23M+
Public WiFi hotspots
~31.6M
broadband customers
~64M+
institutions in network
A large telecommunications provider needed to migrate and modernise its network analytics and monitoring infrastructure, reliably ingesting, querying, and alerting on massive volumes of telecom network data across AWS and third-party observability platforms. As part of its Data and Cloud Modernization Services and Solutions, Inferenz built an event-driven data pipeline integrating AWS-native analytics with Datadog, ELK, and real-time Slack alerting, achieving zero-disruption migration, a 60% reduction in manual monitoring workload, and cutting incident detection-to-response time by over 40%.
The organization’s analytics and monitoring infrastructure had grown across disconnected tools and platforms, making unified observability, proactive alerting, and scalable pipeline management operationally unsustainable.
Operational data and analytics pipelines were built on AWS Athena with no integration into a modern monitoring platform. There was no live dashboard visibility, no unified metrics layer, and no way to detect or respond to pipeline failures in real time.
Query results from Athena had to be manually moved to downstream systems. There was no automated mechanism to push outputs to Datadog for live analytics or to S3 for long-term audit and storage, creating operational overhead on every run.
Monitoring was spread across disconnected tools with no unified alerting layer. Critical incidents were identified reactively, after they had already affected operations. There was no mechanism to route notifications to the right teams in real time through a shared communication channel.
Every new data flow required a custom Lambda build from scratch. There were no shared payload templates, no parameterised execution patterns, and no CI/CD structure to accelerate or standardise deployment — making each new integration a weeks-long exercise.
Inferenz built a robust, event-driven data pipeline connecting the organization’s AWS analytics infrastructure with modern monitoring and real-time alerting, replacing manual workflows with a governed, automated, and fully observable platform.
Data originating in S3 is catalogued via AWS Glue Crawler, queried through Athena, and orchestrated by AWS Step Functions. Dynamic Lambda functions automatically extract and transform query results, pushing outputs simultaneously to Datadog for live analytics and to S3 for long-term storage and audit — with no manual transfer steps at any stage.
Metrics and logs are delivered simultaneously to CloudWatch, ELK Stack, and Datadog, giving the operations team a single, consolidated view across real-time and historical data. EventBridge feeds logs directly into the monitoring pipeline, eliminating the gaps that had previously left failures invisible until they caused downstream impact.
CloudWatch Alarms route critical notifications directly to Slack, ensuring support teams are notified the moment an issue surfaces — cutting detection-to-response time by more than 40% and replacing the reactive, manual monitoring model entirely.
GitHub-managed payload templates enable parameterised Lambda execution across all data flows. New data sources and workflows can be onboarded in days rather than weeks, with consistent deployment standards enforced through CI/CD pipelines built on GitHub Actions.
SNS and SQS integration within Step Functions provides robust, decoupled event handling across the full pipeline, ensuring reliable message delivery and notification orchestration even under high-volume, concurrent workloads.
during migration
Analytics workflows migrated from Athena to Datadog with no interruption to live operations or downstream reporting at any stage.
in manual workload
Automated monitoring and data transfer eliminated the manual effort previously required to move, track, and validate pipeline outputs across platforms.
incident response
Slack-integrated CloudWatch Alarms cut the time from issue detection to team notification, replacing reactive monitoring with proactive real-time alerting.
new data flows from weeks
Parameterised Lambda payload templates and GitHub CI/CD reduced new data source onboarding from weeks to days, with consistent deployment standards across every flow.
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