Swateek Jena swateek

CSV to ICS Converter

This script converts a CSV file containing event details into an ICS file that can be imported into Google Calendar (and other calendar applications).

Usage

Prepare your CSV file: The CSV file should have the following columns (case-insensitive):
- start date (Format: YYYY-MM-DD HH:MM or YYYY-MM-DD for all-day)

end date (Format: YYYY-MM-DD HH:MM or YYYY-MM-DD)

USE information_schema;

DESC MV_DATA_DISK_USAGE;

SELECT * FROM MV_DATA_DISK_USAGE;

SELECT DATABASE_NAME, FORMAT(SUM(`BLOBS_B`) / 1024/1024, 0) MB
FROM MV_DATA_DISK_USAGE
GROUP BY DATABASE_NAME

jq

Print all of the JSON content in a key value pair, like in an environment file.

echo '{"key1":"value1", "key2": "value2", "key3": "value3"}'\
| jq -r 'to_entries|map("\(.key)=\(.value|tostring)")|.[]'

echo '{"key1":"value1", "key2": "value2", "key3": "value3"}'\
| jq -r 'to_entries[] | "\(.key)=\(.value | tostring)"'

	#!/usr/bin/env bash
	# =============================================================================
	# fetch_aws_mfa_creds.sh
	# Fetches temporary AWS session credentials using an MFA token and stores
	# them under a dedicated [mfa] profile in ~/.aws/credentials.
	#
	# Usage:
	# ./fetch_aws_mfa_creds.sh <6-digit MFA token>
	#
	# Prerequisites:

	#!/bin/bash

	CYAN='\033[0;36m'
	GREEN='\033[0;32m'
	YELLOW='\033[1;33m'
	RED='\033[0;31m'
	BOLD='\033[1m'
	NC='\033[0m'

	# Use $HOME by default, or override with CLEANUP_HOME env var

	import json

	# Path to your HAR file
	har_file = "example.har"

	# Load HAR file
	with open(har_file, "r", encoding="utf-8") as f:
	har_data = json.load(f)

	# HAR files store entries under log.entries

	#!/usr/bin/env bash
	set -euo pipefail

	AWS_PROFILE="devops"

	function hosted_zone_exists() {
	local domain=$1
	aws route53 list-hosted-zones-by-name \
	--dns-name "$domain" \
	--query "HostedZones[?Name=='${domain}.'] \| length(@)" \

	import { DBSQLClient } from "@databricks/sql";
	import IDBSQLSession from "@databricks/sql/dist/contracts/IDBSQLSession";

	interface DataBricksWareHouseConnect {
	token: string;
	host: string;
	path: string;
	}

	class DataBricksManager {

	from databricks import sql

	DATABRICKS_USE2_PAT = ""

	use2_workspace = sql.connect(
	server_hostname="<YOUR-SERVER-HOSTNAME>",
	http_path="<YOUR-HTTP-PATH>",
	access_token=DATABRICKS_USE2_PAT,
	catalog="<YOUR-CATALOG-NAME>",
	schema="<YOUR-SCHEMA-NAME>"

	bedrock_agent:
	role: >
	Bedrock AI Assistant
	goal: >
	Help users generate responses using AWS Bedrock's LLM.
	backstory: >
	You are an advanced AI assistant powered by AWS Bedrock. You excel at processing
	and generating meaningful insights from textual data.
	tools:
	- bedrock_tool