rehla-dbx-tools 1.2.2

Rehla FlightDeck for Databricks: a production-focused unified API toolkit for AWS workspace and account automation.

Rehla FlightDeck for Databricks

Rehla FlightDeck for Databricks is a unified, DataFrame-first API layer for AWS Databricks workspace and account operations.

About Rehla Digital Inc

Rehla Digital Inc builds cloud and data engineering solutions that help teams standardize platform operations, accelerate delivery, and reduce integration risk. This package is maintained as part of that effort to provide a practical, production-oriented Databricks API toolkit.

Install

pip install rehla_dbx_tools

Import in Python with underscores:

from rehla_dbx_tools import DatabricksApiClient

Install Spark extras if needed:

pip install "rehla_dbx_tools[spark]"

Quick Start

from rehla_dbx_tools import dbx

client = dbx()  # uses env vars (DATABRICKS_HOST/TOKEN or DBX_HOST/TOKEN)
print("jobs:", len(client.list_jobs(limit=25)))
print("active:", len(client.list_active_job_runs(limit=25)))

Explicit host/token:

from rehla_dbx_tools import dbx

client = dbx("https://dbc-xxxx.cloud.databricks.com", "dapi...token...")
print("runs:", len(client.list_recent_job_runs(limit=25)))

Token can be omitted if you want guided auth:

from rehla_dbx_tools import connect

client = connect(
    host="https://dbc-xxxx.cloud.databricks.com",
    open_browser_for_token=True,  # opens Access Tokens page
    prompt_for_token=True,         # prompts to paste token
)

Windows SSO flow (Databricks CLI login):

from rehla_dbx_tools import DatabricksApiClient

client = DatabricksApiClient.from_windows_sso(
    host="https://dbc-xxxx.cloud.databricks.com",
)

Notebook Context Bootstrap

Inside Databricks notebooks:

from rehla_dbx_tools import DatabricksApiClient

client = DatabricksApiClient.from_notebook_context()
if client.workspace is not None:
    clusters = client.workspace.list_clusters()
    spark_df = clusters.to_spark()
    display(spark_df)

Account API

account client is enabled when DATABRICKS_ACCOUNT_HOST and DATABRICKS_ACCOUNT_ID are set.

if client.account is not None:
    workspaces = client.account.list_workspaces()
    print(workspaces.to_pandas().head())

Version-Aware Generic Request

response = client.workspace.request_versioned(
    "GET",
    service="unity-catalog",
    endpoint="metastores",
    api_version="2.1",
)

Operation Coverage Status

This package build supports read and write operations through workspace and account clients.

• GET requests force pagination aggregation for DataFrame-first workflows.
• Delete operations are exposed for full API parity.
• Delete operation paths are not yet fully cycle-validated end-to-end in live environments for this release.

Version and help metadata

import rehla_dbx_tools as rdt

print(rdt.__version__)
print(rdt.__Help__)

Available tools (current build)

Workspace (client.workspace):

• list_jobs, get_job
• list_job_runs, get_job_run, get_job_run_output, export_job_run
• get_job_permissions, get_job_permission_levels
• get_object_permissions, set_object_permissions, update_object_permissions, get_object_permission_levels
• list_clusters, get_cluster, cluster_events
• get_cluster_permissions, get_cluster_permission_levels
• list_catalogs, list_schemas, get_catalog, get_schema
• list_sql_warehouses, get_sql_warehouse
• list_instance_pools, get_instance_pool
• list_cluster_policies, get_cluster_policy
• list_dbfs, get_dbfs_status, read_dbfs
• list_repos, get_repo
• list_secret_scopes
• list_tokens

Client-level convenience:

• list_jobs
• list_recent_job_runs
• list_active_job_runs

Account (client.account):

• get_assignable_roles_for_resource, get_rule_set, update_rule_set
• list_workspaces, get_workspace
• list_credentials
• list_storage_configurations
• list_networks
• list_private_access_settings
• list_vpc_endpoints
• list_customer_managed_keys
• list_users, get_user
• list_groups, get_group
• list_budget_policies, get_budget_policy
• list_log_delivery_configurations, get_log_delivery_configuration

For detailed setup and examples, see docs/USAGE.md.

How This Differs From Databricks SDK/API

• Less boilerplate: one-liner bootstrap (dbx(...) / connect(...)) for quick scripts.
• DataFrame-first: normalized payloads and built-in Pandas/Spark conversion paths.
• Forced read pagination: GET calls aggregate paginated records automatically for analysis workloads.
• Broad API surface: supports both read and mutation workflows from one client.
• Operational ergonomics: host normalization, env aliases, browser-guided token flow, and Windows SSO helper.

WordPress-Style Docs

• AWS + Databricks blog draft: docs/BLOG_AWS_DATABRICKS_WORDPRESS.md
• Complete tool-by-tool blog reference: docs/BLOG_TOOL_REFERENCE_WORDPRESS.md