Starter Guide
Furnilytics is a structured data platform for the furniture industry, designed to make market data easier to explore, access, and use. The platform combines public market indicators, structured datasets, and API-based delivery in one connected system.
This guide is intended as a practical starting point for new users. It explains how to explore public indicators, browse available datasets, and use Furnilytics data in dashboards or analytical workflows.
Whether you work in market intelligence, strategy, sales, sourcing, or analytics, the goal is the same: to make furniture market data easier to access, easier to interpret, and easier to use in practice.
Step 1 — Explore Public Market Indicators
The easiest way to start using Furnilytics is by exploring the public market indicators available on the website. These indicators present key furniture industry datasets in a structured and visual form, making it easy to quickly understand what is available and how the platform is organised.
The indicator library is organised in several navigation layers to make exploration easier. Users can start from the overall indicator overview, move into topic pages such as Retail, Manufacturing or Macroeconomics, then browse more specific subtopics before opening individual indicator pages with charts and downloadable data.
For users who prefer a more direct way to search across the library, Furnilytics also provides a catalogue view where all indicators can be browsed and filtered in one place.
Each indicator page includes a chart, a short explanation of the dataset, and the option to download the underlying data. This makes the indicator pages useful both for quick exploration and for users who want to continue working with the data in Excel or in their own reporting environment.
Indicator pages also include a link to the underlying dataset, identified by a unique dataset ID. This link opens the dataset page in the data catalogue, where metadata, source information, and the full table structure are documented. These dataset IDs are used consistently across the platform and serve as the reference when accessing Furnilytics data in dashboards or through the API.
For many users, the most practical workflow is to first explore the indicator library, open a relevant indicator to review the chart and context, and then either download the data to Excel or follow the dataset link to understand the underlying table.
Step 2 — Browse Available Datasets
After exploring the public indicator pages, the next step is to browse the underlying datasets in the Furnilytics data catalogue. The catalogue provides a structured overview of available tables and makes it easier to understand how the platform is organised at dataset level.
While indicator pages are designed for quick visual exploration, the data catalogue is designed for navigation, documentation, and data discovery. It allows users to search across tables, filter the library, and inspect the metadata and schema of each dataset before using it in Excel, dashboards, or API-based workflows.
Users can browse the catalogue in several ways. The search bar helps locate specific tables directly, while filters such as public availability and geography make it easier to narrow the list to the most relevant datasets. The catalogue follows the same topic and subtopic hierarchy as the indicator library, reflecting the common data structure used across the Furnilytics platform.
This makes the catalogue useful both for users who already know what they are looking for and for users who want to explore what data is available within a specific market area, topic, or geography.
Once a table is opened, the dataset page provides the key information needed to understand and use the dataset. This includes the access tier, the latest refresh date, the unique table ID, a short description, the underlying source, and the table schema with field names, data types, and definitions. The table ID is particularly important, as it serves as the unique identifier used when retrieving data programmatically through the Furnilytics API.
In practice, these pages function as the documentation layer of the platform. They help users verify what a dataset contains, how it is structured, and whether it is suitable for their analytical use case before connecting it to a dashboard, retrieving it via the API, or requesting access to additional datasets.
Public vs Pro datasets
The Furnilytics platform provides access to datasets at different access tiers. Some datasets are freely available and marked with the Public tag, while others are part of the Pro data library.
Public datasets form the foundation of the indicator library on the website. However, the indicator pages typically display only a selected part of the underlying datasets. In many cases, the public tables available in the data catalogue contain additional variables, longer time series, or more detailed breakdowns than what is shown in the indicator charts.
Pro datasets are not displayed in the public indicator library. These datasets are designed primarily for direct analytical use through the data catalogue or the Furnilytics API. In some cases, selected insights derived from Pro datasets may appear in the analytical articles published on Furnilytics.
For users exploring the platform, the public datasets provide a practical starting point for analysis and can be accessed directly through the catalogue, downloaded as tables, or integrated into dashboards and workflows via the API.
Getting Pro access
In addition to the public datasets available on the platform, Furnilytics provides access to a range of specialised datasets through the Pro data library. These datasets include extended historical series, higher-frequency indicators, and advanced data such as weekly web-scraped market indicators that are designed for deeper market analysis and integration into analytical workflows.
If you would like to access Pro datasets, you can request access by contacting the Furnilytics team at support@furnilytics.com. Once access is approved, you will receive an API key that allows you to retrieve datasets programmatically.
Pro datasets are currently accessed through the Furnilytics API. Data can be retrieved using Python or standard HTTP requests, making it easy to integrate Furnilytics data directly into data platforms, dashboards, or business intelligence tools such as Power BI or Tableau.
Step 3 — Using Furnilytics Data in Dashboards
Furnilytics datasets can be integrated directly into dashboards and reporting environments, allowing external market data to be combined with internal company data such as sales, pricing, or category performance. This enables more structured benchmarking, market monitoring, and data-driven decision-making.
Depending on your setup, Furnilytics data can be accessed through business intelligence tools, data platforms, or programmatically via the API. The most common approach is to connect datasets directly to dashboard tools such as Power BI.
Option A — Power BI
A common way to use Furnilytics data is to integrate it directly into Power BI dashboards. This allows external market data to be combined with internal company data in a single reporting environment.
Datasets are connected using the Furnilytics API via the Get Data → Web connector in Power BI. The API returns structured JSON data, which Power BI converts into a table that can be used in reports.
Step 1 — Build the API request
The request is built from several components:
- Base URL: The Furnilytics API domain
- Endpoint: Defines API endpoint.
/data/is used to retrieve data. - Table ID: The unique dataset identifier from the catalogue
- Filters (optional): Parameters such as geography or time
If you have Pro access, include your API key in the request header. Public datasets can be accessed without authentication.
Step 2 — Set connection type
When prompted, select Anonymous authentication. Authentication is handled through the API key (if required), not through Power BI credentials.
Step 3 — Refresh and publish
After loading the dataset, you can build visuals, refresh the data, and publish the report to the Power BI Service. Publishing allows reports to be shared internally and kept up to date.
Step 4 — Configure refresh in Power BI Service
In your Power BI workspace, locate the dataset (semantic model), click the three dots, and open Settings.
Step 5 — Schedule automatic refresh
Enable Scheduled refresh and configure a refresh frequency. A weekly refresh is recommended for most Furnilytics datasets.
Option B — Other BI Tools and Data Platforms
Furnilytics datasets can also be integrated into a wide range of business intelligence tools and data platforms that support web-based data connections.
Because datasets are accessible through the API, data can be retrieved programmatically and integrated into dashboards, data models, or automated reporting workflows across different environments.
Common use cases
- Tableau: Connect using web-based data queries or API-driven workflows
- Excel: Load data using Power Query (Get Data → From Web)
- Python / Databricks: Retrieve datasets using the Furnilytics Python library or standard API requests
- Data warehouses and pipelines: Ingest data into internal platforms and scheduled data workflows
How it works
In all cases, the process follows the same principle: identify the relevant dataset using its table ID, retrieve the data through the Furnilytics API, and transform it into a tabular format within your tool of choice.
Step 4 (optional) — Access Data Programmatically
For users who want to integrate Furnilytics data into analytical workflows, dashboards, or automated pipelines, datasets can also be accessed programmatically. The two main options are the Furnilytics Python library and the underlying HTTP API.
In both cases, the workflow is the same: identify the dataset using its table ID, retrieve metadata if needed, and then request the data rows directly. Public datasets can be accessed without authentication, while Pro datasets require an API key.
Option A — Python (recommended)
The easiest way to work with Furnilytics data programmatically is through the Python library. It wraps the API and returns datasets directly as Pandas DataFrames, which makes it convenient for analysis, notebooks, ETL jobs, and dashboard backends.
The client supports the main API endpoints for dataset discovery, metadata lookup, and table retrieval. It also handles authentication and common API errors in a more structured way than raw HTTP requests.
Install the package
pip install furnilyticsBasic example
from furnilytics import Client
cli = Client()
# Fetch rows from a dataset
df = cli.data(
"other/materials/eu_pb_price",
limit=10,
geo=["PL", "DE"]
)
print(df.head())
You can also use the client to browse available datasets via /datasets, inspect table metadata via
/metadata, and request metadata for one specific table before retrieving the data itself.
If you have Pro access, you can pass your API key through the client or set it as an environment variable. Public datasets can be used without a key.
See full API and Python documentation →
Option B — HTTP API
Furnilytics data can also be retrieved directly through standard HTTP requests. This is useful for Power BI, Excel Power Query, internal data pipelines, or environments where you prefer to connect to the API without using the Python client.
The main endpoints are:
/datasets— browse the dataset catalogue/metadata— inspect metadata for all datasets/metadata/{table_id}— inspect metadata for one dataset/data/{table_id}— retrieve dataset rows
Basic data request
GET /data/other/materials/eu_pb_price?geo=PL&frm=2023-01-01&limit=100
The /data/ endpoint supports a few simple query parameters:
frm— start dateto— end datelimit— maximum number of rows<column>=value— filter by dataset column, for examplegeo=PL
Text filters are case-insensitive, and multiple values can be passed as comma-separated selections,
for example geo=PL,DE.
Public datasets can be requested directly. For Pro datasets, include your API key in the
X-API-Key request header.
Example with API key
curl -H "X-API-Key: your_api_key" \
"https://api.furnilytics.com/data/other/materials/eu_pb_price?geo=PL,DE&limit=100"This direct HTTP approach is the same foundation used by dashboard tools and other integrations. The Python client is essentially a convenience layer on top of these same endpoints.