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The Race For Data Quality in a Medallion Architecture

DataKitchen

This architecture is valuable for organizations dealing with large volumes of diverse data sources, where maintaining accuracy and accessibility at every stage is a priority. 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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Microsoft Fabric vs. Snowflake: Key Differences You Need to Know

Edureka

Its multi-cluster shared data architecture is one of its primary features. Additionally, Fabric has deep integrations with Power BI for visualization and Microsoft Purview for governance, resulting in a smooth experience for both business users and data professionals.

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Beyond the Data Complexity: Building Agile, Reusable Data Architectures

The Modern Data Company

BCG research reveals a striking trend: the number of unique data vendors in large companies has nearly tripled over the past decade, growing from about 50 to 150. This dramatic increase in vendors hasn’t led to the expected data revolution. The limited reusability of data assets further exacerbates this agility challenge.

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Data Engineering Weekly #161

Data Engineering Weekly

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

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How to Become a Data Engineer in 2024?

Knowledge Hut

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Data scientists and data Analysts depend on data engineers to build these data pipelines.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

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Wizeline and Ascend.io Join Forces to Unleash AI-Powered Data Automation

Ascend.io

” Key Partnership Benefits: Cost Optimization and Efficiency : The collaboration is poised to reduce IT and data management costs significantly, including an up to 68% reduction in data stack spend and the ability to build data pipelines 7.5x ABOUT ASCEND.IO Learn more at Ascend.io or follow us @ascend_io.