Project Name
Built a Real-Time IoT Ingestion Platform Processing 3TB+ Daily with NiFi, Kafka & Cassandra
A large North American telecommunications operator managing millions of connected devices across utility meters, smart grid sensors, IoT field devices, and telecom equipment was generating over three terabytes of telemetry every 24 hours. The existing data infrastructure had no architecture to match that volume: collection queues overflowed at peak, pipeline failures caused permanent data loss with no replay mechanism, historical queries ran for hours, and the Network Operations Centre worked from data that was consistently hours old. Ksolves, an AI-first company, was engaged to design and deliver a four-tier production ingestion platform using Apache NiFi, Kafka, Cassandra, and Hadoop, built for guaranteed delivery, real-time NOC visibility, and historical analytics at full device fleet scale.
The client came to Ksolves with six structural problems blocking reliable data collection, operational visibility, and platform scalability:
- No System Could Handle Real-Time Collection at 3TB+ Daily Volume: The existing ingestion infrastructure had not been designed for the volume generated by millions of simultaneously transmitting devices. At full fleet scale, collection queues overflowed, ingest processes timed out, and the system could not sustain the throughput required, leaving large portions of device telemetry uncollected during peak transmission windows.
- Pipeline Failures Caused Permanent Data Loss: The pipeline had no guaranteed delivery mechanism. When downstream storage systems lagged or experienced failures, data in-flight was dropped with no replay capability. There was no durable buffer between collection and storage, meaning any disruption in the write path translated directly to lost device records with no recovery path.
- Historical Data Analysis Was Slow and Unreliable: Device telemetry accumulated in storage systems not designed for analytical workloads at this data volume. Historical queries, essential for capacity planning, fault root cause analysis, and SLA reporting, ran for hours or failed to complete entirely, making the accumulated data largely unusable for the business decisions that depended on it.
- Heterogeneous Device Fleet with Multiple Collection Protocols: The device estate spanned multiple manufacturers and generations, each transmitting via different protocols: SNMP polling, TCP push, REST APIs, and batch file exports. No unified collection layer existed, forcing the operations team to maintain separate collection processes per device type, each with its own failure characteristics and no shared monitoring or management plane.
- No Real-Time Visibility Into Device Health: Because the existing pipeline could not sustain real-time throughput, the Network Operations Centre was working from data that was hours old. Device fault detection, threshold alerting, and network anomaly identification all operated on stale telemetry, meaning issues that should have been caught in minutes were surfacing hours after impact had already occurred.
- Scalability Ceiling Blocking Fleet Expansion: The operator's device estate was growing continuously. New device types, new geographic areas, and new service categories were adding to the connected fleet. The existing architecture had no headroom for this growth; adding devices to the fleet worsened an already-failing pipeline rather than scaling to meet the new volume.
Ksolves designed the platform around two governing principles: no event is ever dropped, and the real-time and historical tiers never compete for the same resources. The architecture cleanly separates the collection path, the delivery guarantee layer, the real-time serving tier, and the historical analytics tier, so that a slowdown in batch analytics never creates backpressure on live device collection, and a spike in real-time queries never starves the ingest pipeline.
- Multi-Protocol Ingestion Layer: Apache NiFi was deployed as the unified collection and routing layer across all device protocols: SNMP polling, TCP push, REST APIs, and batch file feeds. Custom processors handled protocol-specific quirks and binary payload formats, while NiFi's built-in backpressure controls prevented upstream overflow when downstream systems lagged. Data lineage tracking was enabled end-to-end, providing full provenance for every device event from collection through to storage.
- Guaranteed Delivery Messaging Bus: Apache Kafka was positioned between NiFi and the downstream storage tiers as a durable, partitioned message bus. Each device category was assigned dedicated topics, ensuring that a slowdown in any one consumer, whether Cassandra writers or HDFS ingest jobs, Cassandra writers or HDFS ingest jobs, could not cause data loss upstream. Kafka's replication and retention policies provided the replay capability that the previous architecture entirely lacked, eliminating permanent data loss on downstream failure.
- Real-Time Hot Store for NOC and Dashboards: Apache Cassandra was deployed as the primary real-time data store for device telemetry, partitioned by device identifier and time bucket for sub-millisecond read performance. The NOC dashboards and fault detection systems were wired directly against Cassandra, giving operations teams live visibility into device health within seconds of telemetry receipt. Linear write scalability meant the cluster handled full 3TB+ daily ingest without degradation as the device fleet continued to grow.
- Historical Analytics and Compliance Tier: Hadoop HDFS served as the cold-data and historical analytics layer, ingesting batch windows from Kafka for long-term retention and MapReduce-based analytical workloads. Capacity planning queries, multi-year trend analysis, and SLA compliance reporting, all previously running against an overloaded real-time store, were offloaded entirely to the Hadoop tier, where they ran reliably against purpose-structured historical datasets without impacting live device collection.
Technology Stack
| Category | Technology | Role in This Enggement |
|---|---|---|
| Integration | Apache NiFi | Served as the primary ingestion orchestration layer, routing, transforming, and reliably delivering millions of device events per day from SNMP collectors, TCP push receivers, and batch file feeds into the Kafka messaging bus. Custom processors handled protocol translation and guaranteed delivery with error-path retry. |
| Messaging | Apache Kafka | Provided the high-throughput, fault-tolerant messaging backbone between NiFi and downstream storage systems. Partitioned topics per device type decoupled ingestion speed from storage write speed, eliminating the data loss that had previously occurred when downstream systems lagged during peak collection windows. |
| Database | Apache Cassandra | Served as the real-time hot-data store for device metrics and telemetry, delivering sub-millisecond reads for live device dashboards and NOC feeds. Linear write scalability ensured the cluster absorbed full device fleet volume without degradation as the connected device count grew. |
| Storage & Processing | Hadoop (HDFS) | Provided the historical cold data tier, ingesting batch windows from Kafka into HDFS for long-term retention, MapReduce-based analytics, capacity-planning workloads, and compliance archiving. Enabled slow-path analytical queries over years of device history without impacting the real-time Cassandra tier. |
Within production deployment across the full device fleet, the platform delivered five transformational outcomes:
- 3TB+ Daily Ingest Sustained at Full Device Fleet Scale: The NiFi and Kafka pipeline now sustains 3TB+ daily ingest across millions of simultaneously transmitting devices with no collection gaps, replacing an architecture that overflowed at peak and left large volumes of telemetry uncollected.
- Zero Data Loss, Guaranteed Delivery Achieved: Kafka's durable message bus with configurable retention and full replay capability ensures downstream failures cause temporary delays rather than the permanent, irrecoverable data loss that occurred with every previous storage disruption.
- Historical Query Performance Restored, Hours Reduced to Minutes: The dedicated Hadoop HDFS tier handles all historical analytical workloads independently, cutting query completion times from hours to minutes on multi-year device datasets and making accumulated telemetry usable for capacity planning and SLA reporting for the first time.
- Real-Time NOC Visibility: From Hours-Old Data to Seconds: The Cassandra real-time tier delivers live telemetry to NOC dashboards within seconds of collection, enabling fault detection and anomaly alerting before customer impact occurs, replacing a centre that consistently operated on data hours out of date.
- Unified Collection Across All Device Protocols: Apache NiFi replaced separate, unmanaged per-protocol collection processes with a single protocol-agnostic ingestion layer covering SNMP, TCP push, REST APIs, and batch file feeds, with centralised monitoring and shared backpressure controls across all device types.
“We went from dropping data every time the pipeline hiccuped to having a platform where our NOC team actually trusts what they’re seeing, because the data is live, complete, and hasn’t been lost in transit.”
– VP of Network Engineering / North American Telecom Operator
Before this engagement, the operator had no ingestion architecture capable of handling 3TB+ of daily telemetry. Data was dropped at peak, historical queries timed out, and the NOC worked from data that was hours old. Today, Ksolves has delivered a four-tier production platform using Apache NiFi, Kafka, Cassandra, and Hadoop that processes full device fleet volume with zero data loss and live NOC visibility within seconds of collection. Each tier scales independently, and the unified NiFi collection layer means new device types and protocols can be onboarded as configuration changes rather than rebuilds. For telecom and IoT operators whose pipelines have outgrown their infrastructure, explore Ksolves Big Data Services to see what a production-ready ingestion platform can deliver.
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