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Create and Update Hyper Files

Hyper stores its tables in .hyper files. Using Hyper API, you can create .hyper files and then insert, delete, update, and read data from those files. You can then use .hyper files as data sources in Tableau, across the whole family of Tableau offerings (Tableau Desktop, Tableau Cloud, Tableau Prep, ...). Thereby, putting your data into a .hyper file is a good way to prepare your data for analyzing it with Tableau.

This guide will outline the basic steps for creating and updating data within a .hyper file.

Use Pandas to Load Your Data

While this guide shows you the most versatile way to load your data, using the integration with the pandas data framework might get you to your goal more quickly.

Creating a Hyper File

The following script creates a simple Hyper file with a single table:

from tableauhyperapi import HyperProcess, Connection, Telemetry, CreateMode, \
TableDefinition, TableName, SqlType, Inserter

with HyperProcess(Telemetry.SEND_USAGE_DATA_TO_TABLEAU,
parameters = {"default_database_version": "2"}) as hyper:
with Connection(hyper.endpoint, 'TrivialExample.hyper', CreateMode.CREATE_AND_REPLACE) as connection:
# Create an `Extract` table inside an `Extract` schema
example_table = TableDefinition(TableName('Extract','Extract'), [
TableDefinition.Column('rowID', SqlType.big_int()),
TableDefinition.Column('value', SqlType.big_int()),

# Insert data using the `Inserter` class
with Inserter(connection, example_table) as inserter:
for i in range (1, 101):
[ i, i ]

The script consist of 3 high-level steps:

  1. Start a Hyper process. The HyperProcess
  2. Create a connection to the .hyper file. Since we create the Connection class with the CreateMode.CREATE_AND_REPLACE, the .hyper file will be automatically created if it does not exist yet, and will be overwritten if a file with that name already exists.
  3. Defining the table. In this case, we are using the Python utilities TableDefinition and catalog.create_table. We could have also used a CREATE TABLE SQL command.
  4. Insert the data. In the example, we use the Inserter utility to provide the data from Python. You can also use INSERT or COPY statements or any other means to load data into the table. E.g., you can thereby directly load your table from a CSV file.

File Format Versions

By default, Hyper will use the initial default file format version 0. We recommend to use a more recent file format version, as shown in the example. In general, all reasonably up-to-date versions of Tableau should be able to read file format version 2, but if you are still using (very) outdated Tableau products, you might want to use file format 0 instead To learn more about the available versions and product support, see the Hyper Process Settings.

Update an existing extract file

The workflow for updating an existing extract is similar to the workflow for the basic creation of an extract.

  1. Start the HyperProcess.
  2. Connect to the database (.hyper file) using the Connection object.
  3. Append, insert, or update data in the table(s).

The main difference when connecting is that you use CreateMode.NONE instead of CreateMode.CREATE_AND_REPLACE. By using CreateMode.NONE, Hyper will connect to a pre-existing file instead of recreating a new, empty file. Since the default for CreateMode is NONE, you can also just leave this parameter out completely.

You can then use SQL commands (INSERT, UPDATE, DELETE, COPY, ...) or the Inserter utility class to change the data in the table. You could also create new tables or drop existing tables.

The following example removes rows with a value < 50 and appends two new row to an existing table within an extract file:

from tableauhyperapi import HyperProcess, Connection, Telemetry, CreateMode, Inserter

with HyperProcess(Telemetry.SEND_USAGE_DATA_TO_TABLEAU) as hyper:
with Connection(hyper.endpoint, 'TrivialExample.hyper', CreateMode.NONE) as connection:
# Delete every row where `value < 50`
DELETE FROM "Extract"."Extract" WHERE value < 50

# Insert two new rows
with Inserter(connection, TableName('Extract','Extract')) as inserter:
inserter.add_row([101, 101])
inserter.add_row([102, 102])