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Ripple's Centralized Data Platform

Ripple Engineering

For Ripple's product capabilities, the Payments team of Ripple, for example, ingests millions of transactional records into databases and performs analytics to generate invoices, reports, and other related payment operations.    A lack of a centralized system makes building a single source of high-quality data difficult.

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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. That implies working with new patterns like vector databases.” RAG workflow.

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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!”. The DataOps Advantage .

Process 98
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The Rise of the Data Engineer

Maxime Beauchemin

Data modeling is changing Typical data modeling techniques — like the star schema  — which defined our approach to data modeling for the analytics workloads typically associated with data warehouses, are less relevant than they once were. Those systems have been taught to normalize the data for storage on their own.

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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.