FirstEigen is proud to announce that the DataBuck Observability Module has production customers in all three clouds. The move is critical for helping data owners flag inaccuracies. The award-winning software, DataBuck, detects data quality errors without coding by leveraging AI and Machine Learning.
FirstEigen shares that data errors resulting from system risks are the leading contributors to untrustworthy data. With DataBuck, each data asset is scanned, and any inaccuracies are flagged early, ensuring that data owners have accurate and reliable data in their pipelines.
Undetected errors in data assets steadily multiply across an enterprise, infecting the whole asset. It takes 10x the effort and cost to remedy these errors. But with DataBuck, data observability is at a new level since the tool is at par with conditions like Cloud/Lake use, new sources, high data volumes, changing data structures, and others.
FirstEigen CTO and co-founder Angsuman Dutta says DataBuck will enable data owners to catch errors fast. The DataBuck Observability Module is an autonomous solution that prevents critical data issues. “It scans each data asset and looks for critical errors that may break the data pipeline or disrupt downstream processes. With DataBuck, data engineers do not need to write data validation rules. DataBuck automates tedious, labor-intensive, and time-consuming process for coding rules and orchestration mechanism to detect data issues from 4 hours to 2 minutes.”
With the ability to prevent critical issues in Lake (AWS, Azure Data Lake, GCP Storage) and data pipeline, DataBuck Observability Module can be programmatically integrated into any data pipeline. The module works with AWS Glue, Airflow, Synapse, and Databricks.
Upon flagging critical issues in a data asset, DataBuck Observability Module alerts the data engineer and development teams of:
– Missing file or table
– Additional file or data
– Changes in the data schema
– Duplicate files and records
– Record count mismatch between two steps of the data pipeline
DataBuck also sets thousands of validation checks for continuous testing and data matching to monitor the health metrics and data trust scores. The software’s machine learning algorithms generate an 11-vector data fingerprint that quickly identifies records with issues. Data Consumers can use DataBuck’s self-service feature to turn off or turn on data quality checks specific to their business context.
DataBuck Observability Module ensures customers have higher trust in their reports, analytics, and models. It also lowers data maintenance and costs, multiplying efficiency in scaling data quality operations.
Find out more about FirstEigen and DataBuck from the company website and social channels.