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Pushing The Limits Of Scalability And User Experience For Data Processing WIth Jignesh Patel

Data Engineering Podcast

Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up.

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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. It provides data prep, management, and enterprise data warehousing tools.

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Build Your Python Data Processing Your Way And Run It Anywhere With Fugue

Data Engineering Podcast

If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription The only thing worse than having bad data is not knowing that you have it. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders.

Python 100
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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

The typical pharmaceutical organization faces many challenges which slow down the data team: Raw, barely integrated data sets require engineers to perform manual , repetitive, error-prone work to create analyst-ready data sets. Cloud computing has made it much easier to integrate data sets, but that’s only the beginning.

Process 98
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8 Data Quality Monitoring Techniques & Metrics to Watch

Databand.ai

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

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DataOps vs. MLOps: Similarities, Differences, and How to Choose

Databand.ai

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently.

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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.