fabric_onelake_read_delta_table() now uses the optional
Python ‘deltalake’ reader through ‘reticulate’. The
dest_dir argument has been removed. Remove this argument
from existing calls; use columns and limit to
restrict a read, or result = "arrow_stream" to consume
batches. Tables using unsupported Delta features should be read through
SQL or Spark instead.Discovery functions find the Fabric workspaces and items
available to the signed-in user or application. Use
fabric_workspaces() and fabric_items() for
general discovery, or typed helpers such as
fabric_lakehouses() and
fabric_semantic_models() to find a specific kind of item.
Discovery results are read-only R6 objects that include every service
field, reuse the discovery credential, and provide type-specific methods
for workspaces, SQL items, Lakehouses, Warehouses, mirrored databases,
Eventhouses, KQL databases, GraphQL APIs, semantic models, and runnable
jobs. Semantic models and runnable jobs expose status, wait, and
cancellation methods; KQL items expose status and wait methods for
asynchronous ingestion. Discovered resources can be passed directly to
other ‘fabricQueryR’ functions, avoiding copied IDs and endpoints in
most workflows. Use $as_list(), as.list(), or
output = "list" when a plain record is specifically
required.
fabric_livy_session() and
fabric_livy_batch_submit() add reusable Spark sessions and
standalone batch jobs. fabric_livy_query() is the simplest
option for running one piece of Spark code.
fabric_onelake_read_file(),
fabric_onelake_write_file(),
fabric_onelake_download(), and
fabric_onelake_upload() move files and Parquet, CSV, or
Arrow data between R, local storage, and OneLake.
fabric_onelake_list(),
fabric_onelake_metadata(), and
fabric_onelake_delete() list, inspect, and delete
files.
fabric_lakehouse_schemas(),
fabric_lakehouse_table(),
fabric_lakehouse_tables(),
fabric_lakehouse_read_table(),
fabric_lakehouse_load_table(), and
fabric_lakehouse_write_table() discover Lakehouse schemas
and tables, read them, load CSV or Parquet files, and write data frames
or Arrow data. Both ordinary and schema-enabled Lakehouses are
supported.
fabric_warehouse_schemas(),
fabric_warehouse_table(),
fabric_warehouse_tables(),
fabric_warehouse_read_table(), and
fabric_warehouse_write_table() discover schemas and tables,
read them, and bulk-write Warehouse tables using data frames or Arrow
data. Tables can be created, appended to, overwritten, or recreated as
requested.
fabric_mirrored_databases() and the
fabric_mirrored_database_*() helpers discover mirrored
databases and inspect or read their OneLake Delta tables. Discovered
records also work with the generic SQL helpers through each mirrored
database’s read-only SQL analytics endpoint.
fabric_kql_tables(),
fabric_kql_query(), and
fabric_kql_read_table() discover Eventhouse tables and
bring query or table results into R as typed R objects.
fabric_kql_ingest(),
fabric_kql_write_table(), and
fabric_kql_export() load existing files or R and Arrow data
into Eventhouse, monitor the load, and export large query results to
OneLake or other supported storage. A destination table can be created
when needed.
fabric_graphql_*() functions query a Fabric API for
GraphQL, inspect its schema, work through paginated results, and collect
the result into tidy R objects.
fabric_function_invoke() calls published Fabric User
Data Functions from R.
fabric_onelake_shortcuts() and
fabric_onelake_shortcut_*() functions inspect, create,
update, and delete OneLake shortcuts, which link Fabric items to data
stored elsewhere.
fabric_pbi_refresh_*() functions start, monitor,
wait for, cancel, and inspect the history of semantic-model
refreshes.
fabric_sql_connect() and
fabric_sql_query() support ODBC and ADBC connections,
discovered SQL items, bound query parameters, and Arrow streams for
larger results. SQL queries and Warehouse reads default to the driver’s
numeric conversion for ODBC, with a once-per-session precision warning,
and exact conversion for ADBC. Set
numeric_policy = "driver" to explicitly accept driver
conversion without the warning, or numeric_policy = "exact"
to reject potentially lossy ODBC results.
fabric_job_*() functions run, monitor, wait for, and
cancel Fabric Notebooks, data pipelines, Spark job definitions, and
other supported item jobs. They also inspect run history and manage
recurring schedules.
fabric_operation_*() functions resume, monitor, and
retrieve the results of longer-running Fabric tasks such as Lakehouse
loads.
Arrow streams from fabric_onelake_read_file() and
fabric_pbi_dax_query() release their temporary IPC files on
Windows even when returned Arrow tables remain in use.
Authenticated functions now consistently accept an ‘AzureAuth’
token, a bearer token, or a function that supplies refreshed tokens
through token; auth_args controls ‘AzureAuth’
sign-in. The older access_token argument for SQL and Livy
is deprecated.
fabric_pbi_dax_query() now accepts discovered
semantic models or direct IDs, can test results for a user under
row-level security, and reports incomplete Power BI results instead of
silently returning them. An optional Arrow mode provides typed tibbles
or streams for models that support it. Both JSON and Arrow executions
now expose a client-side HTTP timeout. Mixed JSON Whole
Number columns preserve both signed 64-bit extrema exactly.
fabric_onelake_read_delta_table() now reads current
or historical Lakehouse and compatible Warehouse tables through an
optional Python Delta reader. It supports selected columns, row limits,
and Arrow streams for large or nested results, including through
discovered workspace-private OneLake endpoints.
fabric_livy_query() table results now follow the
declared Spark schema and preserve large whole numbers and decimals
exactly.
fabric_livy_query() now bounds temporary-session
cleanup with a separate deadline and reports both errors when statement
execution and session deletion fail together.
Update e-mail address of maintainer in DESCRIPTION file (change to a personal e-mail address due to leaving the organization).
fabric_onelake_read_delta_table(): add experimental
support for specifying a Lakehouse schema name to read from a specific
schema within a Lakehouse which has Lakehouse schemas enabled.
fabric_livy_query() to run queries against the
‘Fabric Livy API’, allowing remote execution of ‘Spark’ code.