dasl-client 1.0.31

The DASL client library used for interacting with the DASL workspace

DASL Client Library

The DASL (Databricks Antimatter Security Lakehouse) Client Library is a Python SDK for interacting with DASL services. This library provides an interface for interacting with DASL services, allowing you to manage datasources, rules, workspace configurations, and more from Databricks notebooks.

Features

• Simple Authentication: Automatic workspace detection in Databricks notebooks
• Datasource Management: Create, update, list, and delete datasources
• Rule Management: Define and manage security detection rules to identify threats
• Workspace Configuration: Update and retrieve DASL's workspace-level settings

Installation

Install from PyPI:

pip install dasl-client

Quick Start

Databricks Notebook Environment (Recommended)

The DASL client works best in Databricks notebooks with automatic authentication:

from dasl_client import Client

# Automatically detects Databricks context and authenticates
client = Client.for_workspace()
print("Connected to DASL!")

# List existing datasources
print("Existing datasources:")
for datasource in client.list_datasources():
    print(f"  - {datasource.metadata.name}")

# List detection rules
print("Existing detection rules:")
for rule in client.list_rules():
    print(f"  - {rule.metadata.name}")

Creating a Datasource

from dasl_client import DataSource, Schedule, BronzeSpec, SilverSpec

# Create a new datasource
datasource = Datasource(
    source="aws",
    source_type="cloudtrail",
    autoloader=Autoloader(
        enabled=True,
        schedule=Schedule(
            at_least_every="1h",
            enabled=True
        )
    ),
    bronze=BronzeSpec(
        bronze_table="security_logs_bronze",
        skip_bronze_loading=False
    ),
    silver=SilverSpec(
        # Configure silver layer here, see the API reference for more details
    ),
    gold=GoldSpec(
        # Configure gold layer here, see the API reference for more details
    )
)

# Create the datasource
created_datasource = client.create_datasource(datasource)
print(f"Created datasource: {created.metadata.name}")

Creating a Detection Rule

from dasl_client.types import Rule, Schedule

# Create a new detection rule to detect failed logins
rule = Rule(
    schedule=Schedule(
        at_least_every="2h",
        enabled=True,
    ),
    input=Rule.Input(
        stream=Rule.Input.Stream(
            tables=[
                Rule.Input.Stream.Table(name="http_activity"),
            ],
            filter="disposition = 'Blocked'",
            starting_timestamp=datetime(2025, 7, 8, 16, 47, 30),
        ),
    ),
    output=Rule.Output(
        summary="record was blocked",
    ),
)

try:
    created_rule = client.create_rule("Detect Blocked HTTP Activity", rule)
    print(f"Successfully created rule: {created_rule.metadata.name}")
except Exception as e:
    print(f"Error creating rule: {e}")

Requirements

• Python 3.8+
• Access to a Databricks workspace with DASL enabled
• databricks-sdk>=0.41.0
• pydantic>=2

Documentation

For complete DASL Client documentation, examples, and API reference:

• DASL Client Documentation
• API Reference
• Quickstart Guide

Support

• Email: [e-mail]
• Documentation: DASL Documentation