Remove Data Integration Remove Data Management Remove High Quality Data
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Automation and Data Integrity: A Duo for Digital Transformation Success

Precisely

Key Takeaways: Harness automation and data integrity unlock the full potential of your data, powering sustainable digital transformation and growth. Data and processes are deeply interconnected. Today, automation and data integrity are increasingly at the core of successful digital transformation.

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Must-Know Data Integrity Trends for 2025

Precisely

In 2025, its more important than ever to make data-driven decisions, cut costs, and improve efficiency especially in the face of major challenges due to higher manufacturing costs, disruptive new technologies like artificial intelligence (AI), and tougher global competition. Key Data Integrity Trends and Insights for 2025 1.

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Modern Data Architecture: Data Mesh and Data Fabric 101

Precisely

Key Takeaways: Data mesh is a decentralized approach to data management, designed to shift creation and ownership of data products to domain-specific teams. Data fabric is a unified approach to data management, creating a consistent way to manage, access, and share data across distributed environments.

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The Challenge of Data Quality and Availability—And Why It’s Holding Back AI and Analytics

Striim

Without high-quality, available data, companies risk misinformed decisions, compliance violations, and missed opportunities. Why AI and Analytics Require Real-Time, High-Quality Data To extract meaningful value from AI and analytics, organizations need data that is continuously updated, accurate, and accessible.

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Key Data Integrity Trends and Insights for Your 2025 Strategy

Precisely

This means it’s more important than ever to make data-driven decisions, cut costs, and improve efficiency. Get your copy of the full report for all the strategic insights you need to build a winning data strategy in 2025. Data quality is the top data integrity challenge for 64% of organizations this year, up from 50% last year.

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No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically

DataKitchen

Current open-source frameworks like YAML-based Soda Core, Python-based Great Expectations, and dbt SQL are frameworks to help speed up the creation of data quality tests. They are all in the realm of software, domain-specific language to help you write data quality tests.

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Data Migration Strategies For Large Scale Systems

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Can you start by sharing some of your experiences with data migration projects? Closing Announcements Thank you for listening! Don't forget to check out our other shows.

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