Data Quality & Reconciliation Tool for Amazon AWS & Azure Cloud
Key Data Quality Challenge for AWS & Azure Cloud
As data moves in and out of the Amazon AWS Cloud, Data Quality frequently deteriorates and loses its trustworthiness due to (*)
- Inability to validate (and alert) on any bad/missing data sources each day
- Inability to enforce data constraints (eg., Redshift readily accepts duplicates. Primary key violation!)
- Multiple data sources sending data to the AWS Cloud get out of sync over time
- Structural change to data in upstream processes not expected by the Cloud, and,
- Presence of multiple IT platforms (Hadoop, DW, Cloud) makes all the above harder
Faulty process, ad hoc data policies, poor discipline in capturing and storing data and lack of control over some data sources all contribute to data inconsistencies on the Amazon AWS Cloud (Redshift, S3, DynamoDB, RDS).
DataBuck: Machine Learning-Guided, Autonomous Data validation for Amazon AWS & Azure Cloud
An autonomous, self-learning, Big Data Quality and integrity validation and data reconciliation tool.
It validates quality and integrity of Big Data and reconciles the Cloud with the source. It can help you enforce constraints by filtering out the bad data and sending out alerts to the right people.
DataBuck’s advanced Machine Learning algorithms develop detailed understanding of your Data Quality Fingerprint at multiple hierarchical levels. It autonomously sets 100,000’s of validation checks w/o manual intervention. Autonomous Machine Learning also enables the tool to be set up and working on multiple data sets in just 3 Clicks! No coding needed!
This white paper explains the underpinnings of the development of the new paradigm in Big Data Quality validation.
Amazon AWS, Azure and Other Supported Data Sources
DataBuck can accept data from all major data sources, including Hadoop, Cloudera, Hortonworks, MapR, HBase, MongoDB, Cassandra, Datastax, HP Vertica, Teradata, Oracle, PeopleSoft, MySQL, MS SQL, SAP, Amazon AWS, MS Azure, and more.
Ping us if you want to try DataBuck for free for 3 months or do a complementary “Data Health Check” to evaluate your data discrepancies between your on-premise and Cloud data.
To learn more about DataBuck
… for a short demo video
Check out the Cloud and DataLake best practices of the most successful organizations.
The underlying driver that is necessitating the incorporation of Machine Learning into Big Data Quality is discussed in this white paper on: New Paradigm in Big Data Quality Testing- Self Learning Algorithms
* Seismic Shift: Nasdaq’s migration to Amazon Redshift, Jason Timmes, AVP of Software Development Nasdaq, Amazon AWS re:Invent, Nov 2014, Las Vegas, NV