Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Added tokei.rs badge #129

Merged
merged 1 commit into from
Feb 28, 2024
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions README.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
# SPL-to-PySpark transpiler

[![codecov](https://codecov.io/gh/databrickslabs/transpiler/branch/main/graph/badge.svg?token=sVMIEvUjvC)](https://codecov.io/gh/databrickslabs/transpiler)
[![lines of code](https://tokei.rs/b1/github/databrickslabs/transpiler)]([https://codecov.io/github/databrickslabs/transpiler](https://github.com/databrickslabs/transpiler))

Cybersecurity practitioners have plenty of ETL or alerting rules coded in Search Processing Language (SPL) to run within some of the industry-standard SIEM environments. In reality, only the most common commands are used the most by SIEM practitioners, and it’s possible to automatically translate them into corresponding PySpark Structured Streaming or, even later - Spark SQL so that we get the same results on the same datasets with the same query from both SIEM and Databricks. It’s also possible to use this tooling to teach PySpark equivalents to SIEM practitioners to accelerate their time-to-comfort level with Databricks Lakehouse foundations.

Expand Down
Loading