Soda Features

Correctly identify inaccurate, incomplete, or duplicated data from a data source.

Standardize data formatting for uniformity and easier data usage.

Finds duplicates using the fuzzy logic technology or an advance search feature.

Generate real-time depth analytics utilizing event metrics, logging and metadata.

Use custom or pre-built tests for buisness rules. to ensure data quality.

Involves automation capabilities to identify and track issues, failed operations by looking at historical trends.

Complete visibility of the data pipeline, and immediately notifies the data team if any issues. Ensures cross stack visbility.

Provide follow-up information after data cleanings through a visual dashboard or reports.

Schedule automated audits to identify data anomalies over time based on set business rules.

Gives a view of the entire data quality management ecosystem.

Allows user role-based access and actions to authorization for specific tasks.

Identify the different type of anomalies and receive alerts.

The data observability environment can be viewed from a single dashboard.

Provides immediate alerts for any anomalies or expected events.

Establishes lineage for the data pipeline - from data warehouse to the data user.

Support integrations with various business applications which support different data processes. Also, integrate with apps to provide alerts.

Allows users to generate text based on a text prompt.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting