Tables¶

Manage SQL tables on Microsoft Fabric Data Warehouses and SQL Analytics Endpoints. Commands and tools cover listing, counting, reading, creating (including CTAS, empty DDL from schema inference, and zero-copy clone), deleting, clearing, renaming, and loading data via COPY INTO from local files or remote URLs.

Targets: Data Warehouse / SQL Analytics Endpoint

CLI¶

tables clear¶

Targets: Data Warehouse only

Truncate a table (delete all rows, keep structure). You will be asked to confirm unless --yes is passed.

Synopsis

fdw [-w WORKSPACE] tables clear [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Example

fdw -w MyWorkspace --yes tables clear SalesWH dbo.staging_load

tables columns¶

Targets: Data Warehouse / SQL Analytics Endpoint

List the columns of a table, including name, formatted data type, nullability, ordinal position, collation, identity, and computed flags.

Synopsis

fdw [-w WORKSPACE] tables columns [WAREHOUSE] QUALIFIED_NAME

QUALIFIED_NAME must be a dot-separated schema.table_name string, e.g. dbo.Sales.

Example

fdw -w MyWorkspace tables columns SalesWH dbo.Sales

 ordinal  name    data_type     nullable  is_identity  is_computed  collation_name
 -------  ------  ------------  --------  -----------  -----------  ----------------------------
 1        id      INT           False     True         False
 2        amount  DECIMAL(18,2) True      False        False
 3        label   NVARCHAR(100) True      False        False        Latin1_General_CI_AS

tables clear¶

Targets: Data Warehouse only

Truncate a table (delete all rows, keep structure). You will be asked to confirm unless --yes is passed.

Synopsis

fdw [-w WORKSPACE] tables clear [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Example

fdw -w MyWorkspace --yes tables clear SalesWH dbo.staging_load

tables cluster-by¶

Targets: Data Warehouse only

Change (or remove) the data-clustering columns of an existing table via a transactional CTAS-swap.

Performance note: This operation copies the entire table. Runtime is proportional to table size.

The operation is atomic: all three steps run inside a single transaction. Any failure rolls back automatically - no orphan temp table is left behind.

Synopsis

fdw [-w WORKSPACE] tables cluster-by [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Examples

# Set new clustering columns
fdw -w MyWorkspace --yes tables cluster-by SalesWH dbo.orders \
  --cluster-by CustomerID --cluster-by SaleDate

# Remove clustering entirely
fdw -w MyWorkspace --yes tables cluster-by SalesWH dbo.orders

tables cluster-columns¶

Targets: Data Warehouse only

List the data-clustering columns of a table, ordered by clustering ordinal. Returns an empty table when no clustering is defined (exit 0).

Synopsis

fdw [-w WORKSPACE] tables cluster-columns [WAREHOUSE] QUALIFIED_NAME

Example

fdw -w MyWorkspace --json tables cluster-columns SalesWH dbo.orders

[
  {"column_name": "city", "clustering_ordinal": 1},
  {"column_name": "country", "clustering_ordinal": 2}
]

tables clone¶

Targets: Data Warehouse only

Create a zero-copy clone of a table using CREATE TABLE … AS CLONE OF. Pass --at to clone from a point in time within the warehouse data-retention window.

Synopsis

fdw [-w WORKSPACE] tables clone [OPTIONS] [ITEM]

Example

# Clone to the current state
fdw -w MyWorkspace tables clone SalesWH \
  --source dbo.orders \
  --name dbo.orders_backup

# Point-in-time clone
fdw -w MyWorkspace tables clone SalesWH \
  --source dbo.orders \
  --name dbo.orders_may_snapshot \
  --at 2024-05-20T14:00:00

tables count¶

Targets: Data Warehouse / SQL Analytics Endpoint

Return the total row count of a table using SELECT COUNT_BIG(*).

Use --as-of or --ago to count rows as they were at an earlier point in time (time travel). The same retention window applies as for tables read.

Synopsis

fdw [-w WORKSPACE] tables count [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Example

fdw -w MyWorkspace --json tables count SalesWH dbo.orders

{"schema": "dbo", "name": "orders", "row_count": 999999}

# Point-in-time row count
fdw -w MyWorkspace --json tables count SalesWH dbo.orders --as-of 2024-03-15T10:00:00Z
fdw -w MyWorkspace --json tables count SalesWH dbo.orders --ago 1h

tables export¶

Targets: Data Warehouse / SQL Analytics Endpoint

Export the full contents of a table to a local file in Parquet, CSV, or JSON format.

The output format is inferred from the --output extension (.parquet, .csv, .json, .pq). Use --format to override.

Memory caveat: the full result set is loaded into the Python process before writing. Ensure sufficient RAM, or use --limit to export a sample only. A streaming variant is a planned future follow-up.

Use --as-of or --ago to export the table as it was at an earlier point in time (time travel). The same retention window applies as for tables read.

By default, if the output file already exists it is overwritten. Pass --no-overwrite to fail instead.

Synopsis

fdw [-w WORKSPACE] tables export [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Example

# Full export to Parquet (format inferred from extension)
fdw -w MyWorkspace tables export SalesWH dbo.orders --output orders.parquet

# Sample 500 rows as CSV
fdw -w MyWorkspace tables export SalesWH dbo.orders --output sample.csv --limit 500

# Point-in-time export
fdw -w MyWorkspace tables export SalesWH dbo.orders --output snapshot.parquet --as-of 2024-03-15T10:00:00Z

# JSON output (machine-readable)
fdw -w MyWorkspace --json tables export SalesWH dbo.orders --output out.parquet

{"status": "exported", "rows": 42300, "output": "out.parquet"}

tables create¶

Targets: Data Warehouse only

Create a new table on a Fabric Data Warehouse. Two modes are available:

• CTAS (CREATE TABLE … AS SELECT): supply --select or --from-file. The body must start with SELECT (leading block/line comments are allowed).
• Empty DDL (CREATE TABLE … (col TYPE, …)): supply exactly one of --from-parquet, --from-csv, --from-json, or one-or-more --column. The schema is derived (Parquet/CSV/JSON inference) or listed explicitly; no data is ever read or inserted - this scaffolds the table structure only. To load data afterwards, use tables load.

Synopsis

fdw [-w WORKSPACE] tables create [OPTIONS] [WAREHOUSE]

CTAS options¶

Exactly one of --select or --from-file must be provided for the CTAS path. Cannot be combined with empty-DDL options.

Empty-DDL options¶

--from-parquet, --from-csv, --from-json, and --column are mutually exclusive with each other and with the CTAS path. For the explicit path at least one --column must be provided.

Arrow → T-SQL type mapping (Parquet / CSV / JSON inference)

Examples

# CTAS
fdw -w MyWorkspace tables create SalesWH \
  --name dbo.orders_2026 \
  --select "SELECT * FROM dbo.orders WHERE YEAR(sale_date) = 2026"

# Empty table from Parquet schema
fdw -w MyWorkspace tables create SalesWH \
  --name dbo.sales_empty \
  --from-parquet ./exports/sales.parquet

# Empty table from CSV header (type inference)
fdw -w MyWorkspace tables create SalesWH \
  --name staging.raw_products \
  --from-csv ./data/products.csv --varchar-length 500

# Empty table with explicit inline columns
fdw -w MyWorkspace tables create SalesWH \
  --name dbo.events \
  --column "event_id:BIGINT:notnull" \
  --column "event_type:VARCHAR(100)" \
  --column "occurred_at:DATETIME2(7)"

# Empty table from JSON data - JSONL (schema inferred from the data)
fdw -w MyWorkspace tables create SalesWH \
  --name staging.events \
  --from-json ./data/events.jsonl

# Empty table from JSON data - a JSON array of objects
fdw -w MyWorkspace tables create SalesWH \
  --name dbo.audit_log \
  --from-json ./data/audit_log.json --varchar-length 500

# CTAS with CLUSTER BY (column existence not validated on CTAS path)
fdw -w MyWorkspace tables create SalesWH \
  --name dbo.orders_2026 \
  --select "SELECT CustomerID, SaleDate, Amount FROM dbo.orders WHERE YEAR(SaleDate) = 2026" \
  --cluster-by CustomerID --cluster-by SaleDate

# Empty table with explicit columns and CLUSTER BY
fdw -w MyWorkspace tables create SalesWH \
  --name dbo.events \
  --column "CustomerID:INT:notnull" \
  --column "SaleDate:DATE:notnull" \
  --column "Amount:DECIMAL(18,2)" \
  --cluster-by CustomerID --cluster-by SaleDate

tables delete¶

Targets: Data Warehouse only

Drop a table. You will be asked to confirm unless --yes is passed.

Synopsis

fdw [-w WORKSPACE] tables delete [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Example

fdw -w MyWorkspace --yes tables delete SalesWH dbo.orders_2026

tables health-check¶

Targets: SQL Analytics Endpoint

Run sp_get_table_health_metrics against a single table to surface Delta/Parquet layout issues (small files, fragmentation, excessive deletes/updates, delayed checkpoints) and decide whether maintenance is needed.

The proc is Generally Available (announced at Build 2026) but Microsoft Learn has no dedicated reference page yet. Output columns are passed through verbatim - the exact column names and types are determined by the proc and may change across Fabric releases.

Synopsis

fdw [-w WORKSPACE] tables health-check [ENDPOINT] QUALIFIED_NAME

Example

fdw -w MyWorkspace tables health-check MySqlEndpoint dbo.FactSales
fdw -w MyWorkspace --json tables health-check MySqlEndpoint dbo.FactSales

tables list¶

Targets: Data Warehouse / SQL Analytics Endpoint

List all tables on a warehouse or SQL Analytics Endpoint. Pass --schema to filter to a single schema.

Synopsis

fdw [-w WORKSPACE] tables list [OPTIONS] [WAREHOUSE]

Example

fdw -w MyWorkspace tables list SalesWH --schema dbo

 schema_name  name      created               modified
 ------------ --------- --------------------- ---------------------
 dbo          customers 2026-01-10T08:00:00Z  2026-06-01T12:00:00Z
 dbo          orders    2026-02-01T09:00:00Z  2026-05-15T14:00:00Z

tables load¶

Targets: Data Warehouse only

Load data into a warehouse table via COPY INTO from either a local file or a remote URL.

Local file path (--file): the file is staged to a temporary Lakehouse in OneLake (chunked DFS upload), loaded into the target table via COPY INTO, and the staging Lakehouse is automatically deleted in a finally block regardless of success or failure. JSON files are converted client-side to Parquet (requires pyarrow) before staging.

Remote URL (--url): COPY INTO is issued directly from the given URL. For OneLake or same-tenant URLs no credential is needed. For secured external URLs (Azure Blob Storage, ADLS Gen2) supply --credential-type and --secret/--identity as appropriate.

Auto-create (create-and-load): Pass --create to auto-create the target table from the source schema before loading (local files only; requires pyarrow). The schema is inferred from the source:

• Parquet: exact types are read from the Parquet footer (no row data is read).
• CSV: the header row and up to --sample-rows rows are read for type inference. Use --all-varchar to skip inference and force every column to VARCHAR.
• JSON: the file is converted to Parquet internally (as required for staging); the schema is read from the resulting Parquet footer.

Use --if-exists to control behaviour when the table already exists:

truncate and replace are permanently destructive and require confirmation (or --yes / -y).

Use --cleanup-on-failure to drop the table if WE created it in this call and the subsequent COPY INTO fails. A pre-existing table is never dropped by this flag.

Synopsis

fdw [-w WORKSPACE] tables load [OPTIONS] [ITEM] QUALIFIED_NAME

QUALIFIED_NAME is the dot-separated schema.table_name of the destination table.

Examples

# Load a local CSV into an existing table (header row present)
fdw -w MyWorkspace tables load SalesWH dbo.sales --file data.csv

# Load a local Parquet file into an existing table
fdw -w MyWorkspace tables load SalesWH dbo.events --file events.parquet

# Load a local JSON file (converts to Parquet internally; requires pyarrow)
fdw -w MyWorkspace tables load SalesWH dbo.products --file products.json

# Auto-create the table from a Parquet schema, then load
fdw -w MyWorkspace tables load SalesWH dbo.sales --file data.parquet --create

# Auto-create from CSV, force all columns to VARCHAR
fdw -w MyWorkspace tables load SalesWH dbo.raw --file raw.csv --create --all-varchar

# Replace the existing table (drop + recreate schema + load), skip confirmation
fdw -w MyWorkspace tables load SalesWH dbo.sales --file data.parquet --create \
    --if-exists replace -y

# Auto-create; drop the table if the load fails (cleanup_on_failure)
fdw -w MyWorkspace tables load SalesWH dbo.sales --file data.parquet --create \
    --cleanup-on-failure

# Auto-create with CLUSTER BY (columns must exist in the inferred schema)
fdw -w MyWorkspace tables load SalesWH dbo.sales --file data.parquet --create \
    --cluster-by SaleDate --cluster-by CustomerID

# Load from a remote OneLake URL (no credential needed)
fdw -w MyWorkspace tables load SalesWH dbo.orders \
    --url "https://onelake.dfs.fabric.microsoft.com/ws/lh.Lakehouse/Files/orders.parquet" \
    --format parquet

# Load from Azure Blob with SAS token
fdw -w MyWorkspace tables load SalesWH dbo.events \
    --url "https://myaccount.blob.core.windows.net/data/events.csv" \
    --format csv --credential-type sas --secret "?sv=2021&..."

tables read¶

Targets: Data Warehouse / SQL Analytics Endpoint

Read up to --count rows from a table and emit them as JSON (default), CSV, or Parquet.

CSV and Parquet formats require --output. JSON is emitted to stdout by default.

Use --as-of or --ago to read the table as it was at an earlier point in time (time travel). Fabric supports OPTION (FOR TIMESTAMP AS OF ...) on SELECT statements; the retention window is 1-120 days (default 30). Timestamps outside the retention window error server-side - no client-side pre-validation is performed.

Synopsis

fdw [-w WORKSPACE] tables read [OPTIONS] [WAREHOUSE] QUALIFIED_NAME

Example

fdw -w MyWorkspace tables read SalesWH dbo.orders --count 5

[
  {"id": 1, "amount": 99.99, "customer_id": 42},
  ...
]

# Point-in-time read
fdw -w MyWorkspace tables read SalesWH dbo.orders --as-of 2024-03-15T10:00:00Z
fdw -w MyWorkspace tables read SalesWH dbo.orders --ago 2d

tables rename¶

Targets: Data Warehouse only

Rename a table via sp_rename. The new name must be an unqualified (bare) identifier - sp_rename cannot move a table to a different schema.

Synopsis

fdw [-w WORKSPACE] tables rename [OPTIONS] [ITEM] QUALIFIED_NAME

QUALIFIED_NAME is the current dot-separated schema.table_name.

Example

fdw -w MyWorkspace tables rename SalesWH dbo.orders_2025 --new-name orders_archive_2025

MCP tools¶

clear_table¶

Targets: Data Warehouse only

Truncate a SQL table (remove all rows, preserve structure).

CAUTION: This is a destructive, irreversible operation. All rows will be permanently deleted. The table structure is preserved. Confirm with the user before calling.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.

Returns: { "truncated": true }: confirmation.

clone_table¶

Targets: Data Warehouse only

Create a zero-copy clone of a table using CREATE TABLE … AS CLONE OF …. Only supported on Fabric Data Warehouses (not SQL Analytics Endpoints).

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID.
• source (str): qualified source table name, e.g. dbo.sales.
• new_table (str): qualified name for the new cloned table, e.g. dbo.sales_clone.
• at (str | null, optional): ISO-8601 UTC timestamp for a point-in-time clone (e.g. 2024-05-20T14:00:00). Must be within the data-retention window. When omitted, the clone reflects the current state of the source table.

Returns: Table: the newly-created cloned table record.

get_table_columns¶

Targets: Data Warehouse / SQL Analytics Endpoint

Return column metadata for a SQL table via sys.columns. Works on both Fabric Data Warehouses and SQL Analytics Endpoints.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• qualified_name (str): dot-separated table name, e.g. dbo.Sales.

Returns: list[dict]: one dict per column, each containing:

• ordinal (int): 1-based column position (column_id).
• name (str): column name.
• data_type (str): formatted T-SQL type string, e.g. INT, VARCHAR(50), NVARCHAR(MAX), DECIMAL(18,2), DATETIME2(7).
• nullable (bool): whether the column allows NULL.
• collation_name (str | null): collation name, if applicable.
• is_identity (bool): whether the column is an identity column.
• is_computed (bool): whether the column is a computed column.

Results are ordered by ordinal position. Raises a ToolError if the table does not exist.

count_table_rows¶

Targets: Data Warehouse / SQL Analytics Endpoint

Return the total row count of a table via SELECT COUNT_BIG(*).

Supports time-travel counts via as_of: supply an ISO-8601 UTC timestamp to count rows as they were at that point in time. The same retention window applies as for read_table.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.
• as_of (str, optional): ISO-8601 UTC timestamp for a point-in-time count. Omit to count the latest data.

Returns: { "schema": str, "name": str, "row_count": int }: the schema name, table name, and total row count.

create_empty_table¶

Targets: Data Warehouse only

Create an empty SQL table from an explicit column specification (DDL only - no data is ever read or inserted). This scaffolds the table structure so that data can be loaded separately.

Server-side file access is unreliable in MCP deployments, so CSV/Parquet schema inference is not available via this tool. Use fdw tables create --from-parquet or --from-csv (CLI) for file-based schema inference.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID. SQL Analytics Endpoints are rejected.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.
• columns (list[object]): non-empty list of column definitions, each an object with:
• name (str): column identifier (must be a valid SQL identifier).
• sql_type (str): Fabric-DW-supported T-SQL type, e.g. "INT", "VARCHAR(255)", "DECIMAL(18,2)".
• nullable (bool, optional, default true): whether the column allows NULL.
• cluster_by (list[str], optional): column names for the CLUSTER BY clause (up to 4). Each name must appear in columns.

Returns: Table: the newly-created table record.

Example call:

{
  "workspace": "MyWorkspace",
  "item": "SalesWarehouse",
  "qualified_name": "dbo.events",
  "columns": [
    {"name": "event_id", "sql_type": "BIGINT", "nullable": false},
    {"name": "event_type", "sql_type": "VARCHAR(100)", "nullable": true},
    {"name": "occurred_at", "sql_type": "DATETIME2(7)", "nullable": false}
  ]
}

create_table¶

Targets: Data Warehouse only

Create a new SQL table via CTAS (CREATE TABLE … AS SELECT).

CAUTION: select_body is executed verbatim as DDL. Confirm intent before calling. The first non-comment keyword must be SELECT.

When cluster_by is supplied the DDL becomes CREATE TABLE … WITH (CLUSTER BY ([c1], [c2])) AS SELECT …. Column existence is not validated on the CTAS path because result columns come from the SELECT and are not known ahead of time.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.
• select_body (str): the SELECT statement for the CTAS source.
• cluster_by (list[str], optional): column names for the CLUSTER BY clause (up to 4). Column existence is not validated on the CTAS path.

Returns: Table: the newly-created table record.

delete_table¶

Targets: Data Warehouse only

Drop a SQL table.

CAUTION: This is a destructive, irreversible operation. All data will be permanently deleted. Confirm with the user before calling.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.

Returns: { "dropped": true }: confirmation.

get_cluster_columns¶

Targets: Data Warehouse only

Return the data-clustering columns of a table, ordered by clustering ordinal. Returns an empty list when no clustering is defined (not an error).

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID. SQL Analytics Endpoints are rejected.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.

Returns: list[{ "column_name": str, "clustering_ordinal": int }]: ordered by ascending clustering_ordinal.

get_table_health_metrics¶

Targets: SQL Analytics Endpoint

Return health metrics for a table via sp_get_table_health_metrics. Only supported on SQL Analytics Endpoints (not Data Warehouses).

The proc surfaces Delta/Parquet layout issues such as small files, fragmentation, excessive deletes/updates, and delayed checkpoints - useful for deciding whether table maintenance is needed.

The proc is Generally Available (announced at Build 2026) but its output column schema is not yet documented by Microsoft. Columns and rows are passed through verbatim.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): SQL Analytics Endpoint name or GUID. Data Warehouses are rejected with a ToolError.
• qualified_name (str): dot-separated table name, e.g. dbo.FactSales.

Returns: { "columns": list[str], "rows": list[list] }: column names and rows passed through verbatim from the proc.

import_table_from_url¶

Targets: Data Warehouse only

Load data from a remote URL into an existing Data Warehouse table with control over what happens when the table already has data. This tool extends load_table_from_url with an if_exists policy.

if_exists policy:

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID. SQL Analytics Endpoints are rejected.
• qualified_name (str): dot-separated qualified table name, e.g. dbo.sales.
• url (str): source URL (OneLake DFS URL or external Azure Blob URL).
• file_type ("CSV" | "PARQUET"): file type to load. JSON is not supported for remote URLs.
• if_exists ("fail" | "append" | "truncate" | "replace", default "fail"): what to do when the target table already exists.
• credential_type ("none" | "sas" | "managed-identity" | "service-principal" | "account-key", default "none"): credential type for secured external URLs.
• secret (str | null, optional): credential secret (SAS token, client secret, or account key). Never logged.
• identity (str | null, optional): identity for managed-identity or service-principal.
• delimiter (str | null, optional): CSV column delimiter (e.g. ,, \t).
• has_header (bool, default true): when true, the first CSV row is a header and is skipped.
• encoding (str | null, optional): CSV encoding (e.g. UTF8, UTF8BOM).
• field_quote (str | null, optional): CSV field-quote character.
• row_terminator (str | null, optional): CSV row terminator (e.g. \n, \r\n).
• max_errors (int | null, optional): maximum errors before aborting.
• rejected_row_location (str | null, optional): URL to write rejected rows to.

Returns: CopyIntoResult: { "rows_loaded": int, "rows_rejected": int, "target": "schema.table" }.

list_tables¶

Targets: Data Warehouse / SQL Analytics Endpoint

List SQL tables on a warehouse or SQL Analytics Endpoint.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• schema (str | null, optional): when provided, only tables in this schema are returned.

Returns: list[Table]: each with schema_name, name, qualified_name, created, modified.

load_table_from_url¶

Targets: Data Warehouse only

Load data into a Data Warehouse table via COPY INTO from a remote URL. For OneLake or same-tenant URLs, no credential is needed. For secured external URLs (Azure Blob Storage, ADLS Gen2), supply credential_type and the appropriate secret/identity values.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID. SQL Analytics Endpoints are rejected.
• qualified_name (str): dot-separated qualified table name, e.g. dbo.sales.
• url (str): source URL (OneLake DFS URL or external Azure Blob URL).
• file_type ("CSV" | "PARQUET"): file type to load.
• credential_type ("none" | "sas" | "managed-identity" | "service-principal" | "account-key", default "none"): credential type for secured external URLs.
• secret (str | null, optional): credential secret (SAS token, client secret, or account key). Never logged.
• identity (str | null, optional): identity for managed-identity or service-principal.
• delimiter (str | null, optional): CSV column delimiter (e.g. ,, \t).
• has_header (bool, default true): when true, the first CSV row is a header and is skipped.
• encoding (str | null, optional): CSV encoding (e.g. UTF8, UTF8BOM).
• field_quote (str | null, optional): CSV field-quote character.
• row_terminator (str | null, optional): CSV row terminator (e.g. \n, \r\n).
• max_errors (int | null, optional): maximum errors before aborting.
• rejected_row_location (str | null, optional): URL to write rejected rows to.

Returns: CopyIntoResult: { "rows_loaded": int, "rows_rejected": int, "target": "schema.table" }.

read_table¶

Targets: Data Warehouse / SQL Analytics Endpoint

Return up to count rows from a table as JSON-serialisable columns and rows.

Supports time-travel reads via as_of: supply an ISO-8601 UTC timestamp to read the table as it was at that point in time. The Fabric retention window is 1-120 days (default 30); timestamps outside the window error server-side.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse or SQL analytics endpoint name or GUID.
• qualified_name (str): dot-separated table name, e.g. dbo.sales.
• count (int, default 10): maximum rows to return.
• as_of (str | null, optional): ISO-8601 UTC timestamp for a point-in-time read, e.g. 2024-03-15T10:00:00Z. Omit to read the latest data.

Returns: { "columns": list[str], "rows": list[list] }: column names and row arrays.

rename_table¶

Targets: Data Warehouse only

Rename a SQL table via sp_rename. Only supported on Fabric Data Warehouses (SQL Analytics Endpoints are rejected). The new name must be a bare (unqualified) identifier - sp_rename cannot move a table to a different schema.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID.
• qualified_name (str): current dot-separated qualified table name, e.g. dbo.sales.
• new_name (str): new bare table name (no schema prefix), e.g. sales_v2.

Returns: Table: the updated table record.

set_cluster_columns¶

Targets: Data Warehouse only

Change (or remove) the data-clustering columns of an existing table via a transactional CTAS-swap. Requires FABRIC_MCP_ALLOW_DESTRUCTIVE=1.

Performance note: This operation copies the entire table. Runtime is proportional to table size.

CAUTION: Dependent views and stored procedures that reference this table by name are NOT automatically updated by sp_rename and may need refreshing after the swap.

The operation is atomic: CTAS + DROP + sp_rename all run in one transaction. Any failure rolls back automatically - no orphan temp table is left behind.

Parameters:

• workspace (str): workspace name or GUID.
• item (str): warehouse name or GUID. SQL Analytics Endpoints are rejected.
• qualified_name (str): dot-separated qualified table name, e.g. dbo.sales.
• cluster_by (list[str] | null, optional): new column names for the CLUSTER BY clause (up to 4). Pass null or an empty list to remove clustering (rebuilds table without CLUSTER BY).

Returns: Table: the re-clustered table record.