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Data quality tools for NoSQL
Data quality tools measure how good and useful a data set is to serve its intended purpose. High quality data can lead to better decision-making and faster insights as well as reduce the costs of identifying and dealing with bad data in the system. It can also save time and allow companies to focus on more important tasks.
Dataedo
Dataedo provides users with a comprehensive tool for maintaining, tracking, and improving data quality. Access selection of 150+ built-in data quality rules or define bespoke data quality rules using SQL queries. Schedule data quality checks for continuous monitoring. Use a DQ dashboard for quick issue detection and trend analysis.
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Aperture Data Studio
Aperture Data Studio combines self-service data quality with globally curated data sets into a single data quality platform. This empowers modern data practitioners to build a consistent, accurate, and holistic view of their consumer data quickly and effortlessly. It lets you set custom workflows for data profiling, cleansing, validation, transformation, enrichment, and deduplication.
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Data quality tools are the scripts that support the data quality processes and they heavily rely on identification, understanding, and correction of data errors. Data quality tool enhances the accuracy of the data and helps to ensure good data governance all across the data-driven cycle.
The common functions that each data quality tools must perform are:
• Data profiling
• Data monitoring
• Parsing
• Standardization
• Data enrichment
• Data cleansing
Choosing the right data quality tool is essential and impacts the final results. To help you with the right selection, we prepared a list of tools that will assist you with maintaining a high level of data quality.