A regular data audit is essential for maintaining the security of your data, ensuring regulatory compliance, and ensuring data quality. Regular Snowflake evaluations can help identify any data quality problems, from stale data to data outside of expected distribution ranges to incomplete tables. An audit can even uncover if a schema change has affected the data. The Enterprise Edition of Snowflake provides an access history feature, which can help you identify any changes to your data over time.
Snowflake Data Profiling
The benefits of Snowflake data quality profiling are numerous. Not only can it improve data quality, but it can also reveal systemic issues. The tool can also help you draw accurate conclusions about the health of your company, preventing minor errors from growing into major ones. The tool can also help you understand how different events affect your business and use this information to act pro-actively in crises. Here are four of its most important uses.
Data profiling is a powerful tool that automatically analyzes data to find discrepancies, trends, and other issues. The tool uses AI/ML algorithms to monitor the quality of data and convert them into a data trust score. Once you’ve accessed your data, you can then make decisions based on that information. With this tool, you can be confident that your data is safe and secure. With its ease of use, it is easy to deploy and use.
Trifacta Data Quality Rules
When building Snowflake queries, you can easily create validation rules using the Trifacta Wrangler language. The result of these rules is a visual profile that shows the valid, missing, and mismatched values. With the Trifacta Wrangler, building and publishing these rules is quick and easy. With visual profiling, you can see what the data looks like, and which attributes may need to be adjusted.
A company IQVIA won the Ventana Research’s 2018 Overall Digital Leadership Award for applying the Trifacta platform to accelerate discovery and improve outcomes for finding the ideal clinical trial patients. They cited the limitations of existing data tools and spreadsheets, and wanted to streamline data governance to improve the results of their analyses. By using Trifacta’s Data Quality Rules, they have achieved this goal, and much more.
Validatar
As the world becomes increasingly dependent on data, ensuring data quality is critical to driving business impact. Data quality solutions increase speed, accuracy, and time-to-value. Validatar for Snowflake automates data quality testing, empowering analysts to make data-driven decisions faster. Developed by a team of data consultants, Validatar automates the data-quality testing process from source to target.
With validations performed in real-time, Snowflake’s platform tracks the volume and freshness of data in its warehouse. Query logs are useful for troubleshooting. Filter out system queries and extract copy logs to gain an understanding of data movement. You can also determine which tables are critical, and run a data quality test on these tables. It is possible to configure validation rules based on a specific requirement or business rule.
Snowflake offers incredible scalability, but it can also lead to issues with data quality. Validating data using a data validator tool can help companies define their rules visually and give insights into why validations fail. Maintaining data quality and reducing costs in the data pipeline can lower complexity. The validation of Snowflake data will increase the value of the data and minimize errors. There are many other benefits of validating Snowflake data.
CluedIn
The CluedIn snowflake data quality service offers enterprise-grade governance and a data preparation platform to make data’ready for insights.’ This solution automates the process of data fidelity, esoteric data, and data flexibility. The cloud-based platform combines data quality analytics and enterprise-grade governance. It also streams cleaned data directly to analysis systems, leveraging Azure’s power.
The data quality monitoring platform helps businesses to assess and measure their own data. Its sandbox environment lets users easily quarantine bad records and monitor the impact of cleaning work. This helps them achieve the desired Data Quality. Furthermore, CluedIn can track Data Quality trends over time and forecast them. This allows for easy data quality management and gives business executives confidence about the quality of their data. Further, this solution eliminates the need to move data from different platforms.
With the growing cloud computing market, CluedIn has received substantial recognition. It has been named a Gartner “Cool Vendor” for 2020. Its technology orchestrates data from multiple data sources. Its capabilities enable it to serve as a data fabric, providing streaming infrastructure for data and proactively cleaning data as it flows. With the help of Snowflake data quality, businesses can create a better customer experience and increase revenue.