Python Microsoft Fabric API - Tables

write_table, load_table, list_tables and wait_for_operation, with every parameter.

A connection has four methods for lakehouse tables. Every one takes the workspace ID and the lakehouse ID first.

write_table

conn.write_table(workspace_id, lakehouse_id, table_name, rows, mode='Overwrite')

Writes a list of dicts to a table and returns once the data is in it. The rows are written as CSV files to Files/zato/<table_name>/<timestamp>/ in the lakehouse, 100,000 rows per file, then loaded into the table in one operation that the call waits for.

Parameter	Type	Meaning
`workspace_id`	str	The workspace the lakehouse is in
`lakehouse_id`	str	The lakehouse the table is in
`table_name`	str	The table to write to, created if it does not exist
`rows`	list of dict	The rows, all with the keys of the first one, which become the columns
`mode`	str	`Overwrite` replaces the table with the rows, `Append` adds them to what is there

Returns the completed operation as a dict, with status set to Succeeded. A load that fails raises an exception with the operation's details.

# -*- coding: utf-8 -*-

# Zato
from zato.server.service import Service

class WriteTable(Service):

    def handle(self):

        workspace_id = '11111111-2222-3333-4444-555555555555'
        lakehouse_id = '66666666-7777-8888-9999-000000000000'

        conn = self.microsoft.fabric['My Fabric']

        rows = [
            {'insurer_id': 'INS-01', 'name': 'Cascade Health Plan'},
            {'insurer_id': 'INS-02', 'name': 'Pacific Mutual'},
            {'insurer_id': 'INS-03', 'name': 'Evergreen Assurance'},
        ]

        operation = conn.write_table(workspace_id, lakehouse_id, 'insurers', rows)

        self.response.payload = {'status': operation['status']}

load_table

conn.load_table(workspace_id, lakehouse_id, table_name, relative_path, mode='Overwrite',
    file_format='Csv', header=True, delimiter=',', path_type='File', recursive=False)

Starts loading a file or a folder that is already in the lakehouse's Files section into a table and returns right away, without waiting.

Parameter	Type	Meaning
`workspace_id`	str	The workspace the lakehouse is in
`lakehouse_id`	str	The lakehouse the table and the file are in
`table_name`	str	The table to load into, created if it does not exist
`relative_path`	str	The file or folder, relative to the lakehouse, e.g. `Files/incoming/admissions.csv`
`mode`	str	`Overwrite` or `Append`, as in `write_table`
`file_format`	str	`Csv` or `Parquet`
`header`	bool	CSV only - whether the first line holds the column names
`delimiter`	str	CSV only - the character between values
`path_type`	str	`File` when `relative_path` is one file, `Folder` when it is a folder of files
`recursive`	bool	Folders only - whether to include files in subfolders

Returns the address of the operation's status endpoint as a string, for wait_for_operation.

# -*- coding: utf-8 -*-

# Zato
from zato.server.service import Service

class LoadTable(Service):

    def handle(self):

        workspace_id = '11111111-2222-3333-4444-555555555555'
        lakehouse_id = '66666666-7777-8888-9999-000000000000'

        conn = self.microsoft.fabric['My Fabric']

        location = conn.load_table(
            workspace_id,
            lakehouse_id,
            'admissions',
            'Files/incoming/admissions',
            mode='Append',
            file_format='Parquet',
            path_type='Folder',
            recursive=True,
        )

        self.response.payload = {'location': location}

wait_for_operation

conn.wait_for_operation(location, timeout=600, interval=2.0)

Waits until a long-running operation ends and returns its final state. load_table is the method that returns such a location, and write_table calls this one on your behalf.

Parameter	Type	Meaning
`location`	str	The address `load_table` returned
`timeout`	int	Seconds to wait before giving up
`interval`	float	Seconds between checks

Returns the operation as a dict with status set to Succeeded. An operation that fails raises an exception with its details, one that does not end within timeout raises an exception naming the location.

# -*- coding: utf-8 -*-

# Zato
from zato.server.service import Service

class LoadAndWait(Service):

    def handle(self):

        workspace_id = '11111111-2222-3333-4444-555555555555'
        lakehouse_id = '66666666-7777-8888-9999-000000000000'

        conn = self.microsoft.fabric['My Fabric']

        location = conn.load_table(
            workspace_id,
            lakehouse_id,
            'invoices',
            'Files/incoming/payments-2026-03.csv',
            mode='Append',
        )

        operation = conn.wait_for_operation(location, timeout=300)

        self.response.payload = {'status': operation['status']}

list_tables

conn.list_tables(workspace_id, lakehouse_id)

Returns every table of a lakehouse as a list of dicts, following Fabric's paging until there are no more.

Parameter	Type	Meaning
`workspace_id`	str	The workspace the lakehouse is in
`lakehouse_id`	str	The lakehouse to list

Each dict has name, type, location and format.

# -*- coding: utf-8 -*-

# Zato
from zato.server.service import Service

class ListTables(Service):

    def handle(self):

        workspace_id = '11111111-2222-3333-4444-555555555555'
        lakehouse_id = '66666666-7777-8888-9999-000000000000'

        conn = self.microsoft.fabric['My Fabric']

        tables = conn.list_tables(workspace_id, lakehouse_id)

        names = []
        for table in tables:
            names.append(table['name'])

        self.response.payload = {'tables': names}

{"tables": ["admissions", "appointments", "insurers", "inventory", "invoices", "locations", "occupancy", "staff"]}

Page	What it covers
Queries	Reading the tables back with SQL
Files	Putting the files load_table reads into the lakehouse
Loading data into tables	These methods at work in the nightly load

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