Blog Tags: Anomaly Detection
How to Simplify Data Profiling and Management with Snowpark and Streamlit
Learn why data quality is one of the most overlooked aspects of data management. While all models need good quality data to generate useful insights and patterns, data quality is especially important. In this blog, we explore how data profiling can help you understand your data quality. Discover how Tiger Analytics leverages Snowpark and Streamlit to simplify data profiling and management.
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What is Data Observability Used For?
Learn how Data Observability can enhance your business by detecting crucial data anomalies early. Explore its applications in improving data quality and model reliability, and discover Tiger Analytics’ solution. Understand why this technology is attracting major investments and how it can enhance your operational efficiency and reduce costs.
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How Tiger’s Data Quality Framework unlocks Improvements in Data Quality
Accurate data is crucial for informed decisions. Organizations must set clear data quality objectives, implement early data quality processes, and deploy IT solutions aligned with business goals to achieve this. Read how utilizing the Tiger Data Quality framework for automation can help enhance efficiency and eliminate manual data quality checks for better outcomes.
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Automating Data Quality: Using Deequ with Apache Spark
Get to know how to automate data quality checks using Deequ with Apache Spark. Discover the benefits of integrating Deequ for data validation and the steps involved in setting up automated quality checks for improving data reliability in large-scale data processing environments.
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Detecting Unknown Defects: Leveraging GANs in Product Quality
Implement Generative Adversarial Networks (GANs) to identify hidden defects in products with details on how GANs enhance quality control by simulating defect scenarios and improving detection accuracy. Know how to transform product quality assurance with advanced AI techniques.
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