DataKitchen

JULY 27, 2023

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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Monte Carlo

APRIL 24, 2023

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. At Uber, for example, data ingestion scale quickly surpassed what out-of-the-box solutions could support.

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DareData

JULY 29, 2020

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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Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

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DataKitchen

SEPTEMBER 21, 2023

What is Data in Place? Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

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Databand.ai

JULY 10, 2023

An observability platform is a comprehensive solution that allows data engineers to monitor, analyze, and optimize their data pipelines. By providing a holistic view of the data pipeline, observability platforms help teams rapidly identify and address issues or bottlenecks.

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Monte Carlo

DECEMBER 19, 2022

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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