Case Studies

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

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World's Largest Food and Beverage Company

Acceldata replaced internal tools with scalable data observability across Snowflake, SAP/HANA, and finance pipelines, unifying 40+ data products and cutting Executive Daily Sales Dashboard investigation time from 2.5+ hours per incident.

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Problem

Global and regional platforms across Snowflake, Databricks, Synapse, ADF, and Power BI created a decentralized data landscape. Global and local numbers often failed to match, while pipeline failures, stale data, and reconciliation errors surfaced late. Executive Daily Sales Dashboard delays occurred frequently with 2.5+ hours of investigation per incident.

Solution

Acceldata replaced internal tools with scalable data observability across Snowflake, SAP/HANA, and finance data pipelines. Standardized reconciliation, anomaly detection, and freshness controls turned pipeline failures into actionable ServiceNow tickets. Data Products unified 40+ critical products, surfacing accuracy, consistency, timeliness, and anomalies in one view.

Results

Strengthened monthly and quarterly close with repeatable, auditable reconciliation. Standardized financial close with 1,800 active DQ and reconciliation policies across 8 domains and 24 data products. Scaled monitoring across 15,000 pipelines and 1,600 ADF pipelines. Gave business owners unified health visibility across 40+ enterprise data products in scope.

1,800

active DQ and reconciliation policies across 8 domains

15,000

pipelines monitored, including 1,600 ADF pipelines

40+

enterprise data products with unified health visibility

Global Technology Infrastructure Leader

Acceldata unified supply chain observability across 18,000 pipelines, consolidating legacy DQ rules and automating source-to-ODS reconciliation to eliminate 4-6 hours of manual work per week.

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Problem

Thousands of interdependent pipelines moved supply chain data across Oracle, Kafka, SingleStore, PostgreSQL, and Iceberg, and failures at any hop sent missing, stale, or inconsistent data downstream, putting orders, bill-of-material records, and analytics at risk. Source-to-ODS reconciliation required 4 to 6 hours of manual work each week, while legacy Ataccama rules struggled to scale and silent pipeline failures went undetected.

Solution

Unified supply chain observability for pipeline monitoring, reconciliation, and data quality across 18,000 pipelines. Ataccama rules were consolidated, source-to-ODS reconciliation automated, and domain policies applied to critical supply chain data. Operational lineage, reliability scoring, and ADM-assisted analysis accelerated incident management workflows.

Results

Onboarded 18,000+ pipelines for end-to-end observability. Eliminated 4 to 6 hours per week of manual source-to-ODS reconciliation. Operationalized more than 10,000 checks and 5,500 data quality rules. 28 data products monitored with reliability, lineage, and policy context. Migrated Ataccama DQ rules into a single consolidated Acceldata policy.

18,000+

pipelines onboarded for end-to-end observability

10,000+

checks and 5,500 data quality rules operationalized

4-6 hrs/week

of manual reconciliation eliminated

Global Pharmaceutical Leader

Acceldata was deployed as the Enterprise Observability Platform across manufacturing, research labs, clinical, and study data, giving unified visibility into 15,000+ pipelines and 40,000+ data assets, and driving an 85% MTTD improvement.

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Problem

A heavily regulated business and operational data estate spanned AWS MWAA, Databricks, dbt Cloud, Trino, Redshift, S3, and legacy pipelines across research, clinical, commercial, and manufacturing domains. AWS Step Functions pipelines were a black box when failures occurred, and downstream applications and models ran on stale or incomplete data because teams lacked real-time visibility. Volume drifts in SAP replication went unnoticed, and Study Data Tabulation compliance was often inaccurate and delayed.

Solution

Acceldata was deployed as the Enterprise Observability Platform across manufacturing, research labs, clinical, and study data to provide a unified lens into the operational health of ~15,000+ pipelines and ~40,000+ associated data assets. Open Lineage and Eventbridge were established as standards for operational visibility into AWS black box solutions.

Results

Established an Observability Center of Excellence. Illuminated AWS Step Functions pipelines from black box to full traceability for clinical trial study data compliance reporting. Achieved 85% MTTD improvement (YoY) with the SAP data replication issue. Mass onboarding of 40,000+ assets with standardized observability monitoring.

85%

MTTD improvement year-over-year

15,000+

pipelines under unified observability

40,000+

data assets onboarded with standardized monitoring

Global Semiconductors Leader

Acceldata operationalized governance controls across Oracle ERP, Snowflake, Redshift, and Databricks, lifting sales-forecast confidence from 20% to 90% and certifying enterprise data for AI in 60 days.

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Problem

Enterprise data across Oracle ERP, Snowflake, Redshift, and Databricks was not governed, and C-level executives did not trust business metrics across manufacturing, supply chain, and finance. The company's Databricks federated platform proved unreliable for enterprise KPIs, with inconsistent data and broken transformations.

Solution

The company deployed Acceldata to operationalize and measure governance controls by leveraging out-of-the-box reconciliation, freshness, drift, and anomaly monitoring. Acceldata's baseline dashboard delivered governance coverage and compliance scores for 25 enterprise KPIs, driving DQ consistently across all critical data assets and certifying for AI.

Results

~30% faster yield reporting. Improved DRR and gross margin (sales forecast) confidence score from 20% to 90%. Deployed to production in 45 days, meeting an aggressive go-live deadline for yield reporting. Delivered a trust index in sales forecast in ~80 days post-implementation.

90%

sales-forecast confidence, up from 20%

60 days

to certify enterprise data for AI readiness

25 KPIs

covered by governance and compliance scoring

Global Telecommunications Leader

Acceldata rebuilt how compute is scheduled and allocated, decoupling storage from compute so the company cut compute cores by 85% and processing time by 2.5x versus its prior Databricks and Hadoop environments.

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Problem

The company was spending several million dollars a year on Databricks and Snowflake and wanted to cut costs by repatriating ETL workloads on the silver and bronze layers while leaving the gold medallion layer in Databricks. One node had 14 PB of free storage but couldn't take more work because compute was maxed out, and the team needed to consolidate compute and cut vendor dependency.

Solution

Acceldata rebuilt how compute gets scheduled, allocated, and monitored, decoupling storage from compute so capacity constraints don't cascade. Elastic burst absorbs demand spikes without oversizing permanent infrastructure, and bronze and silver layer ETL jobs moved off rented cloud compute onto owned infrastructure, while gold layer work stayed where collaboration and data science capabilities justify the spend.

Results

85% fewer cores needed: 8,400 (xLake) vs 12,000 (Databricks) vs 55,490 (Hadoop). 77% less memory required: 43 TB (xLake) vs 65 TB (Databricks) vs 182 TB (Hadoop). 2.5x faster processing, an average runtime improvement across all benchmark workloads.

85%

fewer compute cores needed vs. Databricks

77%

less memory required vs. Databricks

2.5x

faster processing across benchmark workloads

Global Pharmaceutical Leader

Acceldata established ADOC as the data quality and observability layer for ESG reporting, cutting end-to-end business data assurance from 3 months to 2 weeks and data profiling-to-business-rule cycles from ~40 days to under 48 hours.

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Problem

The Enterprise Data Office spent approximately 3 months certifying drug discovery, clinical trial, and study data, impacting the velocity of drug development and commercialization. For ESG compliance reporting, the team relied heavily on manual checks, which were prone to errors and not scalable.

Solution

Established ADOC as the data quality and observability layer for ESG reporting, automating reconciliation, profiling, pipeline monitoring, and critical-data identification across the reporting lifecycle. Building on that foundation, ADM's agentic workflows accelerate business-rule recommendation, generation, and approval, while operational metric dashboards provide a consistent basis for certifying data products.

Results

Reduced end-to-end business data assurance workflow from 3 months to 2 weeks. Reduced data profiling-to-business-rule cycles from ~40 days to under 48 hours. Catches quality failures before downstream consumption with scheduled alerting. Automated energy sustainability checks across sites, countries, and energy types, with scalable, auditable governance at lower cost and complexity.

3 months → 2 weeks

end-to-end business data assurance workflow

~40 days → <48 hrs

data profiling-to-business-rule cycle

Automated

energy sustainability checks across sites and energy types

Top National Consumer Bank

Acceldata established enterprise monitoring across 20 critical ADS pipelines spanning key consumer banking programs, reducing critical lending data validation time from 4+ hours to 15 minutes.

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Problem

Legacy and cloud platforms drove 4+ hour data validation cycles, with weekend failures going undetected for days. Unresolved failures across banking, lending, cards, credit risk, marketing, and AML created regulatory exposure. Fragmented controls made approved data source governance manual and reactive across seven lines of business.

Solution

Acceldata established enterprise monitoring across 20 critical ADS pipelines spanning key consumer banking programs. ~20,000 DQ rules run across ~670 active pipelines, validating quality, freshness, reconciliation, drift, anomalies, and business rules. ADM replaced manual rule authoring with automated, scalable governance across lines of business.

Results

Reduced critical validation cycle from 4+ hours to 15 minutes. Home lending scaled to ~2,000 rules in a single month, proving new domain ramp-up without infrastructure rebuild. Automated 20K+ policy executions that required manual intervention during an incident. Replaced third-party tools and custom-coded DQ checks, cutting costs across teams. Improved DQ scores and reconciliation coverage across CSBB Deposits, Branch, and lending programs.

4+ hrs → 15 min

critical lending data validation time

~20,000

DQ rules running across ~670 active pipelines

20K+

policy executions automated

Global Information Provider

Acceldata modernized data supply operations with automated observability across BigQuery and Airflow, reducing global data-cloud processing time from 9 days to 7 hours across 30,000+ sources in 220+ markets.

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Problem

The monolithic DQ tool required dedicated developers to build rules in code, limiting speed and scalability. Data anomalies across 600M+ business records were difficult to track, and each rule required a separate narrow data extract, driving up storage and compute costs. Missed SLAs and engineering rework made data operations unsustainable across 220+ country markets.

Solution

Acceldata modernized data supply operations with automated observability across BigQuery and Airflow. Reusable rulesets, anomaly detection, and drift monitoring replaced manual validation across hundreds of policies. Scaled quality controls globally, enabling faster issue detection and delivery of trusted commercial data within SLA.

Results

Reduced global data-cloud processing time from 9 days to 7 hours. Accelerated regional data quality scorecard creation from 1 month to 1 day (30x). Built scalable quality rules across 30,000+ sources in 220+ markets. Automated validation of 1,400+ daily ingested supply chain files.

9 days → 7 hrs

global data-cloud processing time

30x

faster regional data quality scorecard creation

30,000+

sources monitored across 220+ markets

Top Global Biopharma Company

Acceldata gave a leading global biopharmaceutical company one observability layer across Snowflake, S3, and Databricks, supporting three production programs and more than 800 governed policies while reducing weeks of manual work to hours.

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Problem

As the organization expanded across research, pharmaceutical sciences, and global data integrity programs, manual controls slowed delivery and weakened regulatory confidence.

Different asset names across development, staging, and production meant policies had to be remapped or rebuilt for each environment
Trend analysis across more than 1,200 checks required ad hoc extraction and took days, limiting early visibility into recurring issues
Recreating a data source took four to five hours, while large policy updates took days because settings were changed individually

Solution

Acceldata established an observability layer across the company's R&D data environment, standardizing policies for data quality, reconciliation, freshness, anomaly detection, and schema drift.

Business Data Assurance
Enterprise Data Reconciliation
Automated Pipeline Health Monitoring
Policy Lifecycle Management

Results

Prioritized use cases went live in 60 days. One observability layer now supports three production programs and more than 800 governed policies, reducing weeks of manual work to hours.

Policy and asset promotion: 2–3 weeks to hours
Automated summary reporting: Days to daily automated updates
Data source migration: 4–5 hours to minutes
Bulk configuration updates: Hours to minutes

Weeks → Hours

time to deliver trusted data

~95%

reduction in manual work

800+

governed policies — across R&D environments

Top U.S. Consumer Bank

Acceldata plays a key role in the bank’s enterprise-scale data operations. From marketing to lending, Acceldata ensures compliant data pipelines, trusted insights, and proactive risk mitigation.

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Problem

Regulatory risk, revenue leakage, and SLA delays due to:

Inaccurate targeting and non-compliant campaigns
Legacy infrastructure slowing loan decisions
Missing feeds and campaign latency
Inconsistent manual QA across business units

Solution

Embedded observability across the data lifecycle:

Automated anomaly detection and rule-based monitoring
Freshness, completeness, and drift checks for onboarding and lending
Reusable DQ policies across Consumer, Auto, Mortgage, and Marketing
AI-ready data contracts and lineage enforcement

Results

Millions in revenue recovered via timely, accurate incentive offers
SLA breaches reduced by 96%, improving marketing and decisioning speed
$10M in fines avoided through audit-ready lineage and controls
35%+ rule reuse across critical workflows

Observing

Pipelines

Users

Data

Compliance

Infrastructure

Cost

96%

reduction in SLA breaches

$10M+

regulatory fines avoided

35%+

policy reuse across domains

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 issue resolution 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

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

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

Hershey's

Acceldata monitors their Snowflake environment with out-of-the-box graphical representations of usage trends, anomaly detections, and user adoption, eliminating the need for an in-house monitoring tool and reducing manual work significantly.

Problem

Monitoring a newly integrated Snowflake environment for cost, usage, and user metrics presented a significant challenge. Additionally, detecting anomalies in cost trends, query spillages, and warehouse timeouts could take weeks, leading to substantial Snowflake costs.

Solution

ADOC Snowflake Compute offered a solution that effectively reduced costs due to spillages and optimized warehouse processes. Additionally, ADOC Compute provided out-of-the-box graphs that illustrated Snowflake usage and trends, facilitating a clear understanding of Snowflake spend.

Results

A centralized monitoring platform enables a thorough understanding of all Snowflake expenditures and provides timely alerts regarding critical cost spillages for each department within the organization.

Observing

Pipelines

Users

Data

Infrastructure

Cost

<2 days

to detect anomalies vs weeks

200+

warehouses successfully onboarded into ADOC

100+

snowflake users effectively managed with ADOC

Top Cyber Threat Intelligence Company

Acceldata significantly optimized cloud infrastructure costs by identifying process improvements, analyzing cost overages at early stages, and controlling overspending.

Problem

The existing cloud platform experienced a significant increase in monthly expenses, which impacted overall cost management. Additionally, they encountered challenges in scaling their infrastructure to meet the growing activities and user base of their business without incurring additional costs.

Solution

With Cost Observability, cost savings opportunities were identified and implemented. This included effectively right-sizing warehouse recommendations, analyzing queries, grouping queries with estimated costs, and providing intuitive graphical representations of cost metrics.

Results

Significant monthly cost reduction of $10,000 achieved without compromising performance or functionality. Optimized and identified spillage queries, improving 3-4 daily processes. Despite a drastic increase in cloud activities and users, the rate of cost increase has decreased. Additionally, two new data sources have been onboarded following effective cost management for the first data source.

Observing

Pipelines

Users

Data

Infrastructure

Cost

$120,000

annual cost savings identified within one month

process improvements

< 3 months

to achieve time to value

Top Telco in Indonesia

Acceldata enabled a top 3 telco improve data quality, eliminate wasteful data costs, and speed performance, fixed broken data processes, freed-up additional capacity, accelerated both 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

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

Data

Infrastructure

Cost

Users

>50 Billion

Quality Checks under 2 Hrs. Over a billion rows verified for 50 critical data quality rules in under 2 hours

9 Petabytes

of stagnant data identified and eliminated in under 2 weeks, reducing storage consumption and costs.

$350k

in hard cost savings achieved in under 2 weeks

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