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     * Snowflake
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SCHEDULE A DEMO
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 * Home
 * Products
   * DATABUCK
   * EIGEN RULES
   * INGESTION VALIDATION
 * Resources
   * Blog
   * White Papers
   * Case Studies
 * Contact Us
 * Platform
   * Cloud
     * AWS
     * Snowflake
   * ALATION CATALOG

 * Home
 * Products
   * DATABUCK
   * EIGEN RULES
   * INGESTION VALIDATION
 * Resources
   * Blog
   * White Papers
   * Case Studies
 * Contact Us
 * Platform
   * Cloud
     * AWS
     * Snowflake
   * ALATION CATALOG


 * Home
 * Products
   * DATABUCK
   * EIGEN RULES
   * INGESTION VALIDATION
 * Resources
   * Blog
   * White Papers
   * Case Studies
 * Contact Us
 * Platform
   * Cloud
     * AWS
     * Snowflake
   * ALATION CATALOG

SCHEDULE A DEMO



DATA QUALITY MONITORING

Eliminate your blind spots

Schedule a Demo
Ask us about the free trial



WHY MONITOR DATA QUALITY?

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).




CHALLENGE

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


SOLUTION

Catch bad data NOT by writing laborious rules, BUT by sensing changes in the DNA
of data using AI/ML.


BENEFIT

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.

Customer: Top-3 US Bank with Over $1.5 Trillion in assets, reduces operational
and regulatory reporting risk by leveraging DataBuck to monitor 15,000+ data
assets.

Technology: MapR Data Lake, Teradata, Exadata

Challenges:
- Lack of scalable solution to monitor 15,000+ data assets
- Lack of SMEs to define appropriate Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 90 days for 3000+
data assets using 4 resources
- Detected several unexpected data issues within 30 days of production


Read More

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.

Customer: Top 3 global Networking Equipment provider reduces financial reporting
risk by leveraging DataBuck to monitor 800+ data assets in its financial data
warehouse and reconciling financial information.

Technology: Teradata, Postgress, Snowflake

Challenges:
- Lack of scalable solution to validate and monitor 800+ data assets within the
nightly processing window
- Financial organization were surfacing data errors undetected by the technical
team

Outcomes:
- Deployed auto-discovered data quality rules in less than 30 days for 800 data
assets using 2 resources
- Detected several unexpected data errors that would have impacted financial
reports
- DataBuck reduced data validation time from 11 hrs to less than 2 hrs


Read More

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.

Customer: 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.

Technology: Cloudera Data Lake, S3, Teradata, MSSQL

Challenges:
- Lack of SMEs to define comprehensive and effective Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 30 days for 300+ data
assets using 1 resource
- Detected several customer contactability issues within 7 days of production


Read More

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.

Customer: 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

Technology: AWS, S3, Redshift, mySQL

Challenges:
- Lack of SMEs to define comprehensive and effective Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 15 days for 500+ data
assets using 2 resources
- Detected several unexpected data issues within 7 days of production


Read More

Leading media streaming company reduces revenue risk by leveraging DataBuck to
monitor customer, prospect, and account data in near real time.

Customer: Leading media streaming company reduces revenue risk by leveraging
DataBuck to monitor customer, prospect, and account data in near real time

Technology: AWS, Salesforce, S3

Challenges:
- Lack of SMEs to define comprehensive and effective Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 7 days for 20+ data
assets using 1 resource
- Detected 3% of invalid data used for billing


Read More



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.


HOW AI/ML SIMPLIFIES DATA MONITORING AND DATA QUALITY VALIDATION





SITE LINKS

 * Home
 * About
 * DataBuck
 * EigenRules
 * Blog
 * Case Studies
 * White Papers
 * Contact Us
 * Privacy Policy


GET IN TOUCH!

 * 1212 S Naper Ste 119-220 Naperville, IL 60540
   
   
 * (385) 393-4436
   
   
 * contact@firsteigen.com
   
   




RECENT POSTS

 * 12 Things You Can Do to Improve Data Quality
 * How to Build a Robust Data Infrastructure
 * Why Data Quality Management Is Key to Digital Transformation Success
 * The Quick Guide to Data Error Handling in Data Warehouses
 * 10 Data Ingestion Tools to Fortify Your Data Strategy
 * Data Validation for DataOps: The Only Guide You’ll Ever Need


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SITE LINKS

 * Home
 * About
 * DataBuck
 * EigenRules
 * Blog
 * Case Studies
 * White Papers
 * Contact Us
 * Privacy Policy


GET IN TOUCH!

 * 1212 S Naper Ste 119-220 Naperville, IL 60540
   
   
 * (385) 393-4436
   
   
 * contact@firsteigen.com
   
   
 * Mon - Fri : 9:00 AM - 5:00 PM
   Sat - Sun : Closed
   
   




RECENT POSTS

 * 12 Things You Can Do to Improve Data Quality
 * How to Build a Robust Data Infrastructure
 * Why Data Quality Management Is Key to Digital Transformation Success
 * The Quick Guide to Data Error Handling in Data Warehouses
 * 10 Data Ingestion Tools to Fortify Your Data Strategy
 * Data Validation for DataOps: The Only Guide You’ll Ever Need


SUBSCRIBE!

Sign up for our newsletter!

Name
Please enter your name.
Email Address
Please enter a valid email address.
Subscribe!

Thanks for subscribing! Please check your email for further instructions.

Something went wrong. Please check your entries and try again.


SITE LINKS

 * Home
 * About
 * DataBuck
 * EigenRules
 * Blog
 * Case Studies
 * White Papers
 * Contact Us
 * Privacy Policy


GET IN TOUCH!

 * 1212 S Naper Ste 119-220 Naperville, IL 60540
   
   
 * (385) 393-4436
   
   
 * contact@firsteigen.com
   
   
 * Mon - Fri : 9:00 AM - 5:00 PM
   Sat - Sun : Closed
   
   


RECENT POSTS

 * 12 Things You Can Do to Improve Data Quality
 * How to Build a Robust Data Infrastructure
 * Why Data Quality Management Is Key to Digital Transformation Success
 * The Quick Guide to Data Error Handling in Data Warehouses
 * 10 Data Ingestion Tools to Fortify Your Data Strategy
 * Data Validation for DataOps: The Only Guide You’ll Ever Need


SUBSCRIBE!

Sign up for our newsletter!

Name
Please enter your name.
Email Address
Please enter a valid email address.
Subscribe!

Thanks for subscribing! Please check your email for further instructions.

Something went wrong. Please check your entries and try again.


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