DataBuck with Circle logoMachine Learning-Guided Big Data Quality Validation and Data Matching Tool – DataBuck

For a quick introduction to the new paradigm in Data Quality Validation see… Play icon

Big Data Quality must be validated to ensure the sanctity, accuracy & completeness of data, as it moves through multiple IT platforms, or as it is stored in Data Lakes, so that the data is trustworthy and fit for use.

Key Big Data Challenge: Data frequently loses its trustworthiness due to (i) Undetected errors in incoming data (ii) Multiple data sources that get out of sync over time (iii) Structural change to data in upstream processes not expected downstream and, (iv) Presence of multiple IT platforms (Hadoop, DW, Cloud). Unexpected errors creep in when data resides in a system, or it moves between a Data Warehouse to a Hadoop environment , or NoSQL database or the Cloud. Faulty process, ad hoc data policies, poor discipline in capturing and storing data and lack of control over some data sources (eg., external data providers) all contribute to data changing unexpectedly.

What is DataBuck: An autonomous, self-learning, Big Data Quality validation and Data Matching tool. Machine Learning is used to simplify the elaborate and complex validations and reconciliations.

DataBuck learns about your Data Quality behavior and models it using advanced Machine Learning techniques. It develops 1,000’s of Data Quality Fingerprints at multiple hierarchical levels accounting for multiple data cyclicality patterns  and monitors incoming data for reasonableness. Machine-learning capabilities then autonomously set 1,000’s of validation checks w/o manual intervention.

It is built ground-up on a Big Data platform (Spark) and is >10x faster than any other tool or your own custom scripts. Autonomous Machine Learning enables the tool to be set up and working on multiple data sets in just 3 Clicks, with no coding needed.

DataBuck will give you peace of mind that your data is accurate and complete. It can certify the integrity of your data and it can easily be audited as well.

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.


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PDF file icon Click Here for an Overview of DataBuck

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Ping us if you want to trial DataBuck (for free) or do a one time “Data Health Check” to evaluate the seriousness of your data discrepancies between different IT systems. You can spot the symptoms that could become a serious liability. Get peace of mind.

Traditional data-sampling techniques are no longer viable options for Big Data Integrity Validation

When data moves between systems errors creep in. Testers must develop automated scripts that help achieve better test coverage and higher quality in less time. This requires big-data testers to assemble custom programs, scripts and accelerators that can then be packaged as a one-stop testing framework. These scripts must be maintained over the next many years as the data constantly evolves. The catastrophic patch work of maintenance and upgrades for home written scripts will collapse sooner or later.

Click here to learn more about Big Data Errors

Implementing DataBuck as the Big Data Integrity Validation framework will include:

DataBuck can be used for a quick “Data Health Check” or to constantly monitor data discrepancies between different IT systems. In the latter case will include:

– A customized tool that automates the process of capturing and validating Data Integrity

– A custom-built result reporting engine, which highlights discrepancies and presents results to the user

– Easy connectivity and validation of traditional RDBMS, NoSQL databases, Hadoop and Cloud components

DataBuck will help Enterprise to:

– Prevent Data Integrity errors within the Regular Data and Big Data environment and applicable downstream processes

– Reduce testing efforts by as much as 75% — enabling faster time-to-market and reduce cost

– Deliver comprehensive test coverage with complex data during development and operational phase

– Generate 2x return within first 12 months of implementation

The overall concept of data flow and points of validation are shown in the exhibit below. Data Integrity is validated at multiple points during its flow in and out of different platforms, at 10x the speed without any additional load on your source systems. The data is in essence reconciled with the source.


Data flow with Buck pic


When one of the newer technologies like Hadoop is involved, the validation process becomes even more complex as illustrated below. Your Data Integrity will be maintained by implementing a pre-Hadoop validation, Hadoop Map-Reduce validation and a Post-Hadoop validation.


Big Data testing in Hadoop



If you’d like to learn how the only Big Data Validation tool in the market, that can work across many different platforms, can help you, ping us…

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We can help you run a proof of concept (POC) to evaluate if DataBuck is right for you.

For Cloud Data Quality visit 

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Are You Wondering How Errors Creep In… and Whether Your Data May Have Errors In It?