13 Connecting to Data and Measure Names

What This Chapter Covers

Every Tableau workbook begins with a data connection. This chapter covers the complete data connection workflow, connecting to files, databases, and published data sources; configuring live vs. extract connections; understanding how Tableau handles wide (column-heavy) datasets using Measure Names and Measure Values; and managing data source metadata. You will also learn how to use Measure Names and Measure Values, two special auto-generated fields that unlock a class of multi-measure charts that would otherwise require complex data reshaping.

flowchart TD
    A[Connect to Data] --> B[File Sources\nExcel, CSV, JSON, PDF]
    A --> C[Database Sources\nSQL, BigQuery, Snowflake]
    A --> D[Published Sources\nTableau Server or Cloud]
    B --> E[Data Source Page\nConfigure Joins and Unions]
    C --> E
    D --> F[Direct to Worksheet\nno Data Source Page]
    E --> G{Connection Type?}
    G --> H[Live\nReal-time queries]
    G --> I[Extract .hyper\nFast in-memory]
    H --> J[Worksheet]
    I --> J
    style A fill:#e3f2fd,stroke:#1976D2
    style G fill:#fff9c4,stroke:#F9A825
    style J fill:#e8f5e9,stroke:#388E3C

13.1 Data Connection Types

File Connections

Tableau can connect directly to a wide range of file formats without any additional drivers or setup:

File Type	Extension	Notes
Microsoft Excel	.xlsx, .xls	Connects to any sheet or named range
Text / CSV	.csv, .txt, .tsv	Configurable delimiters and character encoding
JSON	.json	Tableau parses nested JSON automatically
PDF	.pdf	Tableau extracts tabular data from PDF tables
Spatial files	.shp, .kml, .geojson, .topojson	For geographic analysis
Statistical files	.sas7bdat, .rda, .sav	SAS, R, and SPSS datasets
Google Sheets	URL	Direct connection via Tableau’s Google Sheets connector

Connecting to Excel: 1. On the Start Page, click Microsoft Excel under Connect > To a File. 2. Navigate to the file and click Open. 3. On the Data Source Page, drag the desired sheet(s) from the left panel to the canvas. 4. Configure joins if multiple sheets are needed.

Database Connections

Tableau connects to over 80 database types and cloud platforms via native connectors. Common connectors include:

Category	Examples
Cloud data warehouses	Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse
Enterprise databases	Microsoft SQL Server, Oracle, IBM Db2, SAP HANA
Open-source databases	MySQL, PostgreSQL, SQLite
Cloud platforms	Salesforce, Google Analytics, Marketo
Web data connectors	Any REST API with a Web Data Connector (WDC)

Connecting to a database: 1. On the Start Page, click the relevant connector under Connect > To a Server. 2. Enter the server address, port, database name, and authentication credentials. 3. Click Sign In. The Data Source Page opens with the available schemas and tables. 4. Drag tables to the canvas to create the data model.

[Insert screenshot of the SQL Server connection dialog showing server name, database, and authentication fields]

Published Data Sources

A published data source is a reusable, governed data connection that has been published to Tableau Server or Tableau Cloud by a data steward. Connecting to a published data source (rather than raw data) ensures all analysts are working from the same certified, clean dataset with consistent field names and calculated fields.

Connecting to a published data source: 1. On the Start Page, click Tableau Server or Tableau Cloud under Connect > To a Server. 2. Sign in with your credentials. 3. Browse or search for the published data source by name. 4. Click Connect. The workbook opens directly to a new worksheet (no Data Source Page, the data model is already configured in the published source).

13.2 Live vs. Extract Connections

Live Connections: Always Current, Sometimes Slow

A live connection queries the database directly every time the view is updated, when you drag a field, apply a filter, or change a parameter. The data in the view is always current as of the latest database state.

When to use live connections: - The data changes frequently and the dashboard must reflect near-real-time data. - The database is fast enough to respond within 2–3 seconds (acceptable for interactive use). - Data volume is manageable within the database’s query performance limits.

Disadvantage: Every interaction triggers a database query. On slow connections or large databases, this creates unacceptable latency.

Extracts: Fast, Portable, and Scheduled

An extract imports a snapshot of the data into Tableau’s in-memory columnar engine (the Hyper format, .hyper file). All queries run against the local Hyper file rather than the remote database, delivering dramatically faster performance.

When to use extracts: - The database is slow or has limited connectivity. - You need to work offline (e.g., on a plane or at a client site without VPN). - You are aggregating or pre-filtering a very large dataset. - You want to share the workbook as a self-contained .twbx file.

Creating an extract: 1. On the Data Source Page, click the Extract radio button (top right). 2. Optionally click Edit to configure extract filters (limit to specific date range), aggregation (pre-aggregate to reduce file size), or row count sampling. 3. Click the first worksheet tab. Tableau prompts you to save the extract as a .hyper file. 4. On Tableau Server, schedule extract refreshes via the Server’s schedule management interface.

[Insert screenshot of the Data Source Page showing the Live/Extract radio buttons, with Extract selected and the Edit button visible]

Extract Incremental Refresh: Keeping Extracts Current

Rather than refreshing the entire extract every day (a full refresh), configure an incremental refresh that only appends new rows since the last refresh. This is dramatically faster for large datasets.

To configure: in the Extract edit dialog, tick Incremental Refresh and select the date or integer field that identifies new records (e.g., Order Date or Record ID). Tableau compares the field value in the existing extract against the source and appends only new rows.

13.3 The Data Source Page: Detailed Navigation

Configuring Your Data Model

The Data Source Page is where you define the structure of the data that feeds your worksheets. Key operations:

Renaming fields: Double-click any column header in the data grid to rename it. Rename fields to use business-friendly names (e.g., rename cust_id to Customer ID, rev_usd to Revenue (USD)). These renamed fields appear in the Data pane in every worksheet.

Hiding fields: Click the small arrow on a column header and select Hide. Hidden fields do not appear in the Data pane, reducing clutter. They are still available for joins but not visible to worksheet builders.

Changing data types: Click the data type icon (Abc for string, # for number, calendar for date) above any column to change its type. Common use: a numeric field like Zip Code must be changed from Number to String to prevent Tableau from summing postal codes.

Splitting fields: For fields that contain multiple values separated by a delimiter (e.g., “New York, USA”), right-click the column header and select Split to automatically separate into individual columns.

Pivoting columns to rows: When data is in “wide” format (one column per measure, e.g., Jan, Feb, Mar as separate columns), select those columns, right-click, and select Pivot to convert them to a single “Month” column and a “Value” column, the tidy format Tableau works best with.

How To: Pivoting Wide Data to Long Format

Wide format (what many Excel spreadsheets use):

Region	Jan Sales	Feb Sales	Mar Sales
East	12000	14000	11000

Long format (what Tableau works best with):

Region	Month	Sales
East	Jan	12000
East	Feb	14000
East	Mar	11000

Steps in Tableau: 1. On the Data Source Page, hold Ctrl and select all the month columns (Jan Sales, Feb Sales, Mar Sales). 2. Right-click the selected headers and choose Pivot. 3. Tableau creates two new columns: Pivot Field Names (containing “Jan Sales”, “Feb Sales”, etc.) and Pivot Field Values (containing the sales numbers). 4. Rename Pivot Field Names to “Month” and Pivot Field Values to “Sales”.

[Insert screenshot of the Data Source Page before and after pivoting, showing the column selection and the resulting long-format data grid]

13.4 Measure Names and Measure Values

What Are Measure Names and Measure Values?

Measure Names and Measure Values are two special auto-generated fields that Tableau creates automatically for every data source. They appear at the bottom of the Dimensions and Measures sections in the Data pane.

Measure Names, A dimension containing the names of all measures in your data source as string values (e.g., “Sales”, “Profit”, “Quantity”, “Discount”).
Measure Values, A measure containing the aggregated values of all measures simultaneously, determined by whatever subset of measure names is filtered or in context.

Together, these two fields allow you to plot multiple measures on the same axis simultaneously, something that is otherwise impossible in Tableau’s standard interface, where each measure gets its own axis.

When Tableau Automatically Uses Measure Names and Measure Values

Tableau automatically places Measure Names on Rows/Columns and Measure Values on the opposite shelf whenever you drag two or more measures to the same axis. You will recognise this pattern: - Both pills appear on the shelves. - A Measure Values card appears in the Marks card area, showing all selected measures with individual mark property controls. - A Measure Names filter appears in the Filters shelf, allowing you to control which measures are displayed.

[Insert screenshot of the Tableau worksheet interface showing Measure Names on Rows, Measure Values on Columns, and the Measure Values marks card with multiple measure entries]

How To: Building a Multi-Measure Comparison Chart Using Measure Names

Goal: Display Sales, Profit, and Quantity side by side for each Sub-Category in a single bar chart.

Drag Sub-Category to the Rows shelf.
Hold Ctrl and drag Sales, Profit, and Quantity to the Columns shelf simultaneously, Tableau automatically creates a Measure Names/Measure Values view with three panels.
Alternatively: drag Measure Values to Columns and Measure Names to the Colour shelf, this creates a grouped bar chart with one colour per measure.
In the Filters shelf, a Measure Names filter appears. Open it and ensure only the desired measures are checked.
Format each measure independently using the Measure Values card in the Marks area.

[Insert screenshot of a grouped bar chart with Sub-Category on Rows and three measures (Sales, Profit, Quantity) as coloured bar groups, with Measure Names on Colour]

Using Measure Names in Small Multiples

The most powerful use of Measure Names is creating a small multiples layout where each panel shows a different measure, allowing direct visual comparison of multiple KPIs with identical axis structures.

Drag Order Date (Month, continuous) to the Columns shelf.
Drag Measure Values to the Rows shelf.
Drag Measure Names to the Rows shelf alongside Measure Values.
Open the Measure Names filter and select only the measures you want (e.g., Sales, Profit, Quantity).
Tableau creates three rows of line charts, one per measure, all sharing the same time axis.
Right-click each Y-axis and select Edit Axis > Independent axis ranges to allow each panel to scale to its own data range.

[Insert screenshot of a three-panel small multiples line chart: one row each for Sales, Profit, and Quantity, all sharing the same X-axis time series]

Measure Names and Formatting Inconsistency

One limitation of the Measure Names/Measure Values approach is that all measures must share the same mark type, colour scheme, and axis format, you cannot independently colour the Sales bar blue and the Profit bar green within the same Measure Values axis. For fully independent formatting per measure, use a dual-axis chart (for two measures) or a small multiples layout with individual worksheets per measure assembled on a dashboard (for three or more measures).

13.5 Data Source Metadata Management

Renaming, Hiding, and Organising Fields

Well-organised Data pane fields make workbooks faster to build and easier for colleagues to use. Best practices:

Rename fields: Double-click any field in the Data pane to rename it. Use business-friendly, unambiguous names. Rename immediately after connecting, before any worksheets reference the original names.

Create folders: Right-click in the Data pane and select Create Folder. Drag related fields into the folder (e.g., “Date Fields”, “Financial Metrics”, “Customer Dimensions”). This is especially valuable when connecting to a database table with dozens of columns.

Hide irrelevant fields: Right-click any field and select Hide. Hidden fields are excluded from the Data pane view but remain accessible via Show Hidden Fields if needed.

Add field descriptions: Right-click a field and select Default Properties > Comment to add a description. This description appears in the tooltip when teammates hover over the field in the Data pane, invaluable for shared published data sources.

Set default aggregation: Right-click a measure and select Default Properties > Aggregation to change from SUM (the default) to AVG, MAX, or another aggregation. The field will use this aggregation by default whenever dragged to a shelf.

13.6 Summary

Key Concepts at a Glance

Concept	Description	Key Access
File connection	Connect to Excel, CSV, JSON, spatial files	Start Page > To a File
Database connection	Connect to SQL, BigQuery, Snowflake, etc.	Start Page > To a Server
Published data source	Reusable governed connection on Server/Cloud	Start Page > Tableau Server
Live connection	Real-time database queries	Data Source Page > Live radio button
Extract (.hyper)	Fast in-memory snapshot	Data Source Page > Extract radio button
Incremental refresh	Append only new rows to extract	Extract Edit dialog > Incremental Refresh
Pivot	Convert wide format to long format	Select columns > Right-click > Pivot
Measure Names	Auto-generated dimension of measure names	Data pane (bottom of Dimensions)
Measure Values	Auto-generated measure containing all measure values	Data pane (bottom of Measures)
Field renaming	Business-friendly names in Data pane	Double-click field in Data pane

Applying This in Practice

The single most impactful data connection habit is to rename and organise all fields immediately after connecting to a new data source, before building any worksheets. The five minutes spent creating folders, renaming cryptic database column names, and hiding irrelevant fields will save hours of confusion for you and every colleague who uses the workbook after you. Treat the Data Source Page as the foundation of your workbook: a clean, well-organised foundation produces better work faster.