Datasphere-CLI 0.3.0

Unofficial TUI for automating recurring tasks in SAP Datasphere

SAP Datasphere Automation

Retro-styled CLI for SAP Datasphere that automates various tasks such as managing analytical models, remote tables, task chains and views.

📋 Table of Contents

• Overview
• Features
• Prerequisites
• Installation
• Configuration
• Usage
• Detailed Function Overview
• Development
• Notes
• Disclaimer

🎯 Overview

This program enables the automation of recurring tasks in SAP Datasphere. It provides scripts for managing:

• Analytical Models
• Remote Tables
• Task Chains
• Views

✨ Features

Analytical Models

• Export all analytical models with their views
• Export all analytical models of a specific space with their views
• Runtime analysis for persisting all views of analytical models

Remote Tables

• Create statistics (Record Count, Simple Statistics, Histogram)
• Refresh existing statistics

Task Chains

• Run task chains

Views

• Export all views with a perfect persistence score of 10 (using view analyzer)
• Export all views that have an attribute that contains a specific substring
• Create partitions by year
• Remove partitions
• Lock partitions up to a specific year
• Unlock partitions
• Persist views
• Unpersist views

🔧 Prerequisites

• Python: Version 3.12 or 3.13 (see pyproject.toml)
• uv for package management

📦 Installation

Option A: Windows (Release)

1. Download the latest release asset: DatasphereAutomation-<version>-windows.exe.

2. Run via double-click or from a terminal:

   DatasphereAutomation-<version>-windows.exe
   

---

Option B: macOS (Release, CLI)

1. Download the latest release asset: DatasphereAutomation-<version>-macos.

2. Remove Gatekeeper quarantine and make it executable:

   xattr -d com.apple.quarantine DatasphereAutomation-<version>-macos
   chmod +x DatasphereAutomation-<version>-macos
   

3. Run from the terminal:

   ./DatasphereAutomation-<version>-macos
   

Note: The Mac app can't be opened via double-click.

---

Option C: Install from Git (all platforms)

1. Clone the repository:

   git clone https://github.com/peterschwps/SAP-Datasphere-CLI.git
   cd SAP-Datasphere-CLI
   

2. Install with uv (recommended):

   uv sync
   

3. Install the required browsers for Playwright:

   uv run playwright install
   

   Docs: https://playwright.dev/docs/intro.

For Developers

1. Clone the repository and navigate to the project directory

2. Install dev dependencies:

   uv sync --group dev
   

3. Install Playwright:

   uv run playwright install
   

🔧 Configuration

The configuration is quite similar to the official SAP Datasphere CLI. In Datasphere you need to create an OAuth Client for Interactive Usage. This client will be used to authenticate and execute commands on SAP Datasphere. The full configuration of the settings file (settings.ini) is described down below.

[!IMPORTANT] Please set the Redirect URI to http://localhost:8080 when creating the OAuth Client. This is the default port that the CLI listens on to retrieve the callback code.

This is how your OAuth Client should look like:

Creating settings.ini

In order to create the settings file you need to run the CLI once. This will create a settings.ini in your user configuration directory:

• macOS/Linux: ~/.config/Datasphere/settings.ini
• Windows: %APPDATA%\Datasphere\settings.ini

Configuring settings.ini

Open the settings.ini file and configure the following settings:

[Setup]
# Your SAP Datasphere URL
# (System > Administration > Tenant Links: SAP Datasphere URL)
DATASPHERE_URL = https://example.eu10.hcs.cloud.sap

# The Authorization URL for OAuth Clients
# (System > Administration > App Integration: Authorization URL)
AUTHORIZATION_URL = https://example.authentication.eu10.hana.ondemand.com/oauth/authorize

# The Token URL for OAuth CLients
# (System > Administration > App Integration: Token URL)
TOKEN_URL = https://example.authentication.eu10.hana.ondemand.com/oauth/token

# Browser to use for the initial Authentication: 'CHROME' or 'EDGE'
BROWSER_TO_USE = EDGE

[Credentials]
# OAuth Client ID of your Configured Client
# (System > Administration > App Integration: Configured Clients: OAuth Client ID)
CLIENT_ID = 

# Secret of your Configured Client
# (System > Administration > App Integration: Configured Clients: Secret)
# NOTE: This value can be left empty and be set with an environment variable 'SECRET'.
SECRET = 

🚀 Usage

Execution

uv run python main.py

Executable:

.\DatasphereAutomation.exe

First Run / Expired Tokens

On first execution (or if your refresh token has expired), the CLI will open a browser window. After logging in the site will automatically redirect to http://localhost:8080, fetch the callback code and close the browser window.

This will create a login session which can be refreshed automatically in the future.

Menu Navigation

The program starts with an interactive menu:

1. Select a category
2. Choose a function
3. Enter the required parameters
4. Optionally select the number of threads for parallel execution

Directory Structure

The datasphere/ folder will be created in the directory where you run the program. It contains three important subdirectories:

• exports/: Contains all extracted data created during program execution (JSON, CSV files)
• results/: Contains an overview of executed tasks showing their status (successful / unsuccessful)
• tasks/: Contains all task files (CSV format) that specify what should be processed

[!WARNING] All files in exports/ and results/ are reset on program start! If you want to preserve files, rename or move them to a different location.

Threading

For time-intensive tasks, threads can be used to process multiple tasks in "parallel" using asynchronous requests. This can significantly improve performance but should be used with caution to avoid triggering rate limits. A thread count of 5-10 has proven to work well.

Stopping the Program

You can stop program execution at any time by pressing Ctrl + C.

📖 Detailed Function Overview

1. Analytical Models

1.1 Export All Analytical Models with their Views

Creates an overview of ALL analytical models with their views in JSON format.

Required task file: None

Parameters:

• Skip duplicates (yes/no): If enabled, views that already occur in multiple analytical models are only saved once and not for every model.

Output file: exports/analytical_models_with_all_views.json

Example output:

{
    "6BB18AB407AC02FH23804E421859F129": {
        "name": "Sales Analytical Model",
        "dependencies": {
            "606E8AB407FG02FB18004E438092F770": [
                "SALES_DEPARTMENT",
                "Sales2025"
            ],
            "606E8AB407FG02FB58929E438092F771": [
                "MASTER_DATA",
                "Customers"
            ]
        }
    }
}

1.2 Export All Analytical Models of a Specific Space with their Views

Performs the same logic as 1.1, but only processes analytical models from a specific space.

Required task file: None

Parameters:

• Space name: The technical name of the space (e.g., CENTRAL_IT)
• Skip duplicates (yes/no): If enabled, views that already occur in multiple analytical models are only saved once and not for every model.

Output file: exports/analytical_models_with_all_views_in_<space_name>.json

Example output:

{
    "6BB18AB407AC02FH23804E421859F129": {
        "name": "Sales Analytical Model",
        "dependencies": {
            "606E8AB407FG02FB18004E438092F770": [
                "SALES_DEPARTMENT",
                "Sales2025"
            ],
            "606E8AB407FG02FB58929E438092F771": [
                "MASTER_DATA",
                "Customers"
            ]
        }
    }
}

1.3 Runtime Analysis for Persisting All Views of Analytical Models

Checks the persistence time for all views of the analytical models listed in the task file.

Required task file: tasks/analytical_models_to_check_view_persistence_time.csv

Parameters: None

Output file: exports/analytical_models_with_all_views_and_persistence_time.json

Example output:

{
    "6BB18AB407AC02FH23804E421859F129": {
        "name": "Sales Analytical Model",
        "dependencies": {
            "606E8AB407FG02FB18004E438092F770": {
                "space": "SALES_DEPARTMENT",
                "name": "Sales2025",
                "runtime": 78,
                "alreadyPersisted": true,
                "removedPersistency": false
            },
            "606E8AB407FG02FB58929E438092F771": {
                "space": "MASTER_DATA",
                "name": "Customers",
                "runtime": 123,
                "alreadyPersisted": false,
                "removedPersistency": true
            }
        }
    }
}       

Note: A runtime value of null indicates an error occurred (or the program is still running if the file is opened during execution).

2. Remote Tables

2.1 Create Statistics (Record Count, Simple Statistics or Histogram)

Creates statistics for all remote tables that do not have a statistic or those that have a statistic of a different type. Existing tables with the same statistics type are skipped.

Please note:: For remote tables that already have the same statistics type, you should use the refresh statistics script (2.2).

Required task file: None

Parameters:

• Statistics type:
  1. Record Count
  2. Simple Statistics
  3. Histogram

Output file: None

Example output: None

Reference: SAP Datasphere Documentation - Statistics for Remote Tables

2.2 Refresh Existing Statistics

Updates all existing statistics for remote tables.

Required task file: None

Parameters: None

Output file: None

Example output: None

3. Task Chains

3.1 Run Task Chains

Runs all task chains in the task file and exports the results of the runs.

Required task file: tasks/task_chains_to_run.csv

Parameters: None

Output file: tasks/task_chains_completed.csv

Example output:

entity,space,isCompleted,runtime
AnalyzeSales2025,SALES_DEPARTMENT,true,1025

4. Views

4.1 Export All Views with a Perfect Persistence Score of 10 (Using View Analyzer)

Performs view analysis on all views and saves all views with a perfect persistence score of 10.

Required task file: None

Parameters: None

Output file: exports/best_views_to_persist.csv

Example output:

entity,space,businessName,isPersisted
Sales2025,SALES_DEPARTMENT,Sales (2025),True 

4.2 Export All Views That Have an Attribute That Contains a Specific Substring

Finds all views that have an attribute containing a specific substring.

Required task file: None

Parameters:

• Search word: The substring to search for (e.g., YEAR)

Output file: exports/view_attributes.csv

Example output (searching for "YEAR"):

entity,space,businessName,attribute
Sales2025,SALES_DEPARTMENT,Sales (2025),FISCAL_YEAR
Customers,SALES_DEPARTMENT,All Customers,YEAR

4.3 Create Partitions by Year

Creates partitions for views based on a yearly interval. Only columns with full year numbers can be used (in Datasphere: STRING(4)).

Required task file: tasks/views_to_create_partitions.csv

Parameters:

• Lower bound (>=): Start year for first partition (e.g., 2000)
• Upper bound (<): End year for last partition (e.g., 2040)
• Overwrite existing partitions (yes/no): Whether to overwrite if partitions already exists

Example: For input 2000 to 2040:

• Partition 1: >= 2000 AND < 2001
• Partition 2: >= 2001 AND < 2002
• ...
• Partition 40: >= 2039 AND < 2040

Output file: results/views_partitions_created.csv

Example output:

entity,space,attribute,createdPartition
Sales2025,SALES_DEPARTMENT,FISCAL_YEAR,True
Customers,SALES_DEPARTMENT,YEAR,True

4.4 Remove Partitions

Removes all existing partitions from specified views.

Required task file: tasks/views_to_delete_partitions.csv

Parameters: None

Output file: results/views_partitions_deleted.csv

Example output:

entity,space,removedPartition
Sales2025,SALES_DEPARTMENT,True

4.5 Lock Partitions Up to a Specific Year

Locks partitions up to and including a specific year (<= year entered). Requires that the views already have partitions. Only partitions with yearly values can be locked (in Datasphere STRING(4)).

Required task file: tasks/views_to_lock_partitions.csv

Parameters:

• Year: The year up to which partitions should be locked (the entered year is also locked)

Output file: results/views_partitions_locked.csv

Example output:

entity,space,lockedPartitions
Sales2025,SALES_DEPARTMENT,True

4.6 Unlock Partitions

Unlocks all existing partitions for specified views.

Required task file: tasks/views_to_unlock_partitions.csv

Parameters: None

Output file: results/views_partitions_unlocked.csv

Example output:

entity,space,unlockedPartitions
Sales2025,SALES_DEPARTMENT,True

4.7 Persist Views

Persists all views listed in the task file.

Required task file: tasks/views_to_persist.csv

Parameters:

• Save runtime (yes/no): Whether to record and save the persistence runtime

Output file: results/views_persisted.csv

Example output:

entity,space,isPersisted,runtime
Sales2025,SALES_DEPARTMENT,True,37
Customers,SALES_DEPARTMENT,True,9

4.8 Unpersist Views

Removes persistence from all views listed in the task file.

Required task file: tasks/views_to_unpersist.csv

Parameters: None

Output file: results/views_unpersisted.csv

Example output:

entity,space,isRemoved
Sales2025,SALES_DEPARTMENT,True

👨‍💻 Development

Code Quality

The project uses:

• ruff for linting and code formatting
• pyright for type checking (in basic mode)

Setting up Development Environment

1. Clone the repository

2. Install development environment:

   uv sync --group dev
   

3. Run pre-commit checks:

   uv run ruff check .
   uv run pyright .
   

Logging

The program uses logging. Log files are created for each day and saved in the user data directory:

• macOS/Linux: ~/.local/share/Datasphere/
• Windows: %LOCALAPPDATA%\Datasphere\

📃 Notes

• Cookies: Authentication cookies are saved in ~/.config/Datasphere/.cookies.json and automatically reused.
• Session Duration: The SAP Datasphere session expires after 1 hour and is automatically renewed using the persistent authentication cookies which last for 3 months.
• Threading: "Parallel" execution is implemented using asynchronous requests. Running tasks simoultaneously can improve performance but should be used with caution to avoid triggering rate limits.
• Export/Results: All files in exports/ and results/ are overwritten on the next program start. You can either move or rename them to prevent results being overwritten.
• Browser: When using browser authentication, a new browser profile is being created to speed up future logins.

🚨 Disclaimer

Important Note: This tool is designed for use with SAP Datasphere. Please ensure you have the necessary permissions before executing automation tasks.

Disclaimer: It is in no way affiliated with, authorized, maintained, or endorsed by SAP or any of its affiliates or subsidiaries. It is an independent and unofficial project. Use it at your own risk.