Visualize Data with Tableau

Tableau is a commercial tool for exploring and visualizing tabular data.

In addition to making visualizations, Tableau makes it easy to select, filter, group, and describe your data in tables. This can be easier than the same operations in a Python REPL with pyspark or pandas.

This page describes various ways to connect Tableau to dsgrid data after you’ve installed Tableau Desktop on your local computer.

Install Tableau

For NLR employees: Licenses are available through theSOURCE. Go to IT Service Portal → Service Catalog → search for Tableau, and submit a ticket to get Tableau Creator installed (IT will install Tableau Desktop).

For others: Visit Tableau’s website to purchase or request a trial license.

Option 1: Parquet Files on Local Computer

Connect Tableau to DuckDB to read Parquet files locally.

Steps

1. Copy Parquet Files

Copy the Parquet files from your query output to your local computer:

# From HPC to local
scp -r username@kestrel.hpc.nrel.gov:/path/to/query_output ./local_data

2. Install DuckDB

Install DuckDB. You want the Command line Environment.

macOS:

brew install duckdb

Windows: Download from DuckDB releases

Linux:

wget https://github.com/duckdb/duckdb/releases/latest/download/duckdb_cli-linux-amd64.zip
unzip duckdb_cli-linux-amd64.zip

3. Install JDBC Driver

Install a JDBC driver and connect Tableau to DuckDB by following DuckDB’s instructions.

4. Create a View

Create a view of your data as noted in DuckDB’s database creation guide.

You can also import your data from Parquet files to a DuckDB database file:

-- Start DuckDB
duckdb my_data.db

-- Create a view from Parquet files
CREATE VIEW load_data AS
SELECT * FROM 'query_output/table.parquet';

-- Or create a table for faster access
CREATE TABLE load_data AS
SELECT * FROM 'query_output/table.parquet';

5. Connect Tableau

  1. Open Tableau Desktop

  2. Connect to More… → Other Databases (JDBC)

  3. Configure connection:

    • URL: jdbc:duckdb:/path/to/my_data.db

    • Dialect: SQL92

  4. Select your view or table

  5. Start visualizing!

Option 2: Parquet Files on HPC

Connect Tableau directly to a Spark cluster running on the HPC.

Requirements

  • Active Spark cluster on Kestrel

  • Network access from your computer to the HPC

  • Spark JDBC driver

Steps

Follow the Spark-on-HPC instructions for detailed setup.

Key steps:

  1. Start Spark cluster on Kestrel (see Start Spark Cluster)

  2. Configure Spark Thrift Server

  3. Install Spark JDBC driver in Tableau

  4. Connect Tableau to Spark cluster

  5. Query data directly from HPC storage

Tip

This option is best for very large datasets that would be cumbersome to copy locally.

Option 3: CSV Files on Local Computer

The simplest approach for small to medium datasets.

Steps

1. Export to CSV

Export your dsgrid query results from Parquet to CSV:

Using Python:

import pandas as pd

# Read Parquet file
df = pd.read_parquet('query_output/table.parquet')

# Write to CSV
df.to_csv('query_output/table.csv', index=False)

Using DuckDB:

duckdb -c "COPY (SELECT * FROM 'table.parquet') TO 'table.csv' (HEADER, DELIMITER ',')"

2. Load in Tableau

  1. Open Tableau Desktop

  2. Connect to Text file

  3. Select your CSV file

  4. Configure data types if needed

  5. Start visualizing!

Warning

CSV files can be very large and slower to load than Parquet. Consider using Option 1 (DuckDB) for better performance.

Comparison of Options

Option

Best For

Pros

Cons

DuckDB

Local analysis, medium-large datasets

Fast, efficient, no network needed

Requires copying data

HPC Spark

Very large datasets, team sharing

No data copy, direct HPC access

Complex setup, network required

CSV

Small datasets, simple workflows

Simple, no extra tools

Large files, slow loading

Tableau Tips for dsgrid Data

Time Series Visualization

  1. Convert timestamp columns to Date/DateTime types

  2. Use Marks → Line for time series plots

  3. Add Geography to columns, Timestamp to rows, Value to values

Geographic Maps

  1. Ensure geography IDs match Tableau’s geographic roles

  2. Assign geographic role: Right-click dimension → Geographic Role → County (or State)

  3. Use Map view type

  4. Add Value to color for choropleth maps

Aggregations

  1. Use Data → New Calculated Field for custom aggregations

  2. Filter by dimension: Drag dimension to Filters shelf

  3. Group records: Right-click dimension → Create → Group

Performance Tips

  • Extract data: Data → Extract Data (creates .hyper file for faster loading)

  • Aggregate in query: Pre-aggregate data in dsgrid query when possible

  • Filter early: Apply filters before loading into Tableau

  • Use DuckDB: Faster than CSV for large datasets

Next Steps