DataGeir HawkEye

HawkEye is a native application for data profiling that can assist you in analyzing your Snowflake data statistically and keeping an eye on the quality of your data as a result. Users can also discover more about data quality by looking at how your data profile has changed over time.

Features of HawkEye:
Dataset Profiling: HawkEye will perform data profiling on the source table. It provides an overall description of the table, including the number of observations, variable types, average record size in memory, and the number of duplicate rows, among other details.

Column Profiling: While analyzing the data, it is important to have the option of observing individual columns separately to get a clear picture of each column. HawkEye provides individual column descriptions based on column data types such as numerical, string, and date.

Cross-column Profiling: HawkEye allows you to analyze the relationships between columns by providing cross-column profiling. This feature helps you gain insights into the connections and dependencies between different columns.

Data Labeling: HawkEye utilizes machine learning techniques to identify different labels or tags within textual columns. It recognizes various entities such as persons, organizations, geographical locations, as well as different linguistic tags like ADJ (adjective), CS (conjunction), CC (coordinating conjunction), and so on.

Anomaly Detection: HawkEye also offers anomaly detection within the data profiler itself. Anomaly detection refers to the identification of outliers that deviate significantly from the majority of the data. These outliers appear inconsistent with the rest of the dataset.

To perform data profiling, users need to provide the source table, a list of columns, the number of records, and other configuration information. HawkEye analyzes the configuration parameters, column data types, and generates a profile report for individual columns and the entire dataset.

After performing data profiling for numerical, string, date, and labeled columns, users can also conduct anomaly detection on the columns based on their preferences. HawkEye employs statistical approaches to identify outliers from the profile observations.

Data Quality Tests: HawkEye also provides data quality rules to ensure the completeness, validity, consistency, accuracy, uniqueness, and timeliness of data. These rules help maintain data integrity and improve the overall quality of the data.