Duration: 22:06
PART 1 — Analytical Summary 🚀
Context 💼
This session, led by Simon Stuppen (Managing Partner at Much Consulting), walks through how to modernize Odoo data workflows with an ELT (Extract, Load, Transform) approach. Much Consulting runs large, ambitious Odoo projects (thousands of legal entities, tens of thousands of daily orders), where analytics at scale can throttle transactional performance. The talk explains why and how to decouple analytics from production, what tools to use, and a step-by-step architecture to turn raw Odoo data into fast, BI-ready datasets.
Why it matters 🧠
Transactional workloads in Odoo are small, update-heavy, and localized; analytics workloads scan and aggregate large portions of data. Running both on the same database creates contention: analytics slow down ops, and ops slow down analytics—especially once databases grow beyond ~10 GB on core tables, user counts exceed 100, or analytics demand increases. Beyond performance, analytics teams struggle with Odoo’s complex schema (hundreds of tables and thousands of fields, with technical joins and nuanced price/tax logic), and organizations need to blend Odoo with external sources (ads, ecommerce, machine data) while meeting governance requirements (e.g., removing sensitive payroll details before analytics).
Core ideas & innovations ⚙️
The proposed architecture embraces a clean ELT pipeline. Extraction and loading first create a safe, performant copy of the production database—either via Odoo.sh backups (up to five per day) or, preferably, through PostgreSQL logical replication (CDC) to maintain a near-real-time replica (sub-second lag) when you control hosting (e.g., Odoo.sh Dedicated, partner hosting, or self-hosted). An alternative, application-layer streaming via webhooks/API is possible, but it increases production load and is best kept for selective data, not full replication.
Transformation then reshapes the raw schema into analytics-friendly, flat tables using DBT. This means merging related entities (e.g., sales lines with products, categories, deliveries, invoices) to avoid joins at query time, choosing clear, business-oriented naming (e.g., defaulting to net amounts where relevant), and standardizing across Odoo versions so dashboards don’t break during upgrades. A dual-schema strategy—raw Odoo schema plus a separate data warehouse schema—preserves full flexibility.
Orchestration and analytics complete the picture: an open-source orchestrator (Dexter) schedules and monitors replication, transformations, documentation generation, and metadata refreshes in BI tools. Analytics tools—like Apache Superset, Power BI, Tableau, or Looker Studio—connect directly to PostgreSQL or cloud warehouses (Google BigQuery, Snowflake, Amazon Redshift, Azure Synapse) for fast, click-driven exploration. The team maintains a reusable base of ~25 flattened tables (e.g., sales order lines, account move lines, delivery and manufacturing lines) and extends per client. Typical cadence is near-real-time where possible; otherwise, every four hours with a nightly quiet window.
Impact & takeaways 💬
This setup isolates operational performance from analytics demands, simplifies BI with human-readable, join-free models, and eases maintenance across Odoo upgrades through stable transformations and versioned code (e.g., in GitHub). It also strengthens governance: sensitive fields can be stripped or aggregated before landing in analytics, satisfying strict data security expectations.
For smaller instances and simple questions, stay inside Odoo: pivots, spreadsheets, and the improved Odoo 19 dashboards are often sufficient. If you need a bit more without the full pipeline, apps like Ninja Dashboards Advanced can help—though be mindful of performance and security. For multi-source analytics, heavy BI, or ML/AI workloads, the ELT route is the pragmatic path. Open source is a key theme: PostgreSQL + DBT + Dexter + Apache Superset provide enterprise-grade capability without expensive ETL licenses.
Notably, Odoo is signaling deeper support for replica-aware operations (a core flag to run certain actions on a replica), potentially enabling future dashboards to read from replicas by default—an encouraging direction for scale.
Key conclusions:
- A transactional Odoo database is not a BI-ready warehouse at scale—separate the concerns.
- Use Odoo standard features first; escalate to ELT only when justified by complexity, performance, or multi-source needs.
- Favor an open-source stack; it’s capable, cost-effective, and aligned with Odoo’s tech foundations.
PART 2 — Viewpoint: Odoo Perspective
AI-generated creative perspective inspired by Odoo’s vision.
At Odoo, our mission is to make powerful software simple. The idea of separating analytics from operations—while keeping everything integrated—is not a contradiction; it’s a path to scale without compromising speed or user experience. If we can let teams explore insights with zero fear of slowing down their daily work, we’re doing our job.
I’m enthusiastic about the momentum around replicas and better dashboards. The more we can embed this thinking natively—leaving room for open-source tools where they shine—the more our community benefits. Simplicity at the surface, robustness under the hood: that has always been our approach.
PART 3 — Viewpoint: Competitors (SAP / Microsoft / Others)
AI-generated fictional commentary. Not an official corporate statement.
The proposal reflects sound data engineering: isolate workloads, use CDC, denormalize for BI, and orchestrate with open tooling. It’s aligned with practices we encourage in enterprise data platforms. The governance angle (filtering sensitive data) is particularly important given regional compliance standards and global-scale reporting requirements.
The challenge for Odoo-centric stacks remains enterprise breadth at very large scales—complex compliance, multi-ERP harmonization, and deep verticals. However, the UX focus and open-source pragmatism are strong differentiators. For organizations already invested in platforms like Microsoft Fabric or SAP Datasphere, the pattern integrates cleanly, but it demands disciplined ownership of transformations, lineage, and SLAs to meet enterprise expectations.
Disclaimer: This article contains AI-generated summaries and fictionalized commentaries for illustrative purposes. Viewpoints labeled as "Odoo Perspective" or "Competitors" are simulated and do not represent any real statements or positions. All product names and trademarks belong to their respective owners.