Data Quality Archives - Tiger Analytics

Blog Tags: Data Quality

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.

Invisible Threats, Visible Solutions: Integrating AWS Macie and Tiger Data Fabric for Ultimate Security

Data defenses are now fortified against potential breaches with the Tiger Data Fabric-AWS Macie integration, automating sensitive data discovery, evaluation, and protection in the data pipeline for enhanced security. Explore how to integrate AWS Macie into a data fabric.

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.

Maximizing Efficiency: Redefining Predictive Maintenance in Manufacturing with Digital Twins

Tiger Analytics leverages ML-powered digital twins for predictive maintenance in manufacturing. By integrating sensor data and other inputs, we enable anomaly detection, forecasting, and operational insights. Our modular approach ensures scalability and self-sustainability, yielding cost-effective and efficient solutions.

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.