Automated SAP inventory synchronization to Redshift with Bedrock Knowledge Base for 70% faster reporting

Cloud Platforms Amazon Web Services (AWS)
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1

We implemented an end-to-end inventory analytics solution that reduced report generation time from 45 minutes to 12 minutes - a 70% improvement. The architecture integrates SAP ERP inventory data with AWS analytics services for real-time visibility across 23 distribution centers.

The challenge was replacing nightly batch ETL jobs that couldn’t keep pace with business needs. Executives needed current inventory positions for decision-making, but our legacy data warehouse was 6-12 hours stale. We needed near-real-time replication from SAP without impacting production systems, plus natural language query capabilities for business users.

Our solution combines AWS Glue Zero-ETL for SAP OData replication, Redshift Serverless as the data warehouse, and Bedrock Knowledge Base for semantic search. The implementation took 8 weeks with a two-person team.

2

What about IAM and VPC configuration? SAP systems are usually in private networks. Did you use PrivateLink, or how did you secure the connectivity between SAP, Glue, and Redshift? Also curious about the least privilege IAM roles - that’s often where implementations get sloppy.

3

Great questions - let me walk through the complete implementation:

AWS Glue Zero-ETL SAP OData Replication: We created a Glue connection to SAP’s OData endpoint with these settings:


ConnectionType: ODATA
URL: https://sap-prod.company.com:8000/sap/opu/odata/sap/API_MATERIAL_STOCK_SRV
AuthType: OAuth2
PollingInterval: 300 seconds

The Zero-ETL integration monitors SAP tables MARA (material master), MARD (storage location data), and MARC (plant data). Delta detection uses SAP’s LAEDA (last change date) field. Initial full load took 4 hours for 2.3M SKUs, then incremental syncs run every 5 minutes averaging 200-500 changed records.

Key configuration: Enable parallel processing in Glue job settings (DPU=10) to handle peak change volumes during business hours.

Redshift Serverless Data Warehouse: We provisioned a Redshift Serverless namespace with 128 RPU base capacity. The Zero-ETL integration creates staging tables automatically:


sap_inventory.material_master
sap_inventory.stock_by_location
sap_inventory.plant_data

We then created aggregated views for common queries:

  • inventory_summary_by_plant
  • low_stock_alerts (items below reorder point)
  • inventory_aging_analysis

Redshift Serverless auto-scales to 512 RPU during morning report generation (8-10 AM), then scales down. This elasticity saves 60% vs. provisioned clusters.

Bedrock Knowledge Base Indexing: This was the most innovative part. We use Bedrock Knowledge Base with OpenSearch Serverless as the vector store:

  1. Created Lambda function triggered by Redshift data API whenever aggregate views update
  2. Lambda extracts inventory summaries and generates embeddings using Bedrock Titan
  3. Embeddings stored in OpenSearch with metadata (SKU, location, quantity, status)
  4. Business users query via natural language: “Show me critical stock items in Texas warehouses”
  5. Bedrock translates to semantic search, retrieves relevant vectors, then generates SQL for Redshift

The Knowledge Base doesn’t query Redshift directly - it uses embeddings for semantic matching, then generates precise SQL based on matched context.

IAM Least Privilege Configuration: We implemented strict role separation:


GlueServiceRole: Read access to SAP OData, Write to Redshift staging
RedshiftServiceRole: Read from S3 (for Glue), Write to data warehouse
LambdaExecutionRole: Query Redshift data API, Invoke Bedrock, Write to OpenSearch
BusinessUserRole: Query Redshift views only (no raw table access)

All roles use condition keys to enforce resource-level permissions. For example, Glue can only write to sap_inventory schema, not other Redshift schemas.

VPC Private Connectivity Setup: Security was critical - SAP is in on-premises data center:

  1. AWS Direct Connect (1 Gbps) links on-prem to AWS VPC
  2. Glue connections run in private subnets with VPC endpoints
  3. Redshift Serverless deployed in private subnet, no public access
  4. VPC endpoints for S3, Glue, Bedrock, OpenSearch (all traffic stays on AWS backbone)
  5. Security groups restrict Glue to SAP IP range only
  6. Network ACLs enforce additional layer of defense

No data traverses public internet. All communication encrypted with TLS 1.3.

Business Value & Use Cases: The 70% improvement enabled several new capabilities:

  1. Real-time Stock Allocation: Sales can check availability during customer calls instead of waiting for overnight reports
  2. Predictive Replenishment: ML models on fresh data predict stockouts 3 days ahead vs. 1 day with old system
  3. Natural Language Analytics: Executives ask questions in plain English: “Which products have highest turnover in Q4?” - no SQL needed
  4. Cross-DC Optimization: Real-time view across all 23 DCs enables dynamic inventory balancing, reducing emergency transfers by 40%
  5. Supplier Collaboration: Near-real-time data shared with key suppliers via secure API for vendor-managed inventory

ROI: Project cost $180K (8 weeks × 2 engineers + AWS services). Annual savings: $520K from reduced emergency shipments, better inventory turns, and eliminated legacy data warehouse licenses.

The natural language interface was the game-changer for adoption. Business users who never touched SQL now run their own analyses. Report requests to IT dropped 65%.

Happy to share more details on specific components if helpful!

4

How did you handle the Bedrock Knowledge Base indexing? That’s the part I’m fuzzy on - how does it connect to Redshift data and enable natural language queries? Is there a vector database in between, or does Bedrock query Redshift directly?

5

SAP side required minimal changes - just enabling OData services for the inventory tables (MARA, MARD, MARC). The key was configuring change data capture intervals. We set it to 5-minute polling which balances freshness with SAP system load. Glue automatically handles the delta detection using SAP’s change timestamps.

6

This sounds impressive. What was the most challenging part of the Glue Zero-ETL setup? We’re considering similar architecture but concerned about SAP OData performance impact. Did you need to tune anything on the SAP side to handle the continuous replication?

7

The 70% improvement is compelling for our leadership. Can you share more about the actual business value? What specific use cases did this enable that weren’t possible before? We need concrete examples to justify similar investment.