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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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Announcing Open Source DataOps Data Quality TestGen 3.0

DataKitchen

Now With Actionable, Automatic, Data Quality Dashboards Imagine a tool that can point at any dataset, learn from your data, screen for typical data quality issues, and then automatically generate and perform powerful tests, analyzing and scoring your data to pinpoint issues before they snowball. DataOps just got more intelligent.

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Drug Launch Case Study: Amazing Efficiency Using DataOps

DataKitchen

This blog dives into the remarkable journey of a data team that achieved unparalleled efficiency using DataOps principles and software that transformed their analytics and data teams into a hyper-efficient powerhouse. data quality tests every day to support a cast of analysts and customers.

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The DataOps Vendor Landscape, 2021

DataKitchen

Read the complete blog below for a more detailed description of the vendors and their capabilities. Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability. Production Monitoring and Development Testing.

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Companies look to sell off assets to pay for AI investments

CIO Business Intelligence

EY, in a recent blog post focused on top opportunities for IT companies in 2025, recommends money raised from these activities be used on AI projects. Divestitures can also help companies zero in on their potential and market relevance, the blog authors note.

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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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Agentic AI design: An architectural case study

CIO Business Intelligence

Development teams starting small and building up, learning, testing and figuring out the realities from the hype will be the ones to succeed. For instance, If you want to create a system to write blog entries, you might have a researcher agent, a writer agent and a user agent. There can be up to eight different data sets or files.

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