Automic Continuous Delivery Automation vs SDSA
Automic Continuous Delivery Automation
broadcom.comSDSA
github.comSide by side
| Figure | Automic Continuous Delivery Automation | SDSA |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Data Masking Software | 8.4 (#4) | 6.9 (#18) |
| Intended users | The product brief describes it as optimized for enterprise environments and intended to empower development and operations. | The maker describes SDSA for enterprise data-sharing workflows such as testing, QA, analytics, and external processing, with explicit policies and reviewable reports. |
| Interfaces | Development, QA, and operations teams can access prebuilt functionality through a UI, API, or web services. | Users can process data through a browser UI, REST API, or headless command-line pipeline for CI/CD and data pipelines. |
| Purpose | It is an application release orchestration tool for automating continuous delivery and deployment across enterprise IT. | SDSA is a self-hostable web app for sanitizing tabular data before it leaves a trusted environment. |
Plans
Automic Continuous Delivery Automation
- Automic Continuous Delivery AutomationPriced on request
In their own words
Automic Continuous Delivery Automation
Automic Continuous Delivery Automation has no write-up on Specifiction yet; its figures are above.
SDSA
SDSA is a free, AGPL-3.0 self-hostable application for sanitizing tabular data before it leaves a trusted environment. It accepts CSV, delimited TXT, single-table SQL INSERT dumps, JSON records, and XML records through a browser interface or REST API. SDSA infers schemas and flags likely sensitive fields such as emails, phone numbers, card numbers, government IDs, dates of birth, names, and addresses. Transformations include masking, HMAC hashing, tokenization, redaction, dropping, numeric binning, date or string truncation, and bounded Laplace noise. Users can enforce k-anonymity for selected quasi-identifiers and measure or enforce l-diversity for sensitive cleartext attributes. The app exports sanitized CSV plus JSON and Markdown privacy reports with utility summaries. A headless command-line pipeline supports CI/CD and data pipelines. Sessions use in-memory storage for a default 30 minutes. Docker and Python installation are documented.