Can you analyze the Data Quality in different clouds safely?
DQO.ai was designed for monitoring distributed environments.
The DQO.ai Data Observability supports distributed agents that will run in monitored clouds. Observe Data Quality across different public and private clouds
DQO.ai was designed for monitoring distributed environments.
The DQO.ai Data Observability supports distributed agents that will run in monitored clouds. Observe Data Quality across different public and private clouds
Multi Cloud Data Observability
Multi Cloud Data Observability
Observe Data Quality issues in multiple clouds from one place.
DQO.ai was designed as a distributed Data Observability framework where the Data Observability agents may run independently. Deploy the Data Observability agent in the monitored cloud (Azure, AWS, GCP) and let it talk to the DQO.ai master node. The agent will efficiently execute Data Quality rules close to the data source.
- Analyze Data Quality across multiple clouds
- Run Data Observability agents close to the source to avoid latency
- Analyze Data Quality of on-premise data sources by deploying the agent in the corporate network
No data movement
No data movement
Avoid moving data across the internet and between clouds to run Data Quality checks. Run the quality checks close to the data source.
DQO.ai Data Quality checks will run a query on the data source and retrieve metrics like an aggregated row count or a row count / column average that is grouped by a business column. Limited metadata that is extracted avoids data leakage of sensitive data or high volume data movements.
- Limit the data movements across clouds
- Avoid the risk of data leakage
- Run the Data Quality checks where the data is located
Shared Data Quality Rules
Shared Data Quality Rules
Run the same set of approved Data Quality checks on different database and Data Lake technologies that are available on different clouds.
SQL engines available in different clouds use slightly different SQL dialects. DQO.ai supports customized SQL templates for Data Quality queries that are adapted for different query engines. The list of quality checks simply references a built-in check that has multiple vendor specific implementations.
- Analyze the quality of data using the same set of rules for multiple clouds
- Safely migrate between clouds by defining Data Quality checks to be executed on both the old and new cloud environments
- Define additional business specific Data Quality rules with vendor specific SQL templates
