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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Their terminal operations rely heavily on seamless data flows and the management of vast volumes of data. Recently, EUROGATE has developed a digital twin for its container terminal Hamburg (CTH), generating millions of data points every second from Internet of Things (IoT)devices attached to its container handling equipment (CHE).

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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

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The 6 Layers of an IoT Solution

CDW Research Hub

Your first thought about the Internet of Things (IoT) might be of a “smart” device or sensor. However, building an IoT solution requires thought into six distinct layers, each with its own considerations and security implications. So, what are the six layers of IoT? Layer 1: IoT devices. Layer 2: Edge computing.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

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The unreasonable importance of data preparation

O'Reilly on Data

You may picture data scientists building machine learning models all day, but the common trope that they spend 80% of their time on data preparation is closer to the truth. This definition of low-quality data defines quality as a function of how much work is required to get the data into an analysis-ready form.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

Imagine such a system processing unstructured text data like historical maintenance logs, technician notes, defect reports and warranty claims, and correlating it with structured sensor data such as IoT readings and machine telemetry. Lets not use a sledgehammer when a well-placed tap will do.