Without a system to ensure good data quality, errors such as dropped columns, duplicated data, and inconsistencies across disparate systems prevent your organization from making smart business decisions using data-driven insights. Worse, poor data quality can lead to missed strategic opportunities: you can’t deliver great insights if you don’t trust your data.
Anomalo automatically monitors and detects data quality issues in records and tables using machine learning—offering real-time, evolving coverage that far exceeds rules-based testing. We offer support for a variety of data lakes, data warehouses, and databases already in your tool stack, making it easy to keep doing your best work.
Today, we’re excited to announce support for Oracle Data Warehouse, offering broad monitoring over your entire data warehouse and the power of Anomalo’s unsupervised machine learning models in uncovering deeper data quality issues.
Oracle’s high-performance capabilities + Anomalo’s data quality monitoring
Oracle Database has been a leading enterprise data warehouse for over three decades, trusted by organizations in industries such as finance, healthcare, and cybersecurity to store critical business data.
The exponential growth of data has made data quality monitoring essential for ensuring your enterprise’s data is consistent, complete, and correct. With Anomalo, you can implement basic monitoring of the entire data warehouse in minutes and at a low cost, and use that as a pathway into deep data quality monitoring to identify issues with the contents of your data in Oracle.
In addition to running a wide variety of data quality checks automatically, Anomalo offers rich visualizations that help you understand data quality problems in context. When an issue is detected, Anomalo’s built-in root-cause analysis points to the likely source of the problem, simplifying the investigation and resolution of the issue.
From your ETL pipeline to every table and record in Oracle, Anomalo is your partner in ensuring data quality and monitoring at scale.
How Anomalo supports a new data lake or data warehouse
Anomalo’s Data Platform team specializes in the intricacies of data sources. For any new data warehouse that we support, such as Oracle, we do a lot of work behind the scenes to handle nuances like SQL syntax, nested data types, and pipeline connectors. This lets enterprises easily integrate and set up Anomalo’s data quality monitoring, regardless of how their data is represented internally.
For instance, every data source has a different method of generating metadata about the tables in the database, and that information may be spread across multiple locations. The Anomalo Data Platform team ensures that the metadata needed for data quality monitoring is ingested correctly, regardless of its location in your data warehouse.
An instant match for any data stack
You can now start monitoring data quality across every table and record in Oracle, and with Anomalo’s suite of integrations, you can improve trust in your data wherever your team works—whether that’s a data catalog like Alation, a communication platform like Teams or Slack, a ticketing system like Jira, or all of the above.
Considering a migration or using multiple data warehouses? When data infrastructure is complex or changing, there’s a higher risk of data quality issues. Anomalo can make managing multiple systems or moving data to the cloud less painful with built-in support for running table comparisons, ensuring you have parity across your infrastructure. We integrate with all the top data warehouses, and it just takes a few clicks to connect a new data source.
If you’re new to Anomalo and interested in learning more, request a demo to find out how we can streamline your business’ data quality monitoring at scale.