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Abstract: The scale of data analysis tasks have increased, highlighting the critical importance of data quality. Data quality assessment and repair have become pivotal in data preparation. Despite the ...

Millions of users work with SQL to keep the gears of their business turning. In an era marked by relentless digital transformation, the proliferation of AI workloads, and tightening regulatory demands ...

Have you ever spent hours wrestling with messy spreadsheets, only to end up questioning your sanity over rogue spaces or mismatched text entries? If so, you’re not alone. Data cleaning is one of the ...

Challenges with data quality and data governance have plagued healthcare analytics efforts for decades – and the stakes are only getting higher in the age of AI. Inaccurate or inconsistent data ...

If you’re leading a data transformation project or migrating to SAP S/4HANA, you know how much of a burden it places on data owners and business users. They are the ones responsible for validating ...

This project focuses on analyzing global layoffs data using SQL. The workflow was divided into two main phases: Data Cleaning → Preparing and standardizing the dataset for accuracy and consistency.

Everything starts with the data. Without a strong foundation, AI initiatives falter. Early in our journey, we discovered that inconsistent data quality was undermining our models. We invested in ...

A new SQL Server 2025 feature lets organizations run vector-based semantic searches on their own data, connecting to local or cloud-hosted AI models without relying on massive general-purpose LLMs. I ...

3.4.2 Making "UPDATE" ...