Données · GitHub

Design-of-experiment (DOE) generator for science, engineering, and statistics

Web scrapping and related analytics using Python tools

Easy monitoring and analytics for API frameworks.

LBADD: An experimental, distributed SQL database

🔍 智能零代码自动化测试 SQL 语句执行结果的数据库工具，任意增删改查、任意 SQL 模板变量、一键批量生成参数组合、快速构造大量测试数据。🔍 A smart SQL testing automation tool for databases, support any CRUD, any template variables, generating argument combinations, generating lots of data rows.

The PyExperimenter is a tool for the automatic execution of experiments, e.g. for machine learning (ML), capturing corresponding results in a unified manner in a database.

Make sense of it all. Semantic data modeling and analytics with a sprinkle of AI. https://totalhack.github.io/zillion/

Next generation compute platform for the post-modern data stack

• Consistent execution environment everywhere - tower run on Tower serverless or your own compute.
• Deploy in under 30 seconds - tower deploy packages and ships.

Python stream processing for analytics

• Works in real time (eg: reading from Kafka) and replay mode (eg: reading from Parquet files).
• Optimized for analytics, using micro-batches (instead of processing records one by one).

Report Web Vitals to your API with one POST request per session.

An LLM Based Diagnosis System (https://arxiv.org/pdf/2312.01454.pdf)

Run individual controls or full compliance benchmarks for CIS, PCI, NIST, HIPAA and more across all of your AWS accounts using Powerpipe and Steampipe.

View dashboards and reports across all of your AWS accounts using Powerpipe and Steampipe.

Is your AWS perimeter secure? Use Powerpipe and Steampipe to check your AWS accounts for public resources, resources shared with untrusted accounts, insecure network configurations and more.