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Data quality tools for Apache Derby
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.
Global IDs Data Quality Suite
Global IDs Data Quality Suite ensures the quality of the data by establishing control points and read-only quality controls at the database level. It continuously monitors quality metrics, while it also automates control generation for critical data elements across all kinds of data sources.
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Ataccama ONE
Ataccama ONE offers self-driving data quality management by letting you quickly understand the state of your data, validate & improve it, prevent bad data from entering your systems, and continuously monitor data quality.
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Atlan
Atlan uses DataOps to create a new paradigm for building trust in your data. It auto-generates data quality profiles which make detecting bad data dead easy. From automatic variable type detection & frequency distribution to missing values and outlier detection, Atlan offers everything.
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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.