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Case Studies

Technology proven in production at some of the world’s leading organizations.

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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

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.

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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
Pipelines
Users
Data
Infrastructure
Cost
99%

reduction in data quality processing time. Reduced from 14 days to 4 hours.

>1400

external input feeds from over 110 countries analyzed everyday and anomalies detected.

20x

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.”

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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
Pipelines
Data
Infrastructure
Cost
Users
46%

improvement in data quality

10+

data engineers directed to higher value added tasks

>200

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.

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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
Infrastructure
Cost
Users
Pipelines
Data
>30%

reduction in HDFS block footprint

>$2M

in OEM licensing costs saved annually

50+

Kafka clusters consolidated to save costs and improve operations

Top 3 Telco

Acceldata enabled a top 3 telco improve Data Reliability, reduce data costs, and speed performance, fixed broken data processes, freed-up additional capacity, accelerated data product delivery, and cloud migration.

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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
Pipelines
Data
Infrastructure
Cost
Users
45

Billion rows verified for data quality in under 2 hours

20%

reduction in storage consumption by eliminating 9PB of stagnant data in 1st 2 weeks

<2

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
Pipelines
Users
Data
Infrastructure
Cost
46%

improvement in data quality

10+

data engineers directed to higher value added tasks

>200

proprietary scripts and other patchwork approaches replaced

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data observability journey?