dheerajinampudi

# Full-Text Search Example
import re
text = "Exploring the universe of AI and ML."
search_term = "universe"
result = re.findall(search_term, text)
print(result)

# Semantic Search Example (using spaCy)
import spacy
nlp = spacy.load("en_core_web_sm")

dheerajinampudi

# %%
import pandas as pd
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
import configparser

# %%
config = configparser.ConfigParser()

dheerajinampudi

# conda environment commands

# CREATE 
conda create --name {env_name}

# LIST
conda list environments

# ACTIVATE
conda activate {env_name}

dheerajinampudi

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

---

General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules

dheerajinampudi

def get_bucket_size_from_cloudwatch(bucket_name, region_name):
    cloudwatch = boto3.client('cloudwatch', region_name=region_name)
    response = cloudwatch.get_metric_statistics(
        Namespace='AWS/S3',
        MetricName='BucketSizeBytes',
        Dimensions=[{'Name': 'BucketName', 'Value': bucket_name}, {'Name': 'StorageType', 'Value': 'StandardStorage'}],
        StartTime=datetime.utcnow() - timedelta(days=30),
        EndTime=datetime.utcnow(),
        Period=86400,
        Statistics=['Average']

dheerajinampudi

from boto3.dynamodb.types import TypeDeserializer, TypeSerializer


def unmarshall(dynamo_obj: dict) -> dict:
    """Convert a DynamoDB dict into a standard dict."""
    deserializer = TypeDeserializer()
    return {k: deserializer.deserialize(v) for k, v in dynamo_obj.items()}


def marshall(python_obj: dict) -> dict:

dheerajinampudi

#%%
import boto3
client = boto3.client('dynamodb')
table_name  = ''

#%%
# INSERT INTO -- CREATE A RECORD WITH Multiple Values
#example args
primary_key = ''
secondary_key = ''

dheerajinampudi

import boto3
import os
key_id = ''
secret_key = ''
region = ''
bucket_name = ''
def upload_files(path,bucket_name):
    session = boto3.Session(
        aws_access_key_id=key_id,
        aws_secret_access_key=secret_key,

dheerajinampudi

S3 Website endpoint,Diff,S3 REST API endpoint and Example
Step 1: Register a custom domain with Route 53,✅,e.g. example.com
Step 2: Create two buckets,✅,"e.g. First Bucket: example.com
Second Bucket: www.example.com"
Step 3: Configure your root domain bucket for website hosting,❌,"Not required. Instead provide rest api endpoint as Origin Domain Name 
Refer Cloudfront Origin Settings"
Step 4: Configure your subdomain bucket for website redirect,✅,www.example.com → website hosting → redirection → https://example.com
Step 5: Configure logging for website traffic,✅,Refer Cloudfront Origin Settings
Step 6: Upload index and website content,✅,Upload website content.
"Step 7: Upload an error document

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