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ACI Infotech

Enterprise data and AI, engineered and run in production.

ACI Infotech is an enterprise data and AI engineering firm headquartered in Somerset, New Jersey, with delivery hubs worldwide. We build the data foundation, put AI on top of it, and run both in production for enterprises in financial services, healthcare, retail, manufacturing, and energy.

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  • Data Engineering
  • Applied AI & ML
  • Cyber Security
  • Cloud Modernization
  • Managed Operations
  • App Development
  • Quality Engineering
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  • GCC & Captive Centers
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  • ACI Interactive
  • ArqAI Labs
  • Databricks
  • Microsoft Azure
  • Snowflake
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  • SAP
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  • Financial Services
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ACI Infotech
  • Founded 2006
  • 1,200+ engineers
  • 500+ enterprise projects
  • 11 global delivery hubs
  • ISO 27001:2022
  • CMMI Level 3
  • Great Place to Work Certified

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All playbooks

/ Cloud playbook

Hadoop → Cloud

Legacy to Cloud Migration

Our most deployed playbook - migrating from aging on-prem Hadoop/Teradata/Oracle to modern cloud with 3x better ROI.

  • 52x deployed
  • Cloud
  • 9-18 months typical
  • 15-30 consultants

/ Typical outcomes

68%

Cost reduction

10x

Processing speed

$20M

3-year savings

Zero

Downtime

/ Overview

What this playbook is for

Your on-premise data platform is becoming a liability. Hadoop clusters need constant care and feeding. Teradata licensing costs grow 15-20% annually. Oracle DBAs are retiring faster than you can hire replacements. Scaling requires 6-12 month hardware procurement cycles while cloud competitors spin up capacity in minutes. Your best data engineers are leaving for companies with modern stacks. This playbook, our most deployed with 52 successful migrations, provides the proven path from legacy on-premise data platforms to modern cloud architectures with zero downtime and 3x better ROI than lift-and-shift.

/ Challenge pattern

When this playbook applies

This playbook fits organizations facing these common challenges:

  • 01On-premise Hadoop/Teradata/Oracle platforms are 5-10 years old, approaching end of vendor support
  • 02Infrastructure and licensing costs growing 15-20% annually with no corresponding capability improvement
  • 03Scaling requires 6-12 month hardware procurement cycles. Business needs capacity now, not next fiscal year.
  • 04Maintenance consumes 40%+ of data team time. Engineers patch and tune instead of building value.
  • 05Talent retention is failing. Top engineers want cloud experience; they leave for modern shops.
  • 06Technical debt accumulates. Workarounds and patches make the platform increasingly fragile.

/ Solution approach

How the pattern runs

  • Assessment & Planning: Comprehensive workload analysis categorizing each job by complexity, criticality, and cloud-readiness. Build migration roadmap based on risk and value.
  • Re-Architecture: Transform workloads for cloud-native benefits. Lift-and-shift misses 70% of cloud value. Re-architecting delivers 3x better ROI.
  • Parallel Run Strategy: New cloud platform runs alongside legacy for 3-6 months. Results validated against production before cutover.
  • Automated Migration: Tooling for schema conversion, data movement, and code translation. Manual migration doesn't scale.
  • FinOps from Day One: Cloud cost optimization practices established before migration starts. Prevents cost surprises post-migration.
  • Team Upskilling: Training and certification program starts 3 months before migration. Teams migrate with knowledge, not fear.

/ Key learnings

Hard-won lessons from 52 deployments

01

Lift-and-shift misses 70% of cloud benefits. Re-architecture is more work upfront but delivers 3x better ROI.

02

Zero-downtime migration requires parallel run strategy. Plan for 3-6 months of dual operation.

03

FinOps practices must be established before migration. Cloud costs can exceed on-prem without governance.

04

Team upskilling should start 3 months before migration. Migrating to a platform nobody understands fails.

05

Legacy SQL and stored procedures need modernization. Porting bad patterns to cloud creates expensive bad patterns.

06

Data validation automation is critical. Manual comparison of billion-row tables isn't feasible.

/ Stack

  • AWS/Azure/GCP
  • Databricks/Snowflake
  • Automated Migration Tools
  • Data Validation
  • Terraform IaC
  • Cost Optimization

/ Industries served

  • Financial Services
  • Healthcare
  • Retail
  • Education

/ Results

What the pattern delivers

68%

Cost reduction

Infrastructure, licensing, and operational savings versus legacy platform

10x

Processing speed

Cloud-native architecture delivers order of magnitude performance improvement

$20M

3-year savings

Average TCO reduction across mid-size mainframe migrations (~$6.5M/yr against a typical $9-10M/yr baseline). Larger footprints land north of $40M.

Zero

Downtime

Parallel run strategy enables seamless cutover with no business disruption

/ More patterns

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Let's Walk Through This Playbook