iSpot's AdsTech cloud migration is a move from a legacy on-premises ETL platform to an AWS-native data architecture built for ad measurement at scale. iSpot's Conversion Team needed to modernize a high-volume data processing platform to better monitor advertiser metrics at scale. ASCENDING helped the team move from a cost-heavy on-premises model to an AWS-native architecture that improved operational stability, governance, and cost efficiency.
BackgroundTV Ad Measurement Analytics Built on High-Volume ETL Pipelines
iSpot TV is a leading TV ad measurement and attribution company that provides analytics for advertisers and media stakeholders. Its Conversion Team runs large ETL and analytics workflows that process millions of data points every day to support reporting, ad-hoc queries, and performance measurement.
As processing demand increased, the existing on-premises environment created scalability and efficiency limitations. iSpot wanted a cloud foundation that could support sustained data growth without excessive rework.
The ChallengeA Migration Prototype That Missed Cost and Performance Targets
The challenge is a stalled migration prototype that missed both cost and performance targets while still needing stronger governance controls. iSpot had already tested an initial migration prototype, but the first approach did not meet expected performance or cost goals.
- The client engineering team had limited hands-on AWS migration experience.
- The initial prototype exceeded cost estimates and underperformed operationally.
- A pure lift-and-shift strategy was no longer acceptable to technical stakeholders.
- The target architecture needed to preserve Spark workload logic while improving scalability.
- The solution had to enforce stronger data governance and compliance controls.
AWS-Native Modernization Without a Disruptive Rewrite
ASCENDING is a migration partner that pairs pragmatic AWS execution with long-term data platform modernization guidance. ASCENDING was selected for its ability to combine pragmatic migration execution with AWS-native modernization guidance. Instead of forcing a disruptive rewrite, the team designed a path that preserved existing Spark processing patterns while introducing managed AWS data services and policy-based data governance.
As an AWS Advanced Tier Services Partner with data engineering expertise, ASCENDING aligned architecture decisions to both business economics and long-term platform manageability.
The SolutionContainerized Spark on Amazon EKS with AWS Lake Formation Governance
The solution is a full re-architecture of iSpot's ETL pipeline for containerized, AWS-native execution rather than a simple lift-and-shift. ASCENDING proposed a containerized migration strategy centered on Amazon EKS for Spark workload execution, combined with an AWS data lake governance layer to unify multi-source analytics access.
- Containerized conversion Spark programs and deployed them on Amazon EKS to improve scaling control and job-level tuning.
- Minimized refactoring by preserving core processing behavior while modernizing runtime infrastructure.
- Implemented AWS data lake governance with AWS Lake Formation and cataloged datasets across S3, Redshift, and Snowflake access patterns.
- Enabled governed, user-friendly analytics consumption for reporting, ad-hoc querying, and KPI monitoring.
50% Lower Costs and a Fully Migrated AWS Data Platform
The outcome is a fully migrated, cost-efficient analytics platform that meets iSpot's governance and compliance requirements. The migration delivered both technical and business impact, giving iSpot a more resilient analytics operating model on AWS.
- Transitioned all ETL workloads from on-premises infrastructure to AWS.
- Achieved a 50% cost reduction compared with the prior on-premises environment.
- Improved governance controls with AWS Lake Formation for granular data access management.
- Strengthened compliance alignment for GDPR and CCPA policy requirements.
- Established a scalable foundation for future growth in ads analytics data volume.
Built with AWS Data Lake and Kubernetes Processing Services
Frequently Asked Questions
How long does an ETL migration like iSpot's typically take from prototype to production?
Timeline is a phased rollout that runs the containerized Spark jobs on Amazon EKS alongside the legacy on-premises pipeline, so ad measurement reporting stays available while workloads are validated and cut over in stages.
How does the new architecture keep multi-source analytics governed and compliant?
Governance is centralized through AWS Lake Formation's fine-grained access policies, unifying Amazon Redshift and Snowflake access under one permission model rather than managing security separately in each analytics tool.
Does moving to Amazon EKS require rewriting the existing Spark jobs?
Rewrites were minimized rather than required — the migration preserved core Spark processing logic and moved the same job definitions onto containerized, horizontally scalable infrastructure instead of a full application rewrite.
