting up automated, column-by-column quality tests to quickly identify deviations from expected values and catch emerging issues before they impact downstream layers.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. What breaks your app in production isnt always what you tested for in dev! The way out?

Testing 174
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Accelerating AI for financial services: Innovation at scale with NVIDIA and Microsoft

CIO Business Intelligence

Financial institutions have an unprecedented opportunity to leverage AI/GenAI to expand services, drive massive productivity gains, mitigate risks, and reduce costs. GenAI is also helping to improve risk assessment via predictive analytics.

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Startup Opkey launches agentic AI platform for ERP lifecycle optimization

CIO Business Intelligence

Opkey, a startup with roots in ERP test automation, today unveiled its agentic AI-powered ERP Lifecycle Optimization Platform, saying it will simplify ERP management, reduce costs by up to 50%, and reduce testing time by as much as 85%. That is what were attempting to solve with this agentic platform.

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Unlocking Data Team Success: Are You Process-Centric or Data-Centric?

DataKitchen

Rather than concentrating on individual tables, these teams devote their resources to ensuring each pipeline, workflow, or DAG (Directed Acyclic Graph) is transparent, thoroughly tested, and easily deployable through automation. Their data tables become dependable by-products of meticulously crafted and managed workflows.

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7 types of tech debt that could cripple your business

CIO Business Intelligence

CIOs perennially deal with technical debts risks, costs, and complexities. While the impacts of legacy systems can be quantified, technical debt is also often embedded in subtler ways across the IT ecosystem, making it hard to account for the full list of issues and risks.

Risk 140
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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

Iceberg offers distinct advantages through its metadata layer over Parquet, such as improved data management, performance optimization, and integration with various query engines. Also, the time travel feature can further mitigate any risks of lookahead bias.

Metadata 111