# How to connect Power BI to Fishbowl

> Source: https://netray.co/integrations/fishbowl-power-bi
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Integrations

## Short answer

The usual path is Power BI's MySQL connector against a read-only user on a Fishbowl replica or staging database, refreshed through an on-premises data gateway. For anything beyond simple reports, copy the data nightly into a small warehouse so your dashboards do not load the live system.

## System

Fishbowl

## Capability

power-bi

## Intro

Fishbowl ships with reports, but teams that want trend lines, cross-filtering and shared dashboards end up in Power BI. Because Fishbowl Advanced stores data in MySQL, connecting is more straightforward than with many ERPs. The hard parts are the data model and keeping the live server healthy.

Fishbowl tables are built for transactions, not analysis. Quantities live in several places, costs are in different tables depending on method, and unit of measure conversions are everywhere. A dashboard that sums the wrong column will be wrong with great confidence.

So treat the connection as a small data project: choose the access path, shape a clean model, build a handful of dashboards that match real decisions, and only then consider natural-language questions on top.

## Use Cases

### Inventory health

**Description:** On-hand value, days of supply, slow and dead stock, and stock by location group with cost tied up.

### Purchasing performance

**Description:** On-time receipt by vendor, price variance against last cost, and open PO value by age.

### Sales and backlog

**Description:** Open sales order backlog by customer and ship date, fulfillment rate and top products by margin.

### Manufacturing throughput

**Description:** Manufacture order cycle time, scrap, and component shortages that delayed orders.

### Inventory accuracy

**Description:** Adjustment trends by location and user to target cycle counts.

### Cash view

**Description:** Inventory value versus open receivables and payables when combined with your accounting data.

## System Surface

- MySQL database: connect Power BI's MySQL connector with a read-only user. Fishbowl's database is not designed for BI, so confirm table names and join logic for your version.
- On-premises data gateway: required for scheduled refresh when MySQL is not reachable from the Power BI service.
- Fishbowl REST API: usable through Power Query web connectors for smaller data sets, though paging and rate limits make bulk loads awkward.
- Report and CSV export: a scheduled export of curated reports to a folder is a low-risk feed for simple dashboards.
- QuickBooks or other accounting data: a common second source in the same model for margin and cash views.

## How It Works

### Choose the access path

**Detail:** For small data, a read-only MySQL user on a nightly copy. For anything heavy, replicate to a staging database or warehouse.

### Install the gateway

**Detail:** Install the on-premises data gateway on a machine that can reach MySQL. Register it to your tenant and set a refresh schedule outside working hours.

### Shape the model

**Detail:** Create views or staging tables for parts, locations, inventory, POs, SOs and manufacture orders with consistent units and cost.

### Build a star schema

**Detail:** Fact tables for inventory movements, order lines and receipts; dimensions for part, vendor, customer, location and date.

### Write measures carefully

**Detail:** Define available, on-hand value and days of supply once in DAX and reuse them everywhere.

### Publish with row-level security

**Detail:** Restrict customer and cost data by role. Keep the gateway account read-only.

### Add natural-language Q&A

**Detail:** Power BI's built-in Q&A works on clean, well-named models; for deeper questions, a separate assistant over the same warehouse can answer and cite measures.

## Example Flows

### Nightly refresh pipeline

**Trigger:** Scheduled at 1:00 each night.

**Steps:**

- A job copies key Fishbowl tables to a staging database.
- Transform scripts build clean fact and dimension tables.
- The gateway refreshes the Power BI dataset.
- Dashboards are ready before the morning meeting.

**Result:** Managers see yesterday's stock and orders without touching the live server.

### Natural-language question

**Trigger:** A manager asks which vendors are slipping.

**Steps:**

- The question goes to an assistant connected to the warehouse.
- It queries on-time receipt measures.
- It returns a ranked list with a link to the dashboard page.

**Result:** A quick answer that points back to the audited measure.

## On Prem

A typical on-prem pattern keeps everything inside: Fishbowl and MySQL on one server, a replica or staging database on a second, the on-premises data gateway on a third small machine, and Power BI Service only receiving the published dataset. If dashboards must not leave your network, Power BI Report Server is an on-prem alternative, though its feature set and licensing differ from the cloud service, so confirm current terms. A local open-weight model on the warehouse can provide natural-language questions without sending data out.

## Security

- Use a read-only MySQL account and restrict it to the tables or views the model needs.
- Never point Power BI at the live production database for heavy queries; use a replica or staging copy.
- Apply row-level security for customers, regions and cost visibility.
- Secure the gateway machine and keep its service account separate from admin accounts.
- Document which fields are exposed to the Power BI service and where they are stored.

## Options

### Direct MySQL connection with a gateway

**Fit:** Small shops that need a few dashboards quickly.

**Effort:** Days.

### Staging warehouse and curated model

**Fit:** Teams that want trusted measures and no load on the live server.

**Effort:** Two to six weeks depending on scope.

### Partner build with Netray

**Fit:** You want the warehouse plus a private natural-language layer and data kept on-prem.

**Effort:** Scoped project, dashboards first.

## Pitfalls

- Querying the live database with heavy DirectQuery loads that slow receiving and picking.
- Summing quantities without handling unit of measure conversions.
- Mixing average and standard cost without saying which one a dashboard uses.
- Refresh schedules that collide with backups or peak hours.
- Building a model off table names from one version and breaking after an upgrade.

## Faq

### Does Power BI have a Fishbowl connector?

**Answer:** I am not aware of an official one, so check the current connector list. Most teams use the MySQL connector against Fishbowl's database, or load exported reports. Some third-party tools offer prebuilt Fishbowl analytics, so compare them to building your own.

### Do I need an on-premises data gateway?

**Answer:** Usually yes. If MySQL is not reachable from the Power BI service, scheduled refresh needs the gateway on a machine inside your network. Without it you are limited to manual refreshes from Power BI Desktop.

### Is it safe to query the Fishbowl database directly?

**Answer:** Read-only access is generally safe, but heavy queries can slow the live server. Use a replica or nightly copy, restrict the user to needed tables, and avoid DirectQuery against production during working hours.

### Can I ask questions in plain English?

**Answer:** Power BI Q&A works if your model has clear names and synonyms. For deeper questions, a separate assistant connected to the same warehouse can answer and cite measures. It should read the model, not the raw transactional tables.

### What dashboards should I build first?

**Answer:** Inventory health and purchasing performance, because they map to decisions made weekly and use data you trust. Add sales backlog and manufacturing next. Avoid a giant first dashboard; each page should answer one question.

## Search terms

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