How to Connect Tableau to AWS Athena

Connecting Tableau to AWS Athena unleashes a powerful way to analyze and visualize massive datasets stored directly in Amazon S3. This article breaks down the process step-by-step, showing you how to set up the connection and sharing tips to optimize performance for lightning-fast dashboards.

First, What Are Tableau and AWS Athena?

Before diving into the setup, it helps to understand what each tool does and why they work so well together.

Tableau is a market-leading data visualization tool. It’s known for its user-friendly drag-and-drop interface that allows anyone, regardless of technical skill, to create interactive charts, graphs, and dashboards to explore data and uncover insights.

AWS Athena is an interactive, serverless query service from Amazon Web Services. It allows you to run complex SQL queries on data stored in Amazon S3 without needing to set up any servers or data warehouses. You just point Athena at your data in S3, define the schema, and start querying. Because it’s serverless, you only pay for the queries you run.

Why Connect Them?

The magic happens when you pair Tableau's intuitive visualization capabilities with Athena's raw query power. This combination allows you to:

• Analyze Huge Datasets: You can visualize terabytes or even petabytes of data sitting in your S3 data lake without having to move it into a separate, expensive data warehouse.
• Save Money: Athena's pay-per-query model is often more cost-effective than paying for a 24/7 dedicated data warehouse, especially for ad-hoc analysis.
• Improve Performance: By running queries directly against optimized data in S3, you get fast results in Tableau without having to wait for slow extract refreshes on massive datasets.
• Simplify Your Data Stack: You can skip complex and time-consuming ETL (Extract, Transform, Load) processes. Your data stays in S3, which acts as a single source of truth.

Prerequisites: What You’ll Need Before Connecting

To ensure a smooth connection process, make sure you have the following in place first. A little preparation here saves a lot of troubleshooting later.

• An Active AWS Account: You’ll need access to the AWS Management Console to configure S3 and Athena.
• Data in Amazon S3: Your source data should be stored in S3. Athena works best with columnar formats like Apache Parquet or ORC but also supports CSV, JSON, and Avro.
• An S3 Bucket for Query Results: Athena needs a dedicated S3 bucket to store the results of your queries. Create a new, empty bucket for this purpose (e.g., my-athena-query-results).
• IAM Permissions: You’ll need an IAM (Identity and Access Management) user with policies that grant access to Athena and your S3 buckets. For security reasons, avoid using your root AWS account credentials. At a minimum, your IAM user will need permissions like:
• Tableau Desktop Installed: You’ll need Tableau Desktop version 2020.3 or later to use the native Athena connector.
• Amazon Athena JDBC Driver: Tableau requires a specific driver to communicate with Athena. You'll need to download this from AWS.

Step-by-Step Guide to Connecting Tableau to AWS Athena

Once you've squared away the prerequisites, you’re ready to establish the connection. Follow these steps carefully.

Step 1: Download and Install the Athena JDBC Driver

Tableau doesn't come with the Athena driver pre-installed. You need to download and install it manually so the two applications can communicate.

1. Navigate to the AWS documentation page for Connecting to Amazon Athena with JDBC.
2. Download the latest version of the JDBC driver. Make sure you get the JDBC 4.2-compatible driver, typically a file named something like AthenaJDBC42_... .jar.
3. Move the downloaded .jar file into the correct Tableau driver directory for your operating system:
4. Restart Tableau Desktop for it to recognize the new driver.

Step 2: Set Up Your Database and Table in Athena

Before you can see your data in Tableau, you need to tell Athena how it's structured. You do this by creating a database and a table definition in the AWS Glue Data Catalog, which Athena uses to understand the schema of your files in S3.

1. Open the AWS Management Console and navigate to the Athena service.
2. In the Athena query editor, you’ll first create a database (if you don’t have one already):

CREATE DATABASE my_analytics_db,

1. Next, create an external table that points to your data in S3. Here is an example for a simple CSV file containing user data. Replace the column names, data types, and S3 location with your own.

CREATE EXTERNAL TABLE my_analytics_db.users (
  `id` int,
  `first_name` string,
  `last_name` string,
  `email` string,
  `signup_date` date
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
WITH SERDEPROPERTIES ('field.delim' = ',')
STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION 's3://your-data-bucket/users/',

If you're using a format like Parquet, your CREATE TABLE statement will be simpler, as Parquet includes the schema, and you won't need to define ROW FORMAT properties.

1. Run a quick query in the Athena editor to confirm your table is set up correctly, for example:

SELECT * FROM my_analytics_db.users LIMIT 10,

Step 3: Establish the Connection in Tableau

With the driver installed and your table defined in Athena, you are now ready to connect from Tableau.

1. Open Tableau Desktop.
2. In the "Connect" pane on the left, under "To a Server," click on Amazon Athena.
3. A connection dialog box will open. Fill in the following details:
4. Click Sign In.

Step 4: Select Your Data and Start Building

If the connection is successful, you will be taken to the Tableau Data Source page.

1. The Catalog dropdown should automatically be set to AwsDataCatalog.
2. In the database dropdown, select the database you created in Athena (e.g., my_analytics_db).
3. You'll now see your tables listed in the left pane. Drag the table you want to analyze (e.g., users) onto the canvas.
4. Click on the Sheet 1 tab at the bottom to switch to the worksheet and start dragging and dropping fields to build your visualizations.

Tips for Optimizing Performance

Connecting is just the first step. To get the best experience and keep your AWS costs low, follow these performance best practices when working with Tableau and Athena.

Use Columnar Data Formats

Convert your raw data (like CSV or JSON) into a columnar format like Apache Parquet or ORC. Athena is optimized for these formats and charges based on the amount of data scanned. Since columnar formats store data by column instead of by row, Athena only has to read the specific columns needed for your query, drastically reducing scan sizes, costs, and query time.

Partition Your Data in S3

Partitioning your data is the single most effective way to improve query performance. This involves organizing your data in S3 into subfolders based on a specific property, typically a date (e.g., year, month, day). A path structure like s3://my-bucket/data/year=2023/month=11/day=15/ allows Athena to skip scanning irrelevant folders altogether when you filter by date, leading to incredible speed improvements and cost savings.

Leverage Tableau Extracts Strategically

While Athena is fast, creating a Tableau Extract can still be beneficial in certain scenarios. An extract is a saved subset of your data. Consider using one if:

• The dashboard needs to be extremely fast and responsive for a large number of users.
• You want to reduce the analytical load and query costs on Athena.
• You need offline access to the data.

For most ad-hoc analyses, a live connection should be sufficient and gives you the benefit of real-time data.

Apply Filters Early

When building in Tableau, always apply relevant filters (especially on partitioned fields like date) to your worksheet before adding lots of measures and dimensions. Tableau translates these filters into WHERE clauses in the SQL query it sends to Athena. This tells Athena to use your partitions and scan a much smaller subset of the data right from the start.

Final Thoughts

Pairing Tableau with AWS Athena creates a scalable, cost-effective, and powerful analytics solution for businesses of all sizes. By connecting Tableau directly to your data lake in S3, you can build interactive dashboards on top of massive datasets without the overhead and complexity of traditional data warehousing.

For many teams, especially in marketing and sales, sorting out drivers, IAM credentials, and S3 staging directories can feel overwhelming when all you want are answers from your data. We built Graphed to remove this friction entirely. We offer one-click integrations to tools like Google Analytics, Shopify, and Salesforce and let you use simple, natural language to instantly build the real-time reports you need, getting you from data to decision in seconds, not hours.