Remove Accessibility Remove High Quality Data Remove Pipeline-centric Remove SQL
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Ripple's Centralized Data Platform

Ripple Engineering

  A lack of a centralized system makes building a single source of high-quality data difficult. The key aspect of any business-centric team in delivering products and features is to make critical decisions on ensuring low latency, high throughput, cost-effective storage, and highly efficient infrastructure.

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Experts Share the 5 Pillars Transforming Data & AI in 2024

Monte Carlo

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. How fresh is the data? Can I see the pipeline? What is the lineage?

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

It’s too hard to change our IT data product. Can we create high-quality data in an “answer-ready” format that can address many scenarios, all with minimal keyboarding? . “I I get cut off at the knees from a data perspective, and I am getting handed a sandwich of sorts and not a good one!”.

Process 98
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Data Pipelines in the Healthcare Industry

DareData

One paper suggests that there is a need for a re-orientation of the healthcare industry to be more "patient-centric". Furthermore, clean and accessible data, along with data driven automations, can assist medical professionals in taking this patient-centric approach by freeing them from some time-consuming processes.

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The Rise of the Data Engineer

Maxime Beauchemin

In retrospect, complex SCD modeling techniques are not intuitive and reduce accessibility. The modern data warehouse is a more public institution than it was historically, welcoming data scientists, analysts, and software engineers to partake in its construction and operation.