Our journey began with an overarching goal: to establish a unified data architecture capable of integrating diverse data sources. The challenge was integrating historical and real-time data from platforms like SAP, RetailNext, Microsoft SharePoint, SFTP, REST APIs, and other internal systems. With business-critical data scattered across these systems, we needed to break the silos and bring everything into a centralized, scalable environment.

RetailNext provided the client with real-time, in-store analytics, giving them valuable insights into customer footfall, store heat maps, and purchase patterns. However, the data from RetailNext was isolated, limiting the company's ability to combine in-store data with digital metrics. We developed pipelines to seamlessly ingest and integrate RetailNext data into the Data Lake, unlocking holistic omnichannel insights.

This integration enabled the client to analyze and correlate in-store behaviors with online shopping habits, allowing them to make data-driven decisions on store layouts, promotions, and stock levels. The integration transformed their in-store strategy, aligning it with the agility of their e-commerce operations.

SAP was the backbone for the client's financials, supply chain, and inventory management. However, the data flow between SAP and other systems was slow and prone to inconsistencies. We optimized the ETL process to ensure efficient and accurate data extraction from SAP into the Data Lake. By leveraging AWS Glue and Databricks, we streamlined data ingestion, automated the data transformation processes, and ensured real-time visibility into key metrics like inventory turnover and product margins.

The integration of SAP data provided a real-time view of product availability across both physical and digital channels, allowing the client to adjust pricing strategies and inventory distribution dynamically.

Microsoft SharePoint housed critical business documents and internal communication data, but its integration with analytics platforms was lacking. Global Nodes designed secure pipelines that allowed structured and unstructured data from SharePoint to be fed into the Data Lake. This enabled the client to correlate internal reports and business updates with real-time sales and inventory data, creating a more cohesive decision-making process.

This unification allowed cross-departmental insights, facilitating collaboration between sales, marketing, operations teams, and finance teams and improving the overall business intelligence landscape.

Beyond standardized platforms, the client relied heavily on custom REST APIs and SFTP servers for third-party integrations and internal applications. Each source brought its own complexities, from varying data formats to processing schedules. We created custom ETL pipelines to handle the ingestion, processing, and augmentation of these datasets. By standardizing data formats and automating the ingestion process, we ensured that all data streams, no matter the source, flowed into the Data Lake efficiently.

This integration enabled the client to make better use of third-party data sources such as loyalty programs and external sales channels, enhancing their ability to tailor marketing and sales strategies to customer preferences.

In any large-scale data architecture, maintaining data quality is the foundation for deriving reliable insights. For the client, the volume of data from sources like SAP, RetailNext, Microsoft SharePoint, and custom APIs presented significant challenges in ensuring consistency and accuracy. To address this, we implemented a comprehensive data validation and automation framework, leveraging the power of AWS Glue and Databricks Delta Live Tables. This framework was designed not only to handle the current data landscape but also to scale as the client’s data demands continued to grow.

The automated validation framework processed over 300 GB of daily incremental data and cross-checked it against predefined rules and formats. With over 1 million records processed per day, it was critical to ensure that only clean, validated data made its way into the analytics pipeline. We employed advanced data quality checks and cross-referenced related datasets from SAP, RetailNext, and custom APIs, ensuring synchronization across data sources. This process improved data consistency by 98%.

We automated the data cleansing process to handle over 35 TB of historical data with zero manual intervention. This significantly improved the accuracy of customer and inventory data, enabling the client to eliminate data redundancies and improve the overall quality of insights by 25%. By the end of the project, automated data validation processes had raised the data accuracy level to an industry-leading 99.9%, giving the client full confidence in their analytics and business intelligence platforms. Real-time integration reduced data latency to under 6 minutes, ensuring the client could act on fresh insights almost instantaneously.

Our robust automation not only improved current processes but also laid the groundwork for future scalability. The client can now scale their data operations seamlessly, handling increasing data volumes and complexities without sacrificing quality. This framework ensures the client’s ability to expand its analytics capabilities while maintaining the highest standards of data integrity.

The journey culminated in empowering the client’s leadership with advanced analytics through Power BI dashboards. The Data from the Data Lakes Gold layer was utilized by the business team and Power BI team to create dashboards that provided real-time insights into key performance metrics, sales trends, customer behaviors, and inventory levels.

These implementations allowed leadership to make proactive, data-driven decisions, streamlining everything from marketing campaigns to inventory management and improving customer experiences both in-store and online.