Project Name
4-Hour Reporting Delays Eliminated for an Enterprise Customer With an Automated Odoo ETL Pipeline and Apache Druid Analytics
A leading enterprise customer managed complex operational and commercial data across several distinct platforms, including a primary MS SQL data lake and SAP records accessed via Databricks, and needed a full 360-degree view of business operations refreshed daily. Reporting relied on manual exports, SharePoint uploads, and Power BI imports, leaving stakeholders working with data up to 4 hours stale, with heavy maintenance overhead and no proactive monitoring. Ksolves designed and implemented an automated ETL framework hosted natively on the customer’s Odoo platform, consolidating both source systems into Apache Druid for sub-second analytical queries through Apache Superset, eliminating manual handling, closing the 4-hour latency gap, and saving an estimated 15+ engineering hours per week.
- Delayed Data Availability: The primary operational data lake refreshed only every 4 hours, forcing stakeholders to make decisions based on stale information instead of real-time operational state.
- Siloed Architecture: Business datasets were distributed across disconnected repositories, including MS SQL, SAP, and Databricks, with no unified analytical data warehouse tying them together.
- Fragile Manual Processes: Reporting required manually exporting datasets, uploading files to SharePoint, and triggering Power BI imports, which caused broken refreshes, human error, and substantial engineering overhead.
- Dashboard Latency and Bottlenecks: Power BI suffered slow query load times when processing large datasets with complex, on-the-fly transformations.
- Lack of Proactive Monitoring: The organization had no automated alert mechanism to notify operations teams when critical metrics crossed designated thresholds.
Ksolves engineered a modern, end-to-end data platform centered on a custom ETL module deployed directly on the customer's Odoo server, giving the organization centralized orchestration and single-pane-of-glass management.
- Direct Data Extraction: Connected the Odoo-hosted pipeline directly to MS SQL Data Lake and Databricks, performing automated delta and incremental extraction with no intermediate file stops.
- Transformation and Validation: Applied standardized data cleansing, null handling, schema alignment, business logic calculations, and enrichment before loading.
- High-Performance OLAP Loading: Wrote processed data into Apache Druid, an open-source, real-time analytical database built for sub-second queries on high-volume datasets.
- Interactive Analytics Layer: Connected Apache Superset directly to Druid, delivering rapid dashboard rendering, multi-dimensional drill-downs, and instant metric filtering.
- Automated Alerts and Reporting: Integrated an alerting engine that dispatches automated email reports and instant notifications whenever key KPIs breach safety or performance thresholds.
Technology Stack
| Category | Technology |
|---|---|
| Source Systems | MS SQL Data Lake, SAP via Databricks |
| ETL and Orchestration | Custom ETL Module on Odoo Server |
| Analytical Storage | Apache Druid |
| Visualization | Apache Superset |
| Alerting and Reporting | Superset Alert Engine, Email Gateways |
- Drastic Overhead Reduction: Replaced manual export/import workflows with scheduled ODOO pipelines, saving an estimated 15+ operational engineering hours per week.
- Sub-Second Dashboard Queries: Migrating analytical workloads to Apache Druid accelerated complex dashboard queries from multi-second load times down to sub-second responses.
- Near Real-Time Operational Visibility: Eliminated the 4-hour batch latency window, empowering managers to react immediately to live operational changes.
- Improved Data Trust & Integrity: Centralizing transformation logic inside the ODOO module eliminated human error and discrepancies across reporting teams.
- Cost Efficiency & Scalability: Leveraging open-source analytical tech (Druid + Superset) avoided scaling licensing costs while establishing a highly scalable foundation for future enterprise analytics.
An enterprise customer whose fragmented data sources, 4-hour reporting delays, and manual SharePoint-dependent ETL process left decision-makers working from stale data was transformed through Ksolves’ big data and analytics services. An automated Odoo-hosted ETL pipeline feeding Apache Druid and Apache Superset now delivers sub-second dashboard performance and automated threshold-based alerting in place of manual monitoring. Manual file handling was eliminated. The 4-hour latency window was closed. An estimated 15+ engineering hours are saved every week, on an open-source analytical foundation that scales without added licensing cost.
Ready to Replace Manual Reporting Delays With Real-Time, Sub-Second Analytics?
