📊 Tableau Learning Roadmap — Part 2

Connecting to Data

Before creating visualizations in Tableau, you need to connect Tableau to a data source. Tableau can work with data stored in files, databases, cloud platforms, and other supported sources.

1. Excel

Tableau can connect directly to Excel files such as:

Sales_Data.xlsx

For example:

Order Date | Product | Region | Sales

Jan 2026 | Laptop | East | 50000

Feb 2026 | Monitor | West | 30000

You can select the required worksheet and begin analyzing the data.

2. CSV and Text Files

Tableau can also connect to:

• CSV files

• Text files

• Delimited files

These are commonly used when data is exported from another application.

3. Databases

Tableau can connect to many database systems, including:

• SQL Server

• MySQL

• PostgreSQL

• Oracle

• Snowflake

• Databricks

Instead of manually exporting database data into Excel, Tableau can connect to the database directly.

4. Cloud Data Sources

Modern organizations often store their data in cloud platforms. Tableau supports connections to various cloud data platforms and services. This allows organizations to analyze centrally stored data without repeatedly downloading files.

5. Web Data

Depending on the connector and setup, Tableau can also work with web-based data sources and supported online services.

The important idea is:

Tableau → Data Source → Analysis → Visualization

Live Connection vs Extract

This is one of the most important concepts in Tableau.

🔵 Live Connection

With a Live connection, Tableau queries the underlying data source when it needs data.

Example: Tableau → SQL Server

When you interact with a visualization, Tableau can send queries to SQL Server and retrieve the required results.

🟢 Extract

An Extract is a snapshot of data stored in Tableau's optimized extract format.

Example: Database → Tableau Extract → Tableau

Instead of querying the original database for every interaction, Tableau can use the extracted data.

Live vs Extract

Live

• Queries the original source

• Data can reflect changes in the source

• Performance depends partly on the underlying source and connection

Extract

• Stores a copy of the data

• Can provide faster analysis in many scenarios

• Requires refreshes when the source data changes

The choice depends on factors such as:

• Data size

• Data freshness requirements

• Database performance

• Network conditions

• Refresh requirements

Data Source Filters

A data source filter restricts the data available from a particular data source.

For example, suppose your dataset contains sales from: India + USA + UK + Germany

You could apply a data source filter to keep only: India + USA

This can reduce the amount of data available for analysis.

Data Source Properties

When connecting to data, Tableau provides settings that affect how the data is interpreted and used.

Depending on the source, you may work with things such as:

• Field names

• Data types

• Connection information

• Extract settings

• Filters

• Metadata

Correctly configuring your data source is important because problems at this stage can affect everything you build later.

🔑 Simple Example

Imagine you receive a company's Sales.xlsx file. Your workflow could be:

Sales.xlsx → Connect Tableau → Select Sales sheet → Check field names and data types → Apply required data source filters → Choose Live or Extract → Start building visualizations

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