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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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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. But in many growing organizations, a combination of Fivetran and custom pipelines using Airflow will usually do the trick.

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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.

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How Assurance Achieves Data Trust at Scale for Financial Services with Data Observability

Monte Carlo

Business data assets at Assurance are loaded into the company’s lakehouse architecture through various methods, then stored in several data stores. The data team then uses tools like dbt and Airflow to refine, model and transform raw data into usable, query-able assets through Trino and Starburst.