Catch data errors before your business partners do Autonomously monitor any metric you want; ML will recommend what else you must
Eliminate your blind spots
Why Monitor data
Error creeps into data every step along the way as it makes it way to the business and analytics team for usage and insights. Companies are seeing an increase in data volume, data complexity, number of data sources and number of platforms (Lake, Cloud, Cloud Warehouses, Hadoop).
Traditional data validation solutions
- Were built for Data Stewards and not for Data Engineers
- They were built for individually writing rules for every table one by one, not built for automation
- Are costly to scale and are difficult to manage
Catch bad data NOT by writing laborious rules, BUT by sensing changes in the DNA of data using AI/ML.
Catch data errors before your business partners do.
- Autonomously validate 1,000’s of data sets in a few clicks
- 10x in scaling Data Quality operations
- Lower data maintenance work & cost
- Trustable reports, analytics & models
DataBuck is not merely a software, adopting it sends a message – build reliability by design
DataBuck is NOT
- BI tool
- ETL tool
- Data wrangling tool
DataBuck IS AN automated data monitoring and validation software
DataBuck has delivered these results for its customers:
Top-3 US Bank with Over $1.5 Trillion in assets, reduced operational and regulatory reporting risk by leveraging DataBuck to monitor 15,000+ data assets.
Top 3 global Networking Equipment provider reduced financial reporting risk by leveraging DataBuck to monitor 800+ data assets in its financial data warehouse and reconciling financial information.
Top-3 Bank in Africa reduces financial crime risk by leveraging DataBuck to monitor client data spanning over 300 data assets in its Data Lake.
Top 3 Telemedicine and Healthcare company reduces data risk and transforms its data pipeline by leveraging DataBuck to monitor eligibility files received from 250+ Hospitals comprising of millions of records in real time.
Leading media streaming company reduces revenue risk by leveraging DataBuck to monitor customer, prospect, and account data in near real time.
What Data Sources Can it Work With:
DataBuck can accept data from all major data sources, including Hadoop, Cloudera, Hortonworks, MapR, HBase, Hive, MongoDB, Cassandra, Datastax, HP Vertica, Teradata, Oracle, MySQL, MS SQL, SAP, Amazon AWS, MS Azure, and more.