As it is the beginning of the new year and our mail inboxes are filled with graduate students’ emails for summer internship applications. But most of the time we received this kind of emails:

This shows that the applicant has not spent a single minute to research about the company and did not even bother to address the name or greetings. So, definitely, the question which you have asked is very much relevant for an internship applicant to – How to apply for an Internship? So, here are few suggestions from my end:

• Never send blind emails in BULK.
• Target few companies and write personalized emails.
• Address the right person in the company. (Contact with someone in the company and ask them for the right person).
• Keep the subject line and introduction short.
• Research about the company’s engineering and stack’s they use (Check out their BLOG section to get the idea).
• Highlight those stacks in your application. (Be mindful – company is not interested in what you know, they are interested in what you know that they can use).
• You can emphasize on how you’d be a good addition to the team.
• Have an online portfolio (LinkedIN profile – You can highlight all your project works in the portfolio)
• Prepare a CV that highlights your strengths and modify it for the target company accordingly and keep your resume short. (Officials usually don’t spend more than 30 seconds to go through any resume.)

To all internship aspirants, next time you apply for an internship, not only for software companies in Chennai or Bangalore? – it’s for every software company, use this post as a checklist. All the best!

Internship Help:- Our company (Cotocus) also accept internship for 3 months and 6 months. If you want to apply for this – you can connect to Biraj Kumar (L & D Manager – +91 9686829970) or you can drop an email at career@cotocus.com

Global Personal Cloud market research report is a highly intelligent reference guide that is equipped with meticulous research output to derive accurate and optimum understanding on market growth synopsis in terms of value and volume. The report is a top-down assessment of all major touchpoints on the market and includes an elaborate vendor profile comprising emerging competitors as well as dominant market players aiming for lucrative break in the competition domain. Internationally acknowledged research practices such as SWOT analysis and PESTEL analysis have also been roped in the report with relevant details on revenue generation trends as well as technological innovations witnessing ample investments.

Competitor Profiling: Global Personal Cloud Market

Apple
Microsoft
Google
Amazon Web Services
Seagate
Dropbox
Egnyte
Buffalo Technology
Sugarsync
Box
In the competition spectrum section of this report, readers are offered decisive understanding on frontline players and the overall competition intensity concerning the market has also been highly discussed in the report. Details pertaining to market share, revenue generation tendencies, product innovations as well as corporate collaborations such as M&A developments have been highly discussed in the report.

The report is also highlighting versatile details on the impact of COVID-19 implications on growth progression. Owing to devastating outcome of the global pandemic, this research report has incorporated pandemic management details in the report as well to encourage appropriate business decisions in global Personal Cloud market.

This report on global Personal Cloud market also extends versatile details on various elements such as product type and applications. Additional details on geographical expanse and regional diversification have also been highlighted in the report. With these information attributes, inquisitive market participants can derive logical insights, crucial to deliver growth specific investment decisions.

Analysis by Type: This section of the report includes factual details pertaining to the most lucrative segment harnessing revenue maximization.

Direct Revenues
Indirect Revenues
Analysis by Application: Further in the subsequent sections of the report, research analysts have rendered precise judgement regarding the various applications that the Personal Cloud market mediates for superlative end-user benefits.

Individual
Small Enterprises
Medium Enterprises

Global Personal Cloud Market Report Indicators: Points to Consider
 Competition Analysis: A critical analytical assessment of frontline players and a detailed overview of the entire competition gamut has been one of the prime focal points of this research study. A critical evaluation inclusive also of a detailed SWOT analysis of the mentioned players has been included in the report for the superlative business outcomes. The Global Personal Cloud Market report includes detailed assessment of revenue generation trends, capacity milestones, production and consumption patterns as well as gross margin and sales developments have been intricately discussed in this report for high end returns.

 Regional and Global Market Outlook: The report in its subsequent sections also encompasses veritable insights on region specific market performance. Further details in the report shed light on prominent growth hubs as well as vendor manufacturing and promotional activities across countries and regions that influence customer response. Details about import and export performance as well as production and consumption trends have also been discussed at length in the report.

 Market Segmentation: The report in its subsequent sections also elaborates on various segment-wise performance in catapulting high revenue growth. Each of the segment specified in the report allows readers to identify the highest potential market segment triggering revenue maximization.

Why Crawl Budget Matters for Large Sites

Crawl budget inefficiencies can quietly undermine organic search performance: if Googlebot never crawls a new or updated page, that page cannot be indexed, and it will not rank. For most small sites this is a non-issue, but high-volume ecommerce and publishing sites — with tens or hundreds of thousands of URLs — can run into real problems.

Google’s Webmaster Central Blog post “What Crawl Budget Means for Googlebot” defines crawl budget as the combination of two factors:

• Crawl rate — how fast Googlebot can fetch pages without overloading the server.
• Crawl demand — how much Google actually wants to crawl a given URL based on its perceived importance and freshness.

Google states that sites with fewer than a few thousand pages are unlikely to notice crawl budget constraints. However, ecommerce sites routinely exceed that threshold, making crawl budget analysis a worthwhile periodic exercise.

The Goal: Find Pages That Should Be Crawled But Aren’t

The analysis described here aims to answer two questions:

1. Are there pages in the XML sitemap that Googlebot has never crawled?
2. How quickly does Googlebot pick up page modifications after the sitemap is updated?

To answer both questions, you need two data sources: your XML sitemaps (which represent what should be crawled) and your web server log files (which record what was actually crawled). Comparing the two surfaces gaps.

Step 1 — Isolating Real Googlebot Requests in Server Logs

Server logs contain requests from many different bots — Bingbot, various SEO crawlers, and tools that spoof the Googlebot user-agent string. Before drawing any conclusions, you must filter down to verified Googlebot traffic only.

Screaming Frog’s Log File Analyser makes this straightforward:

1. Open Log File Analyser and drag your log file(s) into the “Drag and Drop Log Files Here” zone.
2. In the Project tab, go to New > User Agents.
3. Tick “Verify Bots When Importing Logs (Slows down Import)”. This performs a double-reverse DNS lookup against Google’s published IP ranges, confirming each request genuinely originated from Google’s infrastructure — the method Google describes in its Search Console help article “Verifying Googlebot.”

Bot verification can dramatically reduce your dataset. In the Practical Ecommerce example, enabling verification dropped the Googlebot request count from 306,960 to 112,308 — meaning roughly half of the apparent Googlebot requests were spoofed or tool-generated.

Exporting a Clean CSV from Log File Analyser

Once Log File Analyser has finished processing:

1. Set Verification Status to Show Verified to exclude all unverified bot entries.
2. Confirm that the log timestamps are correctly parsed — set your local timezone in the project settings if needed.
3. Export to CSV. This file will be your crawl-activity dataset for comparison.

Step 2 — Extracting URLs and Modification Dates from XML Sitemaps

Your XML sitemaps represent the set of URLs you want Googlebot to crawl and index. For this analysis to be meaningful, two assumptions must hold:

• The sitemaps are comprehensive and contain only canonical, indexable URLs.
• The <lastmod> dates in the sitemap entries are accurate and updated whenever content changes.

Screaming Frog’s SEO Spider can download sitemap URLs but discards the <lastmod> timestamp — which is essential for answering question two. Use Python instead.

Parsing Sitemaps to a Pandas DataFrame with Python

The following approach expands an XML sitemap index, fetches each child sitemap, and loads all URLs plus their modification dates into a Pandas DataFrame — a tabular data structure well-suited to the join operations that follow.

import requests
import pandas as pd
from xml.etree import ElementTree as ET

SITEMAP_NS = '{http://www.sitemaps.org/schemas/sitemap/0.9}'

def fetch_sitemap(url):
    """Fetch an XML sitemap or sitemap index and return a DataFrame of (loc, lastmod)."""
    response = requests.get(url, timeout=30)
    response.raise_for_status()
    root = ET.fromstring(response.content)

    records = []

    # Sitemap index — recurse into child sitemaps
    for sitemap_tag in root.findall(f'{SITEMAP_NS}sitemap'):
        child_url = sitemap_tag.findtext(f'{SITEMAP_NS}loc')
        if child_url:
            records.extend(fetch_sitemap(child_url).to_dict('records'))

    # Standard sitemap — collect URL entries
    for url_tag in root.findall(f'{SITEMAP_NS}url'):
        loc     = url_tag.findtext(f'{SITEMAP_NS}loc', default='')
        lastmod = url_tag.findtext(f'{SITEMAP_NS}lastmod', default='')
        records.append({'url': loc, 'lastmod': lastmod})

    return pd.DataFrame(records)

# Replace with your sitemap index URL
sitemap_df = fetch_sitemap('https://www.example.com/sitemap_index.xml')
sitemap_df['lastmod'] = pd.to_datetime(sitemap_df['lastmod'], errors='coerce')
sitemap_df.to_csv('sitemap_urls.csv', index=False)
print(sitemap_df.head())

Export sitemap_urls.csv and import it into Log File Analyser under File > Import URLs.

Step 3 — Identifying Uncrawled URLs

With the sitemap URLs loaded into Log File Analyser alongside your verified Googlebot log data:

1. Navigate to the URLs tab.
2. Select “Not in Log File” from the filter dropdown.

The resulting list shows every URL that exists in your sitemap but received zero verified Googlebot visits during the log period. These are your highest-priority pages to investigate: Googlebot is either unaware of them, deprioritising them, or blocked from reaching them.

Common causes for URLs appearing here include:

• Pages blocked by robots.txt or a noindex directive contradicting the sitemap entry.
• Pages with very low PageRank receiving low crawl demand.
• Crawl budget consumed by low-value URLs (parameter duplicates, thin paginated pages, etc.) before Googlebot reaches these pages.
• Sitemap errors that prevent Googlebot from discovering the URLs at all.

Step 4 — Measuring How Quickly Googlebot Picks Up Changes

Log File Analyser does not directly support comparing <lastmod> dates from sitemaps to crawl timestamps. Return to Python for this step.

Load the CSV export from Log File Analyser into a second DataFrame, then merge the two on the URL column using a left join. A left join retains every sitemap URL (including those never crawled) while capturing crawl timestamps for those that were.

import pandas as pd

# Sitemap DataFrame from Step 2
sitemap_df = pd.read_csv('sitemap_urls.csv', parse_dates=['lastmod'])

# Log File Analyser CSV export
# Adjust column names to match the actual export headers from your version of the tool
log_df = pd.read_csv('log_file_analyser_export.csv', parse_dates=['Date'])
log_df = log_df.rename(columns={'Address': 'url', 'Date': 'crawl_date'})

# Keep only the most recent crawl per URL
latest_crawl = log_df.groupby('url', as_index=False)['crawl_date'].max()

# Left join: keep all sitemap URLs, attach the latest crawl date where available
merged_df = sitemap_df.merge(latest_crawl, on='url', how='left')

# Calculate lag between last modification and most recent crawl
merged_df['days_to_crawl'] = (
    merged_df['crawl_date'] - merged_df['lastmod']
).dt.days

# Surface pages modified but not yet re-crawled
uncrawled_updates = merged_df[
    merged_df['crawl_date'].isna() | (merged_df['crawl_date'] < merged_df['lastmod'])
]

print(f"Pages modified but not re-crawled: {len(uncrawled_updates)}")
uncrawled_updates.to_csv('uncrawled_updates.csv', index=False)

The output CSV gives you:

• Every URL whose lastmod date is more recent than the most recent Googlebot crawl — pages updated since they were last crawled.
• Every URL with no crawl record at all.
• A days_to_crawl column showing the lag for pages that were eventually re-crawled.

Interpreting and Acting on the Results

A large list of uncrawled or stale URLs does not by itself confirm a crawl budget problem — it could mean your log retention window is too short, or that the URLs were added very recently. Cross-reference the following before drawing conclusions:

• Log period length: Googlebot may not crawl every page every day. A 30-day log window gives a more reliable picture than a 7-day window.
• Google Search Console Coverage report: Corroborate uncrawled URLs against the “Discovered – currently not indexed” and “Crawled – currently not indexed” buckets.
• robots.txt and meta robots: Verify that no directives are accidentally blocking the uncrawled pages.
• Low-value URL waste: Use the log file to find URL patterns Googlebot is spending time on that deliver little value — infinite scroll parameters, session IDs, filter combinations — and block or consolidate them to reclaim budget.

Summary

Diagnosing Googlebot crawl budget problems requires comparing intent (XML sitemaps) against reality (server logs). The workflow is:

1. Import and verify server logs in Screaming Frog Log File Analyser, filtering to confirmed Googlebot IPs only.
2. Parse XML sitemaps — including <lastmod> dates — into a structured CSV using Python and Pandas.
3. Use Log File Analyser’s “Not in Log File” filter to surface URLs that have never been crawled.
4. Use a Pandas left join to measure the lag between content updates and Googlebot re-crawls.
5. Prioritise fixing blocking issues, improve internal linking to low-crawl pages, and remove or consolidate low-value URLs wasting crawl budget.

For further context on using server logs for SEO diagnostics, search for Google’s Webmaster Central Blog post “What Crawl Budget Means for Googlebot” and the Search Console help article “Verifying Googlebot.”