When building a Snowflake data warehouse, data quality is one of the most important aspects. Without trustworthy data, it's hard to create valuable reports and dashboards. The Bigeye Field Guide outlines three phases of data quality and categories to measure. The co-founders of Bigeye have compiled their experiences to provide this comprehensive guide to data quality. Here are three important things to consider when building a Snowflake data warehouse.
Snowflake Data Profiling
When deploying a Big Data solution, you must ensure that data stored in your database is reliable, accurate, and complete. Snowflake Data Quality Profiling is a key tool in this process. Profiling is critical because it helps identify data quality issues so you can quickly correct them. Fortunately, Snowflake's architecture is designed to work with a variety of profiling tools. Read on to learn more about the benefits of using Snowflake data quality profiling.
The main benefit of Snowflake Data Profiling is its ability to track data volume, freshness, and other key metrics. In addition to providing a detailed picture of data volumes, this tool helps you make accurate inferences about the health of your company. This feature is especially useful for crisis management, as it helps you understand how various events have affected your data. You can use this information to inform stakeholders of a data quality problem, allowing you to take proactive measures to address it before it becomes a bigger issue.
Automated data audits
An automated data audit in Snowflake can be a powerful tool for ensuring high data quality. By automating quality checks, you can ensure that all tables are consistent and fresh. Automated data audits can also help troubleshoot data quality issues by examining query logs. These logs can also be used to identify critical tables, extract copy logs, and understand data movement.
One of the main features of Snowflake is its access history functionality. This feature records read operations for every query and identifies which columns were accessed. This feature facilitates compliance with regulatory requirements, as well as data governance. If you're using Snowflake for enterprise data, it's essential to understand what Snowflake can and cannot do. Here are a few features of Snowflake's access history.
Object tagging
Object tagging helps companies manage sensitive data at scale. In addition to data quality, Object Tagging aids organizations in compliance and access control. It does so by reading tags applied to objects in Snowflake. The resulting golden customer records can be used to support digital transformation initiatives, meet regulatory requirements, and better serve customers. Tamr uses Snowflake's ML-powered automation to identify and tag objects based on their business context and use.
Tags are assigned to objects using Snowflake's object-tagging feature. Tags are assigned a string value, and users can define as many tags as they need. Object tagging also supports nested objects, so you can apply tags to multiple objects at once. When tagging an object in Snowflake, you can specify how many tags are associated with an object. Tags are passed down through the hierarchy, so if you apply a tag to an object in a table, it will affect all the columns in that table.
Self-service
Self-service for Snowflake data quality is an effective tool to detect and resolve quality problems in data. Using the Snowflake query tool, you can extract health metrics, UUID rate, completeness, and distinctness. You can also track and report on sensitive data. This feature is available in Snowflake's Enterprise Edition. Self-service for Snowflake data quality comes with a host of features and capabilities.
With Snowflake's Data Cloud, teams can gather, transform, and share data securely. Snowflake's Data Cloud comes with native data quality features, which ensure that data is reliable and consistent. Often, teams spend countless hours fixing unreliable reports or dashboards that are based on incorrect data. Business leaders rely on their gut instincts when it comes to making critical business decisions, but data quality can be compromised in a variety of ways.
Experian Aperture Data Studio
The Experian Aperture Data Studio suite of tools provides modern data practitioners with a self-service data quality platform that combines globally curated data sets and automated data tagging algorithms. Its unique data tagging engine is an important tool for the governance of data quality standards, ensuring compliance with regulatory guidelines. The platform is hosted on premises or runs on virtual machines. Users can access data from any web browser to make decisions and develop workflows.
Snowflake data can cause significant data quality issues, but Experian Aperture provides a platform that detects and corrects data quality issues and offers analytics for Snowflake users. The platform is designed to make data quality easier to manage and leverage by integrating with Snowflake. It supports data from 180 million API requests per month and is integrated with Experian's Open Data Platform.