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Mastering Data Quality: 5 Lessons from Data Leaders at Babylist and Nasdaq

Monte Carlo

while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. When it comes to driving impact with your data, you first need to understand and manage that data’s quality. learn when and why data may be down.

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[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

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Data Quality Testing: 7 Essential Tests

Monte Carlo

When it comes to data engineering, quality issues are a fact of life. Like all software and data applications, ETL/ELT systems are prone to failure from time-to-time. Among other factors, data pipelines are reliable if: The data is current, accurate, and complete. The data is unique and free from duplicates.

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Data Teams and Their Types of Data Journeys

DataKitchen

The article illuminates how Data Journeys can enhance data governance, improve operational efficiency, and ultimately lead to organizational success by thoroughly examining different Data Journey types—’ Watcher,’ ‘Traveler,’ ‘Hub & Spoke,’ and ‘Payload.’ ’ What’s a Data Journey?

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Build vs Buy Data Pipeline Guide

Monte Carlo

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data. Missed Nishith’s 5 considerations? Check out Part 1 of the build vs buy guide to catch 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. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

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Data Pipelines in the Healthcare Industry

DareData

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines.