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The Data Integration Solution Checklist: Top 10 Considerations

Precisely

Key Takeaways: Data integration is vital for real-time data delivery across diverse cloud models and applications, and for leveraging technologies like generative AI. As enterprise technology landscapes grow more complex, the role of data integration is more critical than ever before.

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Building A Data Mesh Platform At PayPal

Data Engineering Podcast

Jean-Georges Perrin was tasked with designing a new data platform implementation at PayPal and wound up building a data mesh. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Are you tired of dealing with the headache that is the 'Modern Data Stack'?

Building 147
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Exploring The Nuances Of Building An Intential Data Culture

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze.

Building 147
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How to Ensure Data Integrity at Scale By Harnessing Data Pipelines

Ascend.io

So when we talk about making data usable, we’re having a conversation about data integrity. Data integrity is the overall readiness to make confident business decisions with trustworthy data, repeatedly and consistently. Data integrity is vital to every company’s survival and growth.

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Aligning Data Security With Business Productivity To Deploy Analytics Safely And At Speed

Data Engineering Podcast

He also explains why data security is distinct from application security and some methods for reducing the challenge of working across different data systems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Join in with the event for the global data community, Data Council Austin.

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Solving The Persistent Challenges of Data Modeling

The Modern Data Company

Understands how different pieces of data (books) relate to each other, helping users find related information. Keeps the library orderly, adjusting as new data arrives. Tracks key performance indicators to understand the use and value of the data. Manages information about data access and monitors data quality.

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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

ERP and CRM systems are designed and built to fulfil a broad range of business processes and functions. This generalisation makes their data models complex and cryptic and require domain expertise. The distance between the owner and the domain that generated the data is key to expedite further analytical development.

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