How Titan America Unified Manufacturing Sensor Data with Tiger Cloud on AWS

Titan America unified fragmented data pipelines onto AWS, enabling real-time operational intelligence and simplified reporting across three plants.

Titan America, a heavy building materials producer with cement plants, aggregates operations, and ready-mix concrete facilities across the eastern United States, moved its manufacturing telemetry pipelines from direct CSV dumps to Tiger Cloud on AWS. The outcome: a single source of truth for OT (operational technology) sensor data from 24/7 production facilities, continuous aggregates feeding real-time dashboards and month-end reporting, and infrastructure that works reliably. In this case study, we will walk through how Titan America evaluated the move and what the unified data foundation means as the business scales.

About the Company & Team

Manufacturing facilities produce continuous streams of sensor data: vibration, temperature, pressure, throughput, equipment state. That data lives in plant SCADA systems and tells you whether a production line is running well or heading toward failure. Getting data out of the plant, into a queryable system, and connected to operational applications is not easy. Legacy pipelines rely on CSV exports, scheduled batch dumps, or direct connections that fail when the network hiccups.

Titan America runs three plants (Miami, Tampa, and a recently acquired facility in Pennsylvania) 24 hours a day producing cement clinker, aggregates, and ready-mix concrete. Downtime hits customer deliveries immediately. The Industrial Digitization team needed a data architecture that could ingest, store, and serve millions of sensor readings per day with the reliability and query performance that real-time manufacturing operations require.

The team is led by Matt Markham (Manager of Industrial Digitization), Chase Witt (Senior Software Engineer), and Dante Ricketts (Jr. Data Engineer), working with data scientists and analysts to turn sensor streams into actionable insights.

The Challenge

Titan America's old pipeline was fragile and expensive. Raw OPC (OLE for Process Control) sensor data from plant SCADA systems flowed through a custom Python app, which dumped CSV records directly into Domo, their BI tool. The architecture had multiple breaking points.

The BI tool was the source of truth. Every analytical query hit Domo. There was no efficient way to store historical data, apply transformations, or run time-series queries without slowing down BI. Network interruptions caused data loss or required manual recovery. The company couldn't implement data retention policies or tiered storage. And operations like compression, continuous aggregation, or state-machine logic (tracking exactly when a machine state changed, what events happened in a shift) required custom code outside the database, slowing iteration.

As Titan America added plants, the data volume grew but the architecture stayed stuck on CSV-to-BI. The company needed a proper data foundation, what they called the bronze layer, which would act as a single source of truth for all OT data.

Why Tiger Cloud: An Architecture-First Decision

Titan America evaluated databases with one requirement: a single source of truth for time-series data that could handle millions of readings per day, serve historical queries efficiently without BI slowdown, support continuous aggregation for real-time dashboards, and stay operationally simple. Self-hosting was an option they quickly ruled out. Running a time-series database in-house means dedicated DevOps, capacity planning, backup and recovery. It also requires taking on overhead that distracts from the core problem: reliable ingestion and fast queries.

Tiger Cloud on AWS offered what the team needed: fully-managed PostgreSQL built for time-series data, automatic scaling, compression and data tiering, and continuous aggregates as a first-class feature. More importantly, Tiger Cloud meant the team could focus on the data model and business logic, not infrastructure or maintenance.

The choice wasn't flashy. As Witt said, “We started with TimescaleDB when it came to replacing what was originally collecting our plant data. We could store this amount of data and query it efficiently. So we used Tiger [Cloud] as our data foundation - the single source of truth.” Tiger Cloud on AWS gave them exactly what they needed: performant, reliable infrastructure for operational and analytics workloads on time-series data.

The Tiger Cloud Stack

The data flows from plant to cloud in layers. SCADA systems emit OPC readings continuously: thousands of data points per minute from kiln temperature, clinker moisture, concrete slump, equipment vibration sensors, and more. Rather than write directly to the cloud, Titan built a custom buffering app between SCADA and Tiger Cloud. This app captures readings, stores them locally, and implements store-and-forward logic: if network connectivity drops, the app queues messages and retransmits when connectivity returns. In a 24/7 manufacturing environment, network blips are inevitable.

Data flows from the buffering app into Tiger Cloud on AWS through a reliable ingestion pipeline. The cloud instance has five Tiger Cloud services running TimescaleDB, using roughly 7 CPU cores and 913 GB of active storage. The primary hypertable is node_value; every OPC reading lands here with a timestamp, node ID, and value.

Titan uses Tiger Data compression extensively. The node_value table is configured with compress_segmentby='node_id' and compress_orderby='server_timestamp DESC'. This means historical data compresses automatically by sensor ID; recent data stays uncompressed for fast queries and older data shrinks by orders of magnitude while remaining queryable. Data tiering is enabled too: hot data stays in fast storage for 1-2 weeks, then migrates to lower-cost tiers. The result: about 19 TB of tiered data on Amazon S3 across the instance.

Continuous aggregates sit on top of the raw hypertable and offer pre-computed, materialized views that update as new data arrives; examples include aggregate metrics per sensor and plant-level summaries. These power real-time dashboards in Grafana and simplify consumption in Domo. The continuous aggregate approach lets Titan capture complex business logic (state machines, timeline functions, process metrics) directly in the database using TimescaleDB functions, rather than in application code.

Downstream, data flows from Tiger Cloud into a silver layer (Amazon Athena with S3 parquet storage) and into Domo for business intelligence. A Python app reads from TimescaleDB, builds analytical datasets, and writes them to the lake. This separation of concerns–raw bronze layer (TimescaleDB), silver refined layer (Amazon Athena), gold curated layer (Domo)–lets the company scale analytics independently from the operational time-series database.

Results: What We've Seen

• Transforming fragile pipelines into reliable infrastructure (while reducing costs): The first win was reliability. Moving from CSV dumps to a proper time-series foundation cut operational complexity. According to Markham, “We shifted our raw time series ingestion from Python dumping CSVs directly into Domo into [Tiger Cloud]. That's been one of the least problematic and most robust things we've ever done.” For a 24/7 manufacturing operation, that's a significant statement. Reliable infrastructure means fewer incidents, less manual work, and more engineering time spent on features instead of firefighting. Throughout their usage on Tiger Data, the team backported nearly 1 TB of legacy OPC data into Tiger Cloud and enabled compression immediately. That historical dataset shrank significantly, letting the company retain years of sensor data without proportional storage cost increases.

• Continuous aggregates simplifying month-end operations: One of the most valuable changes was continuous aggregates feeding SAP and month-end reporting. Before Tiger Cloud, generating monthly reports meant ad-hoc queries, manual aggregations, and reconciliation work. Now continuous aggregates run automatically, computing summaries (total cement produced, average kiln temperature, equipment utilization) that feed directly into ERP and reporting systems. The team has run these aggregates in production for over two months with zero data-integrity issues. This isn't glamorous, but for a manufacturing business on tight margins with customer SLAs, reliable month-end reporting is foundational. It removes a recurring bottleneck and gives finance and operations teams confidence that numbers are consistent across all systems.

• Foundation for operational intelligence: With a unified time-series foundation in place, Titan is exploring new use cases that weren't feasible before. The team is testing AI agents that ingest shift event logs and generate summaries, identifying anomalies and actionable issues automatically. They're evaluating pgvector on their Tiger Cloud instance for semantic search over historical events. And they're investigating Tiger Lake for long-term retention and bidirectional lake connectivity, letting cold data flow back into real-time queries when needed.

• Growth through consolidation: The consolidation of manufacturing facilities shows land-and-expand in action. Titan started with two plants and recently added a third. Rather than duplicate infrastructure or struggle to integrate new plants into a legacy pipeline, the team added SCADA sources to the buffering app and expanded Tiger Cloud capacity. The single source of truth meant integrating new facilities was straightforward: no redesign, no complex migrations.

Looking Ahead

Titan is targeting 19+ TB of tiered data retention and exploring a five-year archival strategy where historical time-series data transitions from hypertables to continuous aggregates to parquet in S3 with Amazon S3 Glacier lifecycle policies. The shift from Databricks to Athena for the lakehouse is cost-driven: same analytics capability, significantly lower spend. This cost efficiency, combined with Tiger Cloud's operational simplicity, lets the company invest more in advanced features (AI agents for predictive maintenance, semantic search over event logs, and real-time anomaly detection) rather than infrastructure overhead.

The decision that matters most isn't about speed. It's architectural coherence. Everything at Titan America runs on one database: raw OPC data, continuous aggregates, month-end reports, dashboards, and emerging AI workloads. One source of truth. That choice eliminates the operational burden of split systems: no pipelines to maintain, no query paths to reconcile, no infrastructure complexity that grows with the data. As Titan adds plants and generates more sensor data, the data model stays constant. Performance scales with the workload. Tiger Cloud's automatic scaling on AWS frees the team to solve manufacturing problems instead of managing databases.