Data Engineering Services for ERP-Driven Businesses
We build the data layer between your systems and your dashboards: ETL pipelines, a warehouse you own, and modeled tables BI reads on a schedule. Sources include Odoo, NetSuite, Sage, Dynamics, storefronts and banks.
What is data engineering?
Getting numbers out of the systems that record your business and into a shape reporting and analytics can trust. Your ERP, storefront and bank each hold part of the picture, and none is built to answer a question spanning all three.
Step 1
Extract
Copied from every source on a schedule, not by hand.
Step 2
Store with history
A warehouse that keeps yesterday’s values. Your ERP overwrites them.
Step 3
Model the meaning
One written definition of revenue and margin. This is the part that decides whether it works.
Step 4
Serve
Dashboards, board packs and AI all read from one layer instead of systems that disagree.
Not a dashboard project or a one-off export: a pipeline that runs once is a migration. This is the part that runs every night and tells you when it breaks.
Our data engineering services
Six disciplines that turn scattered system data into numbers a business can act on. Most engagements use three or four of them.
ETL and ELT pipelines
Scheduled extracts from every system that holds numbers, landed raw in staging before anything is transformed. Includes one-off data migration between platforms.
Warehousing and modeling
A warehouse you own, with modeled tables and one written definition per measure, so every report inherits the same meaning of revenue and margin.
Analytics and BI
Dashboards built against the model rather than production, delivered by our Power BI team and repointed if you already have reports.
Data quality and monitoring
Validation on the way in, reconciliation against the source, and alerting on every pipeline, so a failed refresh reaches us before it reaches your team.
System-to-system sync
Records moved and reshaped between platforms on a schedule. Where records must trigger action in real time, that is integration work instead.
Database performance
Indexes, query tuning and archiving on the systems underneath, so reporting load stops competing with the people doing the work.
The systems we pull from
Pipelines, syncs and transformations that keep every system reading from the same numbers. On the storage side we build on Snowflake, BigQuery or the database you already run, and the source audit picks it rather than us.
Odoo
NetSuite
Sage
Dynamics 365
Shopify
Amazon
Stripe
PayPal
ADPOdooNetSuiteSageDynamics 365ShopifyAmazonStripePayPalADPWhat an ERP data warehouse looks like
Four kinds of systems hold the numbers: the ERP, storefronts like Shopify and Amazon that our multi-channel ecommerce builds also run on, banks and payment processors, and the long tail of 3PLs, payroll tools and spreadsheets.
The warehouse sits beside those systems, not inside any of them. Pipelines copy data in on a schedule, staging keeps the raw record, and modeled tables give every report one agreed definition.
One boundary worth naming: this is analytical plumbing, not operational integration. When orders and records need to flow between systems and trigger action, that is ERP integration work. The warehouse is the read-only copy every report agrees on.
Connecting your ERP to Power BI, properly
On the surface, ERP-to-Power BI integration is a solved problem: connector tools and your ERP’s API can put live fields on a chart in an afternoon. Two problems arrive with growth. Every dashboard refresh queries the production database, so month-end reporting and warehouse picking end up fighting over the same resource. And an ERP stores only the current state of each record, so the moment a price, a stage or an owner changes, the old value is gone. Connectors hand Power BI a flattened now.
The warehouse removes both. Pipelines copy data out on a schedule, so reporting never touches production. Staging keeps the raw record, and the model keeps history as it changes, so a question like “what did the pipeline look like on March 1” stays answerable. There is a second payoff, too: clean, modeled data is exactly what our Odoo AI agents read from when clients add AI on top.
Connector tool or built warehouse
Off-the-shelf connectors are genuinely good at what they promise, which is speed. The comparison only turns when several sources and several people depend on the numbers.
If the honest answer at your size is a connector, the source audit will say so. The failure mode we build against is not any particular tool. It is a stack where nobody owns the model.
From source audit to documented handover
Five steps. The fixed fee is scoped after the first one, so the number you approve reflects your systems, not a template.
01Source audit
Every system that holds numbers gets inventoried, and the fixed fee is scoped from what we find.
- Odoo modules, storefronts, banks and the spreadsheets doing quiet glue work
- The questions leadership actually wants answered, collected first
- The model is designed backward from those questions, not from a template
- 02
Model design workshop
Definitions get settled with your team and written down, so the numbers stop being arguable.
- What counts as revenue, and when an order counts as shipped
- How margin treats freight and channel fees
- The output is a data dictionary, the document arguments end on
- 03
Build and backfill
Pipelines are built source by source, one at a time rather than all at once.
- Raw data lands in a staging layer, then transforms into modeled tables
- History backfilled as far as each source allows
- Your trends start deep instead of starting at go-live
- 04
Dashboard wiring
The modeled tables plug into Power BI, and existing dashboards get repointed.
- Built with our Power BI reporting team
- Dashboards read from the model instead of from production Odoo
- Numbers checked against the sources before anyone relies on them
- 05
Documented handover
Everything needed to run the warehouse is handed over in writing.
- Runbooks, the data dictionary, credentials and the refresh schedule
- We keep running the pipelines on a small monthly engagement, or your team takes the keys
- Either way, you own the warehouse
Refresh cadence, agreed and monitored
Freshness is a dial, not a boast. Each step up adds cost and moving parts, so the cadence gets chosen against the decisions it serves.
Nightly, the standard
A complete, consistent picture of yesterday, ready before the first meeting. For most planning and finance decisions, this is genuinely enough.
Hourly, where it matters
Operational views like open orders and the shipping queue, refreshed through the working day, because those decisions change by the hour.
Near-real-time, when justified
Possible, and occasionally right. It has to earn its extra cost and complexity against a real decision that cannot wait an hour.
Every pipeline is monitored and alerted. If a refresh fails, you hear it from us before you notice a stale chart.
CASE STUDY
Bag House of America: ad clicks traced to revenue, across systems
Not a warehouse project by name, but exactly this discipline. Bag House needed to know which Google Ads click became which opportunity, so we carried the click identifier from the ad account into Odoo’s CRM and kept the meaning intact along the way. Data moved between systems reliably, and a question that used to be unanswerable, which ad actually paid, got a daily answer.
The dashboard side is a real service line too: our Power BI reporting team builds and maintains executive reporting for US clients, on exactly the kind of modeled layer this page describes.
What it costs
One fixed fee covers the build, agreed in writing after the source audit. Three things move the number: how many sources feed the warehouse, how deep the history backfill goes, and how contested the definitions are. None of them stays a mystery past the audit, which is why the fee follows it instead of preceding it.
Ongoing care is a small monthly fee, and it can fold into the same flat-rate HERO support plans that cover Odoo itself, from $250 a month with a one-month, no-obligation trial. No hourly billing anywhere in the engagement.
Two honest exits from this page. If a connector tool is genuinely enough at your size, the audit will say so and cost you nothing. And if the systems underneath are the real mess, a warehouse will faithfully report the mess; consolidating onto one backbone is an ERP solutions conversation, and sometimes it comes first.
The data layer, answered.
How do you get ERP data into a warehouse?
Through pipelines that pull from your ERP’s API or, where the hosting allows it, from the database itself. The route depends on how your ERP is hosted, cloud or on-premise, and the audit settles it early. Raw data lands in a staging layer first, then gets transformed into modeled tables, so the warehouse never depends on fragile manual exports.
Can an ERP connect directly to Power BI?
Yes. Connector tools and Odoo’s API can feed Power BI directly, and for one user asking simple questions that is enough. At team scale the warehouse takes over, because it removes the production load and keeps the history that direct connections flatten.
Does an ERP have a built-in data warehouse?
No. Odoo’s own reporting reads the live transactional database, which is fine for operational lists and simple pivots. It is not designed for heavy analytical queries or for preserving history as records change, and it cannot join storefront and bank data to ERP records on its own. That is the job of a separate warehouse.
What is the best ETL tool or connector?
Several work well, and we are deliberately unreligious about the choice: the right one depends on your hosting, your sources and your budget. It also matters less than people expect. Data projects rarely fail on tool choice; they fail because nobody owns the model, so every dashboard invents its own definitions. Whichever tool runs the pipelines, someone has to decide what revenue means and write it down. We treat that as the actual product.
How often does the data refresh?
Nightly is the standard, hourly suits operational views like open orders, and near-real-time has to earn its extra cost. Every pipeline is monitored, so a failed refresh reaches you as an alert rather than a stale chart.
What does it cost to connect your ERP to Power BI?
One fixed fee for the build, scoped in writing from a free source audit, plus a small monthly care fee, never hourly. If a connector subscription is genuinely enough at your size, the audit will say so.
Do we need a data team to maintain this?
No. We run and monitor the pipelines as part of the engagement, and everything we build is documented and yours. If you later hire a data person, the handover is already written and the warehouse is already theirs to take over.
Which sources can you connect?
Odoo and QuickBooks, the major storefronts and marketplaces, banks and payment processors, 3PLs, and most tools with an API or a report export. If it holds data, we can usually move it.