Real-time analytics: Oldcastle integrates Infor with Amazon Aurora and Amazon Quick Sight

_This post is cowritten with Avdhesh Paliwal from Oldcastle._

Oldcastle APG is one of the largest suppliers of construction materials in North America, including asphalt and concrete. The company also provides construction and paving services across more than 150 facilities. As the company migrated from on-premises systems to Infor Cloud ERP hosted on Amazon Web Services (AWS), they faced a critical challenge: maintaining the real-time operational reporting capabilities that hundreds of users across customer service, finance, logistics, and manufacturing depended on daily.

This post explores how Oldcastle used AWS services to transform their analytics and AI capabilities by integrating Infor ERP with Amazon Aurora and Amazon Quick Sight. We discuss how they overcame the limitations of traditional cloud ERP reporting to deploy real-time dashboards and build a scalable analytics system. This practical, enterprise-grade approach offers a blueprint that organizations can adapt when extending ERP capabilities with cloud-native analytics and AI.

**Challenges with cloud ERP reporting**

The primary challenge that we faced was finding a solution that could accomplish the following:

• **Maintain real-time data access** – Our on-premises environment supported hundreds of complex real-time reports, but Infor ERP Cloud’s configuration-based reporting covered minimal reports of our operational needs.
• **Support complex reporting requirements** – Users needed multi-dimensional analysis across customer service, finance, logistics, and manufacturing functions.
• **Provide seamless user experience** – Business users demanded integrated reporting within the ERP network without switching between multiple systems.
• **Enable advanced analytics** –We needed capabilities for demand forecasting, machine learning (ML) capabilities, and intelligent search across real-time data.
• **Scale efficiently** – The solution needed to support over 100 concurrent users and process millions of transactions while maintaining performance.
• **Expose Data using API**– The solution needed to expose data through APIs, allowing both external and internal applications to access and consume the data securely and efficiently.

Our existing batch reporting process created significant operational challenges across our organization. We had to wait for batch reports, which consumed valuable time and led to delays in critical decision-making across many of our teams. This lag prevented us from capitalizing on real-time business insights and responding quickly to operational issues or economic changes. Without immediate data visibility, our managers couldn’t make timely, data-driven decisions, resulting in missed opportunities for improvement and competitive advantage. Our IT team also struggled with constant report requests but lacked a scalable system to deliver them efficiently, further compounding the productivity loss across our organization.

**Solution overview**

AWS Solutions Architects worked closely with our application team to build a comprehensive analytics and AI solution to address these challenges. The architecture uses Infor Data Fabric Stream Pipelines to deliver real-time data to AWS. It powers operational dashboards, artificial intelligence and machine learning (AI/ML) models, and intelligent search capabilities. This approach aligns with Infor’s broader strategy of integrating ERP data, data lake information, machine learning (ML) predictions, and documentation to provide comprehensive end-to-end business solutions.

**Real-time data streaming architecture**

The foundation of our solution is Infor’s Data Fabric Stream Pipelines, an add-on feature that provides real-time streaming data processing. When data events are ingested into Data Fabric, Stream Pipelines processes them immediately and continuously without waiting for storage in the data lake. This approach minimizes the data journey and accelerates operations, helping us extract insights from our data in real time.

**The end-to-end workflow consists of the following components:**

**Data ingestion** – Infor Data Fabric tables stream changes in real-time. We enabled Stream Pipelines as an add-on feature within our Infor Cloud ERP environment. We Configure the specific ERP table that we want to stream (such as sales orders, inventory, financial transactions) to publish change events immediately upon data modification. Stream Pipelines captures insert, update, and delete operations with metadata about the operation type and timestamp.

**Load distribution** – Because Infor can’t reach our private VPC directly, we use Elastic Load Balancing (ELB) to distribute traffic and provide secure database access. We implemented a Network Load Balancer (NLB) with static Elastic IP addresses in public subnets, giving us stable, allowlisted IP addresses for Infor’s outbound connections. We configured an Amazon Relational Database Service (Amazon RDS) router with Amazon Elastic Compute Cloud (Amazon EC2) instances as NLB targets. These routers forward traffic from the NLB to our Amazon Aurora database in the private subnet using iptables NAT rules. This makes sure that even if the IP of Aurora changes during failover, our static Elastic IPs remain constant. We configured security groups to accept HTTPS traffic (port 443) only from Infor’s IP ranges on the NLB and allow traffic only from the NLB to the RDS routers.

**Connection management** – We use Amazon RDS Proxy to manage database connections and provide automatic failover. We deployed RDS Proxy in the private subnet between our RDS router instances and Aurora cluster to pool and reuse connections. This is critical for handling our high-frequency streaming data. We configured the proxy with IAM authentication for secure credentials and set connection pool parameters based on our expected concurrent stream volume to handle burst traffic without overwhelming the database. With automatic failover enabled, if our primary Aurora instance fails, RDS Proxy automatically redirects traffic to the promoted replica, maintaining continuous data flow.

**Data storage –** We store our operational data in Amazon Aurora PostgreSQL- Compatible Edition with multi-Availability Zone deployment for high availability. We provisioned an Aurora PostgreSQL cluster with one writer instance and multiple reader instances across different Availability Zones. We designed our database schema to handle the incoming streaming data, storing it in JSONB columns for flexible querying while using the native JSON functions of Aurora PostgreSQL when we need to parse and normalize specific fields. We created indexes on frequently queried fields to maintain query performance as our data volume grows. We also configured automated backups with point-in-time recovery and set up the automatic storage of Aurora scaling to accommodate our data growth.

**Analytics and visualization** – Amazon Quick Sight delivers the interactive dashboards and pixel-perfect reports our teams need. We created a Quick Sight account and established a connection to our Aurora PostgreSQL database using VPC connectivity with credentials stored in AWS Secrets Manager. We identified which datasets benefit from SPICE (Super-fast, Parallel, In-memory Calculation Engine) caching—typically aggregated or frequently accessed data—and configured incremental refresh schedules to keep them current. We built our dashboards using the visual interface of Quick Sight, using calculated fields for business logic, parameters for user interactivity, and row-level security rules to make sure that users only see data that they’re authorized to access. For pixel-perfect reports, we use the pixel-perfect report feature of Quick Sight to create formatted documents suitable for printing or regulatory compliance.

**Embedded integration** – We securely embedded Amazon Quick Sight dashboards within Infor OS through Amazon API Gateway, which generates dynamic URLs for seamless user access. We enabled Quick Sight embedding in our AWS account and registered our Infor domain. We created an API Gateway REST API with Lambda functions that authenticate users, validate Infor session tokens, and call QuickSight’s `GenerateEmbedUrlForRegisteredUser` API to produce time-limited, signed URLs with row-level security. Our Lambda function maps Infor user roles to Quick Sight permissions and applies dashboard filters based on the user’s organizational context. We configured CORS settings in API Gateway to allow requests from our Infor domain and implemented rate limiting. On the Infor side, we embedded the Quick Sight dashboards using iframe elements that call our API Gateway endpoint, providing a seamless experience where our users access analytics without leaving the ERP interface.

The following diagram illustrates the real-time analytics architecture:

This embedded experience aligns with Infor’s broader system strategy of integrating insights seamlessly into workflows.

**Results and business impact**

The implementation using this architecture on AWS brought substantial benefits, directly addressing the critical challenges that we faced and demonstrating measurable value in employee productivity and core business process optimization.

**Business process improvement**

The solution successfully addressed Oldcastle’s key operational challenges:

**Challenge: Limited visibility into real-time operations**

– Solution delivered: Deployed over 50 complex dashboards and reports in eight months, providing immediate visibility across customer service, finance, logistics, and manufacturing.- Technical achievement: Used Infor Data Fabric Stream Pipelines to process data events immediately upon ingestion, alleviating delays from traditional batch processing.- Impact: Real-time streaming architecture using the NDJSON format makes sure decision-makers have access to current operational data when they need it most.

**Challenge: Fragmented user experience requiring multiple systems**

– Solution delivered: Dashboards embedded directly into the Infor environment through Amazon API Gateway.- Technical achievement: Generated dynamic URLs for secure embedding with single sign-on capabilities.- Impact: Users access insights without leaving their familiar interface, with personalized views based on roles and permissions that maintain context across the application.

**Challenge: Inflexible reporting limiting business agility**

– Solution delivered: Both interactive dashboards and pixel-perfect reports are available to meet diverse business needs.- Technical achievement: Quick Sight SPICE caching enables subsecond response times on complex analytics across large datasets.- Impact: On-demand access for immediate insights, scheduled distribution, custom formatting aligned with corporate standards, and multiple export formats (PDF, CSV, Excel).

**Challenge: Delayed decision-making due to outdated data**

– Solution delivered: Real-time visibility into operations enabled faster, data-driven decisions.- Technical achievement: The Multi-AZ deployment of Amazon Aurora PostgreSQL maintains high availability and continuous data access.- Impact: Decision-makers can respond immediately to operational issues and economic changes with current, reliable data.

**Scalability and performance**

The architecture delivered exceptional scale and performance, addressing concerns about future growth: – High concurrency: Supports over 100 concurrent users without performance degradation- Data volume handling: Processes millions of transactions daily in real-time- Elastic scaling: Aurora read replicas automatically scale based on demand- Future-ready: Architecture designed to expand to additional regions and use cases- Cost efficiency: AWS services avoided complex third-party integrations, with infrastructure costs scaling efficiently with business growth- API capabilities: Ability to expose data using AWS technologies enables integration with third-party and internal applications

**Conclusion**

Our journey proves that cloud ERP migrations don’t require sacrificing real-time operational reporting capabilities. By combining Infor Data Fabric Stream Pipelines with AWS analytics and AI services, we’ve maintained real-time data access, accelerated innovation, improved user experience, and built a system that scales efficiently as our business needs evolve. The combination of Infor’s enterprise-grade ERP system with the comprehensive analytics capabilities of AWS has given us the best of both worlds. As we expand our AWS analytics and AI capabilities, we’re not just maintaining parity with on-premises systems, we’re unlocking new sources of business value that weren’t possible before.

**Further reading**

For more information on the services mentioned in the post, see the following resources:

**AWS services:**

• **Amazon Aurora PostgreSQL Features** – Learn more about the high-availability database that powers Oldcastle’s real-time data storage and Multi-AZ deployment strategy
• **Amazon Quick Sight Embedded Analytics** – Explore how to embed interactive dashboards and pixel-perfect reports directly into your enterprise applications, as demonstrated in Oldcastle’s Infor OS integration
• **Amazon Bedrock for Generative AI** – Discover opportunities to enhance your analytics system with AI-powered insights and intelligent search capabilities
• **Elastic Load Balancing** – Understand how to distribute traffic and secure database connections when integrating cloud ERP systems with AWS services
• **Amazon API Gateway** – Learn how to create secure, dynamic URLs for embedding analytics and exposing data through APIs to internal and external applications

**Infor Resources:**

• **Infor Data Fabric Documentation** – Deep dive into Stream Pipelines configuration and real-time data streaming capabilities that enable immediate operational insights
• **Bringing the Power of AWS Generative AI to Infor’s Industry-First Approach** – Explore Infor’s strategic vision for integrating AI capabilities across their ERP system network
• **Strategies, Patterns, and Security Measures for Integrating Infor Cloud Suite with AWS** Review architectural patterns and security best practices for enterprise-grade ERP-to-AWS integrations

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About the authors