Data Analytics Platform (DAP) 2.0 Delivery.




Background


The organization aimed to establish a scalable, cloud-native analytics platform to support enterprise data ingestion, transformation, machine learning, and analytics use cases. Existing data pipelines and analytics workflows were fragmented and difficult to scale, limiting the ability of business teams to reliably access and operationalize data.

DSM Analytics Platform (DAP) 2.0 was designed as a centralized AWS-based data and analytics ecosystem with standardized ingestion patterns, monitoring, machine learning lifecycle automation, and governed data consumption capabilities. 


Objectives


  • Enable scalable onboarding of enterprise data sources into a centralized cloud data lake
  • Standardize data ingestion, monitoring, and provisioning across domains
  • Automate machine learning development, deployment, and lifecycle management
  • Provide governed and simplified data consumption for analytics, dashboards, and applications
  • Improve platform reliability, observability, and operational efficiency




Solution


Designed and implemented standardized data ingestion pipelines to onboard SAP and non-SAP data sources into AWS S3 using Theobald Xtract Universal and AWS Glue, aligned with defined data lake architecture and naming standards. 


Developed the DAP Data Provisioning & Monitoring (DPM) Framework to capture runtime metrics across ingestion and cleansing processes and provide end-to-end pipeline visibility via AWS QuickSight dashboards. 


Built an MLOps Framework enabling automated machine learning model build, deployment, and lifecycle management through standardized templates and defined lifecycle stages (development, build, deploy, consumption). 


Designed and implemented the Data Consumption Framework providing secure and governed access to data from the Data Lake, Enterprise Data Warehouse (Redshift), and ML models (SageMaker) through APIs and access controls. 


Enabled platform automation to simplify ingestion, cleansing, monitoring, and ML deployment processes, improving usability and reducing manual operational effort. 


Challenges


  • Integrating diverse enterprise data sources across business domains
  • Ensuring scalable and standardized ingestion architecture
  • Providing full observability of complex data pipelines
  • Automating ML lifecycle while maintaining governance and security
  • Simplifying data access for multiple user personas and applications



Results


  • Established scalable enterprise data ingestion and monitoring frameworks
  • Improved reliability and observability of data pipelines across domains
  • Accelerated ML model deployment through standardized MLOps processes
  • Enabled governed and simplified access to data and analytics assets
  • Reduced manual operational workload through platform automation



Lessons Learned


  • Standardized ingestion and monitoring frameworks are critical for platform scalability
  • End-to-end observability significantly improves operational reliability
  • MLOps automation accelerates ML adoption and reduces deployment risk
  • Clear data consumption patterns enable broader business adoption
  • Platform usability and automation drive sustained user engagement




Conclusion


The DSM Analytics Platform (DAP) 2.0 delivery established a robust, scalable AWS-based enterprise analytics platform with standardized data ingestion, monitoring, machine learning lifecycle automation, and governed data consumption. The initiative significantly improved data accessibility, analytics enablement, and operational efficiency across business domains.