t04glovern

import logging
import requests
import json
import time
from tabulate import tabulate

# Configure logging
logging.basicConfig(
    level=logging.INFO,  # or logging.DEBUG if you want more verbose output
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'

t04glovern

#!/usr/bin/env python3
#
# pip install python-frontmatter openai
# ./spell-check-post.py content/blog/amazon-eventbridge-slack-notification-on-event/index.mdx
#
# The following file will be created:
# content/blog/amazon-eventbridge-slack-notification-on-event/index.new.mdx
#
# Follow the stream of content:
# tail -f content/blog/amazon-eventbridge-slack-notification-on-event/index.new.mdx

t04glovern

#!/usr/bin/env python3

"""
This script generates sample data, uploads it to an S3 bucket, and creates Iceberg and Athena tables.
It also creates IAM roles and policies for optimization and statistics generation if specified.
There is also an option to create a Firehose delivery stream and insert random records into it.

Install:
    python3 -m venv .venv
    source .venv/bin/activate

t04glovern

#!/usr/bin/env python3

"""
This script reads and processes Puffin files, extracting and printing blob metadata.
It supports reading metadata from the file footer and decompressing the
data using the specified compression codec.

Spec:
    https://iceberg.apache.org/puffin-spec/

t04glovern

AWSTemplateFormatVersion: '2010-09-09'
Description: A CloudFormation template to create IAM role, policy, Step Functions, and CloudWatch event rule and target for table ingestion maintenance vacuum with concurrency control

Parameters:
  WorkgroupName:
    Type: String
    Default: primary
    Description: The name of the Athena Workgroup

  DatabaseName:

t04glovern

provider "aws" {
  region = "us-west-2"
}

resource "aws_s3_bucket" "my_bucket" {
  bucket = "unique-bucket-name"
}

resource "aws_s3_access_point" "my_access_point" {
  name   = "myaccesspoint"

t04glovern

# Create scratch location
cd
mkdir wsl-ubuntu-24.04

# Download WSL image
wget `
  https://cloud-images.ubuntu.com/wsl/noble/current/ubuntu-noble-wsl-amd64-24.04lts.rootfs.tar.gz `
  -O ubuntu-noble-wsl-amd64-24.04lts.rootfs.tar.gz
 
# Import image to new WSL VM

t04glovern

---
- name: Identify reachable hosts
  hosts: all
  connection: local
  gather_facts: false

  tasks:
    - block:
        - name: determine hosts that are reachable
          ansible.builtin.wait_for_connection:

t04glovern

#!/usr/bin/env python3

"""
This script captures images from an RTSP stream at regular intervals and uses
the Claude 3 Haiku model to generate descriptions based on a custom prompt.

Install:
    python3 -m venv .venv
    source .venv/bin/activate
    pip3 install boto3==1.34.62 opencv-python-headless==4.9.0.80

t04glovern

#!/usr/bin/env python3
#
# -- Run other script to create the Iceberg table
#
# pip install boto3
# curl https://gist.githubusercontent.com/t04glovern/04f6f2934353eb1d0fffd487e9b9b6a3/raw \
#    > lets-try-iceberg.py \
#    && chmod +x lets-try-iceberg.py
# ./lets-try-iceberg.py --table lets_try_iceberg
#