Overcome the Limitations of AWS Deequ with AI/ML

Data Trust Score Without Coding

Scalable

Scalable

Set up 1,000 data assets in less than 40 hours

Fast

Time reduction to onboard data set

Validate 100 million records in 60 seconds

Better

Reduction in unexpected errors

Look for 14 types
of data errors

Economical

Cost reduction

Validate 10,000 Data Assets in less than $50

Secure

Increase in processing speed

No Data leaves your Data Platform

Integrable

Cloud native

Data Pipeline Data Governance Alert System Ticketing System

Problems with AWS Deequ

Solutions like Deequ, Griffin, and Great Expectations rely on a rule-based approach to validate AWS S3. Data Engineering team experiences the following operational challenges while integrating data validation solutions:

Labor Intensive

  • Every dataset’s underlying behavior has to be deeply analyzed and understood.
  • Consult the subject matter experts to determine what rules need to be implemented.
  • Must implement rules specific to each bucket. So, the effort is linearly proportional to the number of buckets in the S3 Data Lake

Incomplete Rules Coverage

  • Users must predict everything that could go wrong and write rules for it.
  • The quality of rules coverage is non-standard and user-dependent.

Lack of Auditability

  • It’s challenging for the business to go back a week or a month and review past results or compare them to the present.

Not Scalable for 1,000’s of data assets

Problems in AWS Deequ

Our 10-Point Framework to Overcome the Limitation for Deequ

White Paper
  • Machine Learning Enabled
  • Autonomous
  • Data Variety Support
  • Scalability
  • In-Situ
  • Serverless
  • Part of the Data Validation Pipeline
  • Integration and Open API
  • Audit Trail/Visibility of Results
  • Business Stakeholder Control
Download the WhitePaper!

Solution: DataBuck - Trusted Data w/o Coding

DataBuck is a scalable solution that can deliver trusted data for tens of 1,000’s of datasets leveraging AI/ML to autonomously track data and flag data errors. It automatically detects 100% of all Data errors due to “Systems Risks” with zero human intervention using AI/ML. Errors creep in the data pipeline and steadily multiply like cancer across an enterprise. For every step the error spreads, it takes 10x more cost and effort to fix it. User’s benefit by:

  • Trustable reports, analytics & models
  • Lower data maintenance work & cost
  • 10x efficient in scaling Data Quality operations

DataBuck is recognized by Gartner and IDC as the most innovative DQ validation software.

Set up data quality validation for 1,000's of buckets in just a few clicks with DataBuck

Solution

Out-of-the-box dash board can slice and dice data quality validation results and its easy to audit

Solution 2

Benefit of automating data quality validation on AWS

Get consumable, crystal clear stream of data from AWS along with these benefits…

People Productivity

People productivity
boost >80%

Reduction in unexpected errors

Reduction in unexpected errors: 70%

Cost reduction

Cost reduction >50%

Time reduction to onboard data set

Time reduction to onboard data set ~90%

Increase in processing speed

Increase in processing speed >10x

Cloud native

Cloud native

What DataBuck users say…

“What took my team of 10 Engineers 2 years to do, DataBuck could complete it in <8 hrs”

- VP Technology, Enterprise Data Office, Major US bank

“DataBuck’s Data Quality automation does 80% of the heavy lifting for us with just 5% of the effort.”

- CIO of US Financial Services firm

“Streamlining the DQ monitoring and validation process w/DataBuck has reduced our time-to-market. With fewer resource we auto discover DQ rules, which also self-heals as the data evolves.”

- Head of Enterprise Data Quality Monitoring, Major US bank

“DataBuck can really add a lot of headcount efficiency for us. This tool makes it easy for us to not only profile and discover the rules, but also to operationalize them and auto-heal as the data evolves over time.”

- VP, Enterprise Information Management, Information Governance Leader, Insurance Company

“AML is on the rise. We have data from 10 countries in different formats and standards that need to be validated. We could not keep up doing it manually. DataBuck has automated and streamlined our data pipeline.”

- Sr. Exec. Technology Office, Top-3 African bank

“In the last 3 years we’ve had a 100x increase of API’s and microservices on the Cloud. This proliferation is beyond what Data Stewards can manage. As Cloud-native tool designed for Data Engineers, DataBuck autonomously validates data upstream and tremendously eases the burden on Stewards.”

- Sr. VP Data Mgmt and Analytics, US Investment Bank

“Monitoring and validating files and data at ingestion directly impacts our revenues. DataBuck gives us the reliability, intelligence and speed we need to eliminate revenue-leakage.”

- VP Technology, Enterprise Data Office, Telehealth provider

“Aggregating weekly sales data from many dozens of sources and validating them is laborious and error prone. With DataBuck’s AI/ML-driven DQ automation we got more accurate data with less than 10% effort.”

- Director, Commercial Data Operations, US pharmaceutical

“With the traditional Data Quality tools, we could not thoroughly audit the financial data for the Street w/in our audit window. DataBuck’s performance has reduced data validation times from 11 hrs to 2 hrs, and w/higher accuracy.”

- Director, IT – Data Strategy, Financial Planning, Fortune-50 Hi Tech manufacturer

Cases Studies

Click here to download the collection of case studies

Introduction Data Quality Monitoring- Why it's important?
FirstEigen recognized in AWS re:Invent as best-of-breed DQ tool
Autonomous cloud Data Quality validation demo with DataBuck

Friday Open House

Our development team will be available every Friday from 12:00 - 1:00 PM PT/3:00 - 4:00 PM ET. Drop by and say "Hi" to us! Click the button below for the Zoom Link:

Friday Open House - Talk to Us Live!