Real-time data pipelines
Important streaming events could move through the platform with substantially lower latency.
Data Platforms & Engineering
How OriginLabs Reduced Analytics Latency from 24 Hours to Under One Minute for a Global Streaming Platform
Client: ViewLift
OriginLabs redesigned an unreliable analytics platform, replacing fragile batch workflows with real-time data pipelines, proactive alerting, optimized ETL processes, and more dependable client-facing dashboards.
24 hours → < 1 minuteReporting latency after the platform redesign
Business context
Analytics was central to how the platform operated.
ViewLift is an end-to-end OTT streaming platform used by sports, media, entertainment, and broadcasting organizations to operate branded services across web, mobile, connected television, and other digital platforms.
Timely, accurate reporting supported decisions across several internal and external stakeholder groups.
Executive leadershipCustomer successMarketingClient analytics users
The reliability problem
The system could fail silently—and customers sometimes noticed first.
The existing platform relied heavily on batch processing and lacked sufficient monitoring and observability. Pipelines could fail without timely alerts, leaving dashboards stale, incomplete, or incorrect.
Engineering teams were pulled into recurring diagnosis and support after customer impact. The system needed a redesign around speed, reliability, data quality, and operational visibility—not another incremental patch.
01Batch pipeline fails
02Failure remains undetected
03Dashboard data becomes stale
04Customer reports the issue
The platform redesign
A faster platform designed to surface problems before publication.
OriginLabs combined real-time processing with improved batch workflows, validation, monitoring, and client-facing analytics. Each part addressed a specific failure mode in the previous system.
Optimized batch ETL
Scheduled workflows were redesigned for better performance, consistency, and data quality.
Validation and quality controls
Incomplete, inconsistent, or delayed data could be identified before reaching reporting layers.
Monitoring and alerting
Pipeline and quality issues could be surfaced before they affected client dashboards.
Simplified solution flow
From platform event to trusted analytics.
This public view intentionally shows the operational sequence rather than ViewLift’s internal infrastructure or proprietary architecture.
Streaming and platform events
Real-time data pipelines
Validation and quality controls
Cloud data platform
Monitoring and alerting
Client analytics dashboards
Public technology set: AWS · Amazon S3 · AWS Glue · Amazon Athena · Amazon DynamoDB · Apache Airflow · Python · SQL
Engineering challenges
Modernizing a live analytics system without compromising trust.
High-volume event processing
The platform needed to maintain speed, consistency, and operational stability as streaming activity moved through the system.
Silent pipeline failures
The redesign needed to surface problems before customers or client-facing dashboards were affected.
Real-time and batch coexistence
Low-latency processing was introduced where it mattered while scheduled workflows remained where they were operationally appropriate.
Data quality at speed
Latency improvements had to preserve accuracy, so validation was designed into the path to publication.
Redesign without disruption
The platform had to evolve while continuing to support existing stakeholders and reporting needs.
Approved results
Faster insight. Earlier detection. More dependable reporting.
→ < 1 min
Analytics moved from next-day availability to near-real-time delivery.
Improved ingestion and processing accelerated the path from source to usable data.
Failures could be identified before they created customer impact.
Is unreliable data slowing down your product?
OriginLabs helps teams redesign complex data platforms for speed, reliability, quality, and operational clarity.