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Boosting Data Accuracy: Resolving Common Data Quality Issues Using SQL

Poor data quality significantly hinders large enterprises from deriving meaningful value. The variety of data is a crucial factor to account for when assessing & rectifying data quality, as diverse data types introduce unique challenges in maintaining accuracy and consistency. Good data quality and integrity are essential for accurate insights, good decision-making, and potential success of large enterprises. Inconsistent, incomplete, and inaccurate data significantly affect business operations, customer satisfaction, and financial performance. SQL provides robust functions to proactively identify and resolve these data quality issues, ensuring accurate, reliable, and consistent data for reporting and analysis.

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2025-01-24

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SQLServerCentral Article

Influencing a Local AI

I set up a local AI LLM to run and then conducted some experiments. This post shows a bit about the experiments and a little code. The setup is on my blog using Docker to containerize an Ollama LLM, which you can read about. The Basic Setup If you read my blog, I downloaded an […]

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2025-01-20

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SQLServerCentral Article

Vector DB implementation using FAISS

Searching for relevant information in vast repositories of unstructured text can be a challenge. This article explains a Python-based approach to implementing an efficient document search system using FAISS (Facebook AI Similarity Search) for Vector DB and sentence embeddings, which can be useful in applications like chatbots, document retrieval, and natural language understanding. In this […]

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2025-01-17

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Question of the Day

Identities and Sequences III

When thinking of the Identity property for auto incrementing columns and sequences for the same action, which can be used with the BIGINT data type?

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