Case Studies
Technology proven in production at some of the world’s leading organizations.
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Top 3 Data Provider
Acceldata plays a key role in the company’s internal data supply chain. All data is validated and cleansed in Acceldata before it enters the supply chain.
Problem
Data quality checks taking too long, which resulted in poor data quality. FTC fines and other costs imposed on company due to bad data.
Solution
Implemented quality and drift checks throughout the data pipeline starting with the landing zone, which hosts >1400 daily inputs from 110 countries.
Results
Increased data quality coverage, problems caught at the source and remediated. Business owners able to add rules, improving collaboration and accuracy.
Observing
reduction in data quality processing time. Reduced from 14 days to 4 hours.
external input feeds from over 110 countries analyzed everyday and anomalies detected.
increase in speed and accuracy of rule creation.
PhonePe
A hypergrowth payment processor with over half billion daily transactions. Acceldata helps manage one of the world’s largest instant payment systems. "PhonePe’s data infrastructure reliability initiative would never have been possible without Acceldata.”
Problem
Inability to scale their data engineering and operations efforts as the transaction volumes increased by many orders of magnitude.
Solution
Observability to eliminate data pipeline scaling challenges across Streaming, OLAP & OLTP. Automatic reconciliation between 70+ Live and DR Hadoop Clusters.
Results
Stable architecture with resilient data operations that accelerated migration to cloud while maintaining performance of existing Hadoop environment
Observing
improvement in data quality
data engineers directed to higher value added tasks
proprietary scripts and other patchwork approaches replaced
Pubmatic
Acceldata isolated bottlenecks, automated performance improvements, and distinguished between mandatory and unnecessary data to rapidly scale big data environment to meet expanding business requirements and reliably support mission-critical and customer-facing analytics requirements.
Problem
Consistently experienced high MTTR (Mean Time to Resolution) metrics, frequent outages, and performance bottlenecks.
Solution
Predict, prevent and optimize PubMatic’s data system performance by isolating bottlenecks and automating performance improvements
Results
Efficiency gains by Acceldata materially improved Pubmatic's ‘cost per ad impression’ metric, a critical business requirement
Observing
reduction in HDFS block footprint
in OEM licensing costs saved annually
Kafka clusters consolidated to save costs and improve operations
T-Mobile
Acceldata enabled T-Mobile improve Data Reliability, reduce data Costs, and speed Performance. Fixed broken data processes, freed-up additional capacity, accelerated data product delivery, and cloud migration.
Problem
Needed better visibility and improved data quality for critical data pipelines serving their customer offers and uplift models.
Solution
Observability across their on-premise and cloud data infrastructures. Over 50 data quality rules applied on 45 billion rows on a daily basis.
Results
Reduced compliance fines and improved their customer offer models. Over $350k in hard cost saving in first 2 weeks.
Observing
Billion rows verified for data quality in under 2 hours
reduction in storage consumption by eliminating 9PB of stagnant data in 1st 2 weeks
weeks for time to value and $350k in hard cost savings
Top 10 Global Bank
This large financial institution replaced their proprietary technologies and brittle DIY implementations for data quality and observability with Acceldata. Now have visibility across all Data Processing on HDP, CDP, ODP, and our stand alone Spark and Kafka Pipelines
Problem
Visibility challenges across cloud and on-premises data making migrations extremely hard to achieve without loss or errors
Solution
Data Observability across HDP, CDP, ODP and Cloud Data environments with reconciliation and drift checks across all their complex pipelines.
Results
Stable architecture with resilient data operations that accelerated migration to cloud while maintaining performance of existing Hadoop environment
Observing
improvement in data quality
data engineers directed to higher value added tasks
proprietary scripts and other patchwork approaches replaced
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