Anomaly: Something that deviates from the standard, normal, or expected. This can be in the form of a single data point, record, or a batch of data

Accuracy: The data represents the real-world values they are expected to model.

Burndown Charts: A graphical representation of completed and remaining work versus time.

Comparison: An evaluation to determine if the structure and content of the source and target data stores match

Comparison Runs: An action to perform a comparison

Completeness: Required fields are fully populated.

Conformity: Alignment of the content to the required standards, schemas, and formats.

Connectors: Components that can be easily connected to and used to integrate with other applications and databases. Common uses include sending and receiving data.

• We can connect to anything within the Spark and Kafka ecosystems, and we have major integrations with leading ELT/ETL providers. If you have a data store we don’t yet support, talk to us!
• We currently support: Files on Object Storage (CSV, JSON, XLSX, Parquet); ETL/ELT Providers (Fivetran, Stitch, Airbyte, Matillion – and any of their connectors!); Data Warehouses (BigQuery, Snowflake, Redshift); Data Pipelining (Airflow, DBT, Prefect), Databases (MySQL, Po stgreSQL, MSSQL, SQLite, etc.) and any other JDBC source

Consistency: The value is the same across all datastores within the organization.