Don't like leaving your notebook? Want to build Owl into your in-house data quality pipeline? Owl can do both!
Owl software comes with full swagger support built into each release. Swagger provides a feature rich GUI allowing users' the ability to consume and test Owl's REST API.
An OwlCheck is bash script that is essentially the launch point for any owl job to scan a dataset. A dataset can be a flat file (such as textfile, json file, parquet file, etc), or a table from any number of Databases (such as Oracle, Postgres, Mysql, Greenplum, DB2, SQLServer, Teradata, etc).
Add data quality to your data pipeline by using Owl’s DQ framework and libraries. Access dozens of DQ algorithms that run consistently on files, database tables and kaka topics. All formats from s3 buckets to json, xml and csv. The framework is purpose built for DQ making each line of code simple, terse and scalable. Use IF blocks to make decisions when erroneous data enters your pipeline.
Data quality can be simple or complex depending on your needs. Many organizations require the ability to catalog all data experiments, apply DQ checks, run DQ jobs on a regular schedule, alert when errors arise and move errors into a work queue to remediate. Every wonder how successful your current DQ program is? Try Owl reports to get an overview of your progress overtime.