Faasera Documentation

Faasera Implementation Guides

This section provides detailed guidance on integrating Faasera with your environment using SDKs, APIs, or cloud-native services.

SDK Integration (Java / PySpark)

Faasera SDKs offer drop-in modules for Java and PySpark environments. These SDKs let you embed Faasera’s profiling, masking, and validation engines directly into your Spark pipelines or Java applications.

Key Features

Built-in profiling and masking functions
Support for structured and semi-structured data
Works with Snowflake, Databricks, Apache Spark, Hadoop
Deterministic and format-preserving masking options
On-the-fly policy generation using metadata

See full guide: implementation-sdk.md (coming soon)

API Integration

Faasera’s REST APIs provide granular access to each platform component, enabling integration with any system that can send HTTP requests.

Key Use Cases

Trigger profiling or masking tasks
Upload policies or schema definitions
Fetch audit logs and reports
Orchestrate pipelines via Faasera Orchestrator APIs

Authentication is handled via JWT tokens with fine-grained scope permissions.

See full guide: implementation-api.md (coming soon)

Cloud Functions

Deploy Faasera as event-driven serverless functions using:

AWS Lambda
Azure Functions
Google Cloud Functions

These functions can be triggered via API Gateway, message queues, or event buses.

Example Use Cases

Real-time masking on data ingestion
Validate records in motion
Apply profiling logic inside ETL streams

Faasera’s cloud-native footprint makes it ideal for embedding compliance logic with zero operational overhead.

See full guide: implementation-cloud.md (coming soon)