How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture

This blog post is co-written with Hardeep Randhawa and Abhay Kumar from HPE.

HPE Aruba Networking, formerly known as Aruba Networks, is a Santa Clara, California-based security and networking subsidiary of Hewlett Packard Enterprise company. HPE Aruba Networking is the industry leader in wired, wireless, and network security solutions. Hewlett-Packard acquired Aruba Networks in 2015, making it a wireless networking subsidiary with a wide range of next-generation network access solutions.

Aruba offers networking hardware like access points, switches, routers, software, security devices, and Internet of Things (IoT) products. Their large inventory requires extensive supply chain management to source parts, make products, and distribute them globally. This complex process involves suppliers, logistics, quality control, and delivery.

This post describes how HPE Aruba automated their Supply Chain management pipeline, and re-architected and deployed their data solution by adopting a modern data architecture on AWS.

Challenges with the on-premises solution

As the demand surged with time, it was imperative that Aruba build a sophisticated and powerful supply chain solution that could help them scale operations, enhance visibility, improve predictability, elevate customer experience, and drive sustainability. To achieve their vision of a modern, scalable, resilient, secure, and cost-efficient architecture, they chose AWS as their trusted partner due to the range of low-cost, scalable, and reliable cloud services they offer.

Through a commitment to cutting-edge technologies and a relentless pursuit of quality, HPE Aruba designed this next-generation solution as a cloud-based cross-functional supply chain workflow and analytics tool. The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections. Overall, this new solution has empowered HPE teams with persona-based access to 10 full-scale business intelligence (BI) dashboards and over 350 report views across demand and supply planning, inventory and order management, SKU dashboards, deal management, case management, backlog views, and big deal trackers.

Overview of the solution

This post describes how HPE Aruba automated their supply chain management pipeline, starting from data migration from varied data sources into a centralized Amazon Simple Storage Service (Amazon S3) based storage to building their data warehouse on Amazon Redshift with the publication layer built on a third-party BI tool and user interface using ReactJS.

The following diagram illustrates the solution architecture.

In the following sections, we go through the key components in the diagram in more detail:

Source systems
Data migration
Regional distribution
Orchestration
File processing
Data quality checks
Archiving processed files
Copying to Amazon Redshift
Running stored procedures
UI integration
Code Deployment
Security & Encryption
Data Consumption
Final Steps

1. Source systems

Aruba’s source repository includes data from three different operating regions in AMER, EMEA, and APJ, along with one worldwide (WW) data pipeline from varied sources like SAP S/4 HANA, Salesforce, Enterprise Data Warehouse (EDW), Enterprise Analytics Platform (EAP) SharePoint, and more. The data sources include 150+ files including 10-15 mandatory files per region ingested in various formats like xlxs, csv, and dat. Aruba’s data governance guidelines required that they use a single centralized tool that could securely and cost-effectively review all source files with multiple formats, sizes, and ingestion times for compliance before exporting them out of the HPE environment. To achieve this, Aruba first copied the respective files to a centralized on-premises staging layer.

2. Data migration

Aruba chose AWS Transfer Family for SFTP for secure and efficient file transfers from an on-premises staging layer to an Amazon S3 based landing zone. AWS Transfer Family seamlessly integrates with other AWS services, automates transfer, and makes sure data is protected with encryption and access controls. To prevent deduplication issues and maintain data integrity, Aruba customized these data transfer jobs to make sure previous transfers are complete before copying the next set of files.

3. Regional distribution

On average, Aruba transfers approximately 100 files, with total size ranging from 1.5–2 GB into the landing zone daily. The data volume increases each Monday with the weekly file loads and at the beginning of each month with the monthly file loads. These files follow the same naming pattern, with a daily system-generated timestamp appended to each file name. Each file arrives as a pair with a tail metadata file in CSV format containing the size and name of the file. This metadata file is later used to read source file names during processing into the staging layer.

The source data contains files from three different operating Regions and one worldwide pipeline that needs to be processed per local time zones. Therefore, separating the files and running a distinct pipeline for each was necessary to decouple and enhance failure tolerance. To achieve this, Aruba used Amazon S3 Event Notifications. With each file uploaded to Amazon S3, an Amazon S3 PUT event invokes an AWS Lambda function that distributes the source and the metadata files Region-wise and loads them into the respective Regional landing zone S3 bucket. To map the file with the respective Region, this Lambda function uses Region-to-file mapping stored in a configuration table in Amazon Aurora PostgreSQL-Compatible Edition.

4. Orchestration

The next requirement was to set up orchestration for the data pipeline to seamlessly implement the required logic on the source files to extract meaningful data. Aruba chose AWS Step Functions for orchestrating and automating their extract, transform, and load (ETL) processes to run on a fixed schedule. In addition, they use AWS Glue jobs for orchestrating validation jobs and moving data through the data warehouse.

They used Step Functions with Lambda and AWS Glue for automated orchestration to minimize the cloud solution deployment timeline by reusing the on-premises code base, where possible. The prior on-premises data pipeline was orchestrated using Python scripts. Therefore, integrating the existing scripts with Lambda within Step Functions and AWS Glue helped accelerate their deployment timeline on AWS.

5. File processing

With each pipeline running at 5:00 AM local time, the data is further validated, processed, and then moved to the processing zone folder in the same S3 bucket. Unsuccessful file validation results in the source files being moved to the reject zone S3 bucket directory. The following file validations are run by the Lambda functions invoked by the Step Functions workflow:

The Lambda function validates if the tail file is available with the corresponding source data file. When each complete file pair lands in the Regional landing zone, the Step Functions workflow considers the source file transfer as complete.
By reading the metadata file, the file validation function validates that the names and sizes of the files that land in the Regional landing zone S3 bucket match with the files on the HPE on-premises server.

6. Data quality checks

When the files land in the processing zone, the Step Functions workflow invokes another Lambda function that converts the raw files to CSV format followed by stringent data quality checks. The final validated CSV files are loaded into the temp raw zone S3 folder.

The data quality (DQ) checks are managed using DQ configurations stored in Aurora PostgreSQL tables. Some examples of DQ checks include duplicate data check, null value check, and date format check. The DQ processing is managed through AWS Glue jobs, which are invoked by Lambda functions from within the Step Functions workflow. A number of data processing logics are also integrated in the DQ flow, such as the following:

Flag-based deduplication – For specific files, when a flag managed in the Aurora configuration table is enabled, the process removes duplicates before processing the data
Pre-set values replacing nulls – Similarly, a preset value of 1 or 0 would imply a NULL in the source data based on the value set in the configuration table

7. Archiving processed files

When the CSV conversion is complete, the original raw files in the processing zone S3 folder are archived for 6 months in the archive zone S3 bucket folder. After 6 months, the files on AWS are deleted, with the original raw files retained in the HPE source system.

8. Copying to Amazon Redshift

When the data quality checks and data processing are complete, the data is loaded from the S3 temp raw zone into the curated zone on an Redshift provisioned cluster, using the COPY command feature.

9. Running stored procedures

From the curated zone, they use AWS Glue jobs, where the Redshift stored procedures are orchestrated to load the data from the curated zone into the Redshift publish zone. The Redshift publish zone is a different set of tables in the same Redshift provisioned cluster. The Redshift stored procedures process and load the data into fact and dimension tables in a star schema.

10. UI integration

Amazon OpenSearch Service is also integrated with the flow for publishing mass notifications to the end-users through the user interface (UI). The users can also send messages and post updates via the UI with the OpenSearch Service integration.

11. Code Deployment

Aruba uses AWS CodeCommit and AWS CodePipeline to deploy and manage a bi-monthly code release cycle, the frequency for which can be increased on-demand as per deployment needs. The release happens across four environments – Development, Testing, UAT and Production – deployed through DevOps discipline, thus enabling shorter turnaround time to ever-changing user requirements and upstream data source changes.

12. Security & Encryption

User access to the Aruba SC360 portal is managed via SSO with MFA authentication and data security managed via direct integration of the AWS solution with HPE IT’s unified access management API. All the data pipelines between HPE on-premises sources and S3 are encrypted for enhanced security.

13. Data Consumption

Aruba SC360 application provides a ‘Private Space’ feature to other BI/Analytics teams within HPE to run and manage their own data ingestion pipeline. This has been built using Amazon Redshift data sharing feature, which has enabled Aruba to securely share access to live data in their Amazon Redshift cluster, without manually moving or copying the data. Thus, the HPE internal teams could build their own data workloads on core Aruba SC360 data while maintaining data security and code isolation.

14. Final Steps

The data is finally fetched into the publication layer, which consists of a ReactJS-based user interface accessing the data in the Amazon publish zone using Spring Boot REST APIs. Along with data from the Redshift data warehouse, notifications updated in the OpenSearch Service tables are also fetched and loaded into the UI. Amazon Aurora PostgreSQL is used to maintain the configuration values for populating the UI. To build BI dashboards, Aruba opted to continue using their existing third-party BI tool due to its familiarity among internal teams.

Conclusion

In this post, we showed you how HPE Aruba Supply Chain successfully re-architected and deployed their data solution by adopting a modern data architecture on AWS.

The new solution has helped Aruba integrate data from multiple sources, along with optimizing their cost, performance, and scalability. This has also allowed the Aruba Supply Chain leadership to receive in-depth and timely insights for better decision-making, thereby elevating the customer experience.

To learn more about the AWS services used to build modern data solutions on AWS, refer to the AWS public documentation and stay up to date through the AWS Big Data Blog.

About the authors

Hardeep Randhawa is a Senior Manager – Big Data & Analytics, Solution Architecture at HPE, recognized for stewarding enterprise-scale programs and deployments. He has led a recent Big Data EAP (Enterprise Analytics Platform) build with one of the largest global SAP HANA/S4 implementations at HPE.

Abhay Kumar is a Lead Data Engineer in Aruba Supply Chain Analytics and manages the Cloud Infrastructure for the Application at HPE. With 11+ years of experience in the IT industry domains like banking, supply chain and Abhay has a strong background in Cloud Technologies, Data Analytics, Data Management, and Big Data systems. In his spare time, he likes reading, exploring new places and watching movies.

Ritesh Chaman is a Senior Technical Account Manager at Amazon Web Services. With 14 years of experience in the IT industry, Ritesh has a strong background in Data Analytics, Data Management, Big Data systems and Machine Learning. In his spare time, he loves cooking, watching sci-fi movies, and playing sports.

Sushmita Barthakur is a Senior Solutions Architect at Amazon Web Services, supporting Enterprise customers architect their workloads on AWS. With a strong background in Data Analytics and Data Management, she has extensive experience helping customers architect and build Business Intelligence and Analytics Solutions, both on-premises and the cloud. Sushmita is based out of Tampa, FL and enjoys traveling, reading and playing tennis.