Email File Management

One of the biggest hurdles in cold email outreach? Getting into the primary inbox. We send cold emails hoping for replies, but the reality is—it’s nearly impossible to get responses if your emails are landing in spam. I’ve faced this issue myself until I realized there are key steps you can’t ignore to boost deliverability. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐡𝐚𝐬 𝐰𝐨𝐫𝐤𝐞𝐝 𝐟𝐨𝐫 𝐦𝐞: ▪️𝐒𝐞𝐜𝐨𝐧𝐝𝐚𝐫𝐲 𝐃𝐨𝐦𝐚𝐢𝐧𝐬 Using secondary domains is crucial for two reasons: → It protects your main domain from being flagged. → It helps distribute the load when sending a high volume of emails. ▪️𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐞𝐭𝐭𝐢𝐧𝐠𝐬 Without setting up proper authentication, you’re likely to be blocked. Make sure you configure: → DMARC → DKIM → SPF → MX These might sound technical, but they’re essential for ensuring your emails land in the right place. ▪️𝐏𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐚𝐧𝐝 𝐖𝐚𝐫𝐦 𝐔𝐩 𝐌𝐚𝐢𝐥𝐛𝐨𝐱𝐞𝐬 Warming up new mailboxes gradually by sending smaller batches is key. Over time, this builds your sending reputation and helps you avoid the spam folder. Since using Maildoso, I’ve seen firsthand how these steps can be simplified. It automates these processes and ensures that my cold email outreach is efficient and impactful. The 99.9% inbox rate means my emails actually get seen, and my outreach efforts are more productive. How are you solving email deliverability challenges in your campaigns? #ColdEmail #EmailMarketing #Deliverability #BusinessGrowth #Maildoso #MarketingStrategy

Data Engineer Interview Scenario!! You are tasked with deduplicating a massive dataset where processing the entire dataset at once is not feasible due to memory constraints. You need to handle it in chunks, ensuring deduplication based on specific columns while keeping one record per duplicate set. How would you approach this in PySpark? Key Interview Flow # 1. Initial Question: Partitioning Logic - How would you process such a huge dataset without loading all the data into memory at once? - Expected Answer: Repartition the dataset by a logical key to split it into manageable chunks. # 2. Code Discussion: Partition-Based Deduplication Present this code to the candidate and ask them to explain each step: ```python chunked_df = df.repartition("key") # Step 1: Repartition by key def process_partition(iterator): partition_data = list(iterator) # Step 2: Collect data in the current chunk if not partition_data: return # Skip empty chunks # Convert the chunk into a DataFrame partition_df = spark.createDataFrame(partition_data, schema=df.schema) # Deduplicate within the chunk using Window function window_spec = Window.partitionBy("key").orderBy(col("date").desc()) deduplicated = partition_df.withColumn("row_number", row_number().over(window_spec)) \ .filter(col("row_number") == 1) \ .drop("row_number") # Simulate saving results for row in deduplicated.collect(): print(row) # Replace with save logic chunked_df.foreachPartition(process_partition) # Step 3: Apply logic to each partition ``` Follow-Up Questions # Partitioning Challenges 1. Why is repartitioning critical in this approach? - Expected Answer: Repartitioning ensures that all duplicate records for a given key are in the same partition, enabling efficient local deduplication. 2. What could go wrong if some keys have significantly more data than others? - Hint: Lead the discussion toward data skew and its impact on memory. 3. How would you handle a situation where a single partition exceeds memory limits? - Expected Answer: Use salting techniques, further chunking, or write intermediate results to disk. # Deduplication Logic 1. What is the role of the `Window` function here? - Expected Answer: It assigns a `row_number` to each duplicate set based on the ordering criteria (`date` in this case). 2. Can `dropDuplicates()` replace the `Window` function here? Why or why not? - Expected Answer: No, because `dropDuplicates()` does not allow control over which duplicate to keep. # Performance/Optimization 1. How does `foreachPartition` improve performance compared to `mapPartitions`? - Expected Answer: `foreachPartition` is designed for side effects (like saving to a database) and avoids unnecessary data collection. 2. What optimizations can you suggest for this approach? - Repartitioning based on expected skew. - Caching intermediate results. - Using efficient file formats like Parquet.

🔍 Level Up Your Email Investigations with Email OSINT Tools! 📧✨ Ever struggled to track down email addresses or verify their authenticity during your investigations? This Email OSINT Cheat Sheet is a treasure trove of powerful tools and tricks to streamline your email-based recon and investigations. Here’s a snapshot of some standout tools: 🚩 The Harvester • Scrape emails and usernames from multiple sources (Google, LinkedIn, Twitter). 🔎 SimplyEmail • Effortlessly gather emails linked to domains with rapid enumeration. 🎯 Holehe • Quickly verify if an email is registered across platforms like Twitter or Instagram. 📌 Quidam & Mailcat • Investigate usernames and identify connected email addresses. 🛠️ MOSINT • Automated email OSINT framework to uncover social media profiles and breaches. ✨ Bonus Tools: Zen, Yopmail & more! Email OSINT is critical for: • Threat hunting and cybersecurity investigations. • Digital forensics and incident response. • OSINT enthusiasts and analysts. 👉 Pro tip: Combine multiple tools for comprehensive recon and cross-verification. Which email OSINT tool do you prefer in your toolkit? Drop your recommendations below! #OSINT #EmailOSINT #CyberSecurity #DigitalForensics #ThreatIntelligence #InfoSec #Recon #SecurityTools #Investigation #BlueTeam #ThreatHunting

A lot of people screw up data enrichment. And not in small ways...in big ways. I've walked into instances where: 😱 Recent sales-entered data was being overwritten by stale enrichment data 😱 Instead of setting up an integration, a massive file was imported all at once, into standard fields, without a data back-up....leaving no audit trail and losing historical data 😱 Enrichment was set up to trigger every time a record was created or updated in Salesforce, creating a situation where only 1,000 records or less could be updated at one time without hitting the Salesforce API limits 😱 Enrichment data wasn't standardized, so each vendor was entering in different formats for fields like employee size So, how do you set it up correctly? Here's what it should look like... Typical steps included: 1. Input Stage: Define the entry points for raw data (e.g., web forms, imports, email captures). 2. Cleaning Stage: Build workflows to: 🔺 Standardize formats (e.g., phone numbers, dates, addresses). 🔺 Correct invalid or missing data (e.g., normalize country names to ISO codes). 🔺 Remove duplicates based on unique identifiers (e.g., email or account ID). 3. Enrichment Stage: 🔺 Match records with external datasets to fill gaps. 🔺 Append metadata (e.g., confidence scores, enrichment source). 4. Output Stage: Push cleaned and enriched data back into your CRM or database. Example washing machine flow: 1. Input: New leads enter from web forms or imports. 2. Cleaning: 🔺 Deduplicate by email or company domain. 🔺 Standardize phone numbers to E.164 format. 🔺 Normalize country names to ISO codes. 3. Enrichment: 🔺 Call Clearbit API to append industry, company size, and LinkedIn URL. 🔺 Validate emails with an email verification tool. 4. Output: Push cleaned and enriched data back to CRM, tagging it with the enrichment source and date. Things to consider: 🔻 Typically, you want to enter enrichment data into separate custom fields. This is duplicative, but if you don't have really strong audit trails and strong enrichment rules, you shouldn't write into a default field because you could cause confusion and frustration with sales, if you overwrite their recently entered data. 🔻 You need to understand all of the fields you're enriching very intimately...what is their purpose, at which stage do they need to be enriched? Don't be lazy and enrich every field at every record edit, it'll harm your systems and speed-to-lead. 🔻 Make sure any enrichment automation takes race conditions into consideration -- what other automations could be triggered, and how would that impact the API limits/system performance? 🔻 How will you monitor results? Set up reports and audit trails, whether through Snowflake or field history in Salesforce. 🔻 Don't forget about consent management fields! Running out of room....what else? Did you find this helpful? #marketing #sales #marketingoperations #revenueoperations

I've audited 500+ cold email setups. 90% of campaigns fail because of 3 fixable issues. The exact audit checklist I use to diagnose what's broken (and how to fix it): Step 1: Check Your Reply Rate - Reply rate under 1% = infrastructure problem. - Reply rate 1-3% but no meetings = weak offer. Most people skip this step and waste weeks tweaking the wrong things. If Reply Rate is Sub-1% (Infrastructure Audit): Campaign Setup: → Spintax added for variability? → Copy free of spam trigger words? → Signature has spintax? → No longer then 2 step sequence? → Adjusting start / end times daily? Technical Infrastructure: → Each domain in its own tenant? → Low inbox count per tenant? → Using Hypertype with Google/Azure correctly? → Sending volume per inbox appropriate? → Domain went through 2-week warmup? → Redirect is working? → DNS records are properly setup? → Warmup didn’t get turned off for sending accounts? Data Quality: → SMTP validation via Million Verifier completed? → Catch-all emails separated and validated via Scrubby? → Company name formatting cleaned up? → Too many enterprise security systems in your list? → Minimum of 2500 leads in the campaign? If Reply Rate is 1-3% (Offer/Copy Audit): Copy Structure: → Subject line looks internal (lowercase preferred)? → Using soft CTA instead of direct meeting ask? → Includes relevant social proof? → Has personalization elements? → Avoids overselling? → No links or attachments? Offer Quality: → Is it "cold traffic ready" with low perceived risk? → Helps prospect make or save money? → Solves a massive pain point? → Includes risk reversal/guarantee elements? Targeting: → Prospects match your ICP exactly? → Company size appropriate for your offer? → Targeting actual decision makers? Red Flags to Fix Immediately: → High bounce rates → Campaigns not sending daily → Long, complex copy → No spintax or variability → Generic, non-personalized messaging → Weak offer positioning I use this exact checklist for every client audit. Takes 20 minutes to run through. Saves weeks of guessing what's broken.

𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐒𝐞𝐫𝐢𝐞𝐬 – 𝐃𝐚𝐲 𝟏𝟐⁣ ⁣ ⁣ 𝐓𝐚𝐬𝐤:⁣ Write a SQL query to identify email addresses that appear more than once in the customers table.⁣ ⁣ This type of question is commonly asked in interviews at companies like PwC, KPMG, and Infosys, especially when the role involves data quality audits, reporting, or data migration tasks. The focus here is on identifying duplicates—an essential skill in data cleaning and preprocessing workflows.⁣ ⁣ ⁣ 𝐇𝐨𝐰 𝐭𝐨 𝐟𝐫𝐚𝐦𝐞 𝐢𝐭:⁣ Start by grouping the table by the email column. Then apply the COUNT(*) function to count how many times each email appears in the dataset. To find duplicates, use a HAVING clause to return only those email groups where the count is greater than one.⁣ ⁣ This logic helps detect data integrity issues such as multiple records with the same email due to failed validations or duplicate imports.⁣ ⁣ ⁣ 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐮𝐬𝐞𝐝:⁣ 1. 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 𝐂𝐥𝐚𝐮𝐬𝐞:⁣ Groups records by email so that aggregation functions can be used to count how many times each unique email appears.⁣ ⁣ 2. 𝐂𝐎𝐔𝐍𝐓(*) 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧:⁣ Counts the total number of records for each grouped email value. If an email appears more than once, its count will be greater than one.⁣ ⁣ 3. 𝐀𝐥𝐢𝐚𝐬𝐢𝐧𝐠 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞𝐬:⁣ The result of COUNT(*) is aliased as occurrences for better readability and downstream usage in reporting or debugging queries.⁣ ⁣ 4. 𝐇𝐀𝐕𝐈𝐍𝐆 𝐂𝐥𝐚𝐮𝐬𝐞 𝐭𝐨 𝐅𝐢𝐥𝐭𝐞𝐫 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞𝐬:⁣ Since WHERE cannot be used with aggregated values, the HAVING clause is applied to filter the grouped data. It returns only those emails with more than one record.⁣ ⁣ 5. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞:⁣ Identifying duplicates is critical in ETL pipelines, CRM data syncs, and compliance checks. Interviewers expect you to write efficient queries that surface these issues clearly.⁣ ⁣ 6. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐅𝐨𝐥𝐥𝐨𝐰-𝐔𝐩 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬:⁣ Once duplicates are identified, interviewers may ask how to remove or resolve them. You can suggest using ROW_NUMBER() to isolate the latest record, or DISTINCT to retain unique values based on business rules.⁣ ⁣ 7. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧:⁣ This pattern is useful in fraud detection, contact deduplication, lead cleanup, or preparing customer data for machine learning models.⁣ ⁣ ⁣ 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐢𝐬 𝐚𝐬𝐤𝐞𝐝:⁣ This question tests your understanding of SQL’s grouping and filtering logic, and how to detect and report anomalies in data. Clean data is the foundation of every analytics project, and the ability to identify duplicates is a must-have skill for any analyst or engineer. Interviewers look for candidates who can think practically and solve messy data problems with precision.⁣ ⁣ ⁣ ⁣ #SQL #SQLInterview #PwCInterview #DataCleaning #HAVINGClause #DataAnalytics #SQLQuery #LearnSQL #DataQuality #BusinessIntelligence #InterviewPreparation #DataEngineering #AnalyticsJobs #SQLTips

𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗘𝗺𝗮𝗶𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗳𝗿𝗼𝗺 $𝟰𝟵𝗞 𝘁𝗼 $𝟯𝟬𝟬𝗞 𝗶𝗻 𝟵𝟬 𝗗𝗮𝘆𝘀 Initial Situation and Challenges: The client was struggling with a stagnant email marketing performance: Open Rates: 7% Click Rates: Less than 0.2% Inbox Placement: Around 60% across major ISPs Spam Rates: Above 0.4% at Gmail, and 0.1% - 0.5% at other ISPs These figures highlighted significant deliverability issues, with a considerable portion of emails not reaching the inbox, affecting engagement and revenue. 𝗦𝘁𝗲𝗽 𝟭: 𝗔𝗻𝗮𝗹𝘆𝘇𝗶𝗻𝗴 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 𝗧𝗼 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝗿𝗼𝗼𝘁 𝗰𝗮𝘂𝘀𝗲𝘀, 𝘄𝗲: 1. Studied Unsubscribes and Soft Bounces: Determined that certain segments and content types had higher unsubscribes and soft bounces. 2. Content Performance Review: Found that concise content (no more than 2 scrolls) with a CTA within the first scroll had higher engagement rates. Actionable Insights: Shorter emails with prominent early CTAs drove better conversions. 𝗦𝘁𝗲𝗽 𝟮: 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 We executed multiple tests to refine content: 1. Layout and Image Alterations: Changed email layouts and image-to-text ratios to see their impact on deliverability. 2. Footer Disclaimers and Content Changes: Tweaked footer disclaimers which led to better inbox placement, especially in Gmail. Results: Improved Gmail inboxing rates and engagement. However, these changes did not significantly impact Yahoo and Hotmail. 𝗦𝘁𝗲𝗽 𝟯: 𝗜𝗦𝗣-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Revenue and Click Analysis by ISP: Discovered Yahoo and Hotmail had better conversion rates than Gmail, indicating higher engagement from these ISPs. 2. Hotmail Focus: Despite low inboxing (45%), Hotmail drove more revenue than Yahoo. We liaised with Microsoft for three weeks to resolve IP blocking issues, doubling the volume sent to Hotmail. 3. Yahoo Adjustments: Improved inboxing to 80% by targeting users who had engaged (opened emails at least 10 times and clicked once) in the last 60 days. 4. Gmail Strategy: Implemented content changes and special segmentation strategies, boosting inboxing to 70% and reducing spam rates below 0.2%. Outcome: ISP-specific strategies led to improved inbox placement and engagement across the board. Step 4: Results and Impact Inboxing Improvements: Gmail: Increased to 70% Yahoo: Improved to 80% Hotmail: Resolved IP issues and doubled volume. Open Rates: Grew to an average of 15% in 90 days Revenue: Increased from $49K to $300K per month within 90 days. Continued in the comment section... #email #emailmarketing

Your email marketing might be broken... and it's not what you think 📧 I was reviewing a very large startup's email strategy recently when something became painfully clear: They were obsessing over subject lines and send times while their entire email foundation was crumbling. Sound familiar? Most businesses treat email marketing like a slot machine - keep pulling the lever and hope for the best. But here's the thing: The SYSTEM matters more than the individual campaigns. Your email performance isn't random. It's the direct result of five critical factors that most marketers completely overlook: 1️⃣ Email architecture - Is your technical foundation solid or full of cracks? 2️⃣ List hygiene - Those "subscribers" from 2018 who never open? They're actively hurting your deliverability. 3️⃣ Acquisition quality - How you BUILD your list matters more than its size. 4️⃣ Journey design - Random blasts ≠ strategic journeys that convert. 5️⃣ Performance tracking - If you can't access your bounce logs and campaign stats, you're flying blind. The real problem? Most businesses jump straight to optimizing content without fixing these foundational issues first. It's like repainting a house with a crumbling foundation. So what's the fix? Start with a complete email system audit. Fix the architecture. Clean your list. Improve acquisition. Map your journeys. And for heaven's sake, get access to your performance data! Only THEN should you worry about that clever subject line. Because the truth is: The best email content in the world won't save a broken email system. Fix the foundation first. Then optimize. Your open rates (and revenue) will thank you.

I burned through $15K perfecting cold email copy. Here's what I learned when I focused on deliverability instead. While I was obsessing over subject lines and CTAs, most of my emails were landing in spam folders. I had killer copy that nobody ever saw. But here's what happened when I fixed the infrastructure piece first… I went from ignored emails to 800,000+ monthly sends at ColdIQ. If your cold emails aren't working, deliverability beats copy every single time. WHY DELIVERABILITY IS EVERYTHING: 1. Perfect copy means nothing in spam. You can have killer targeting, perfect messaging, incredible offers... but if your email lands in spam? Game over. 2. It compounds everything else. Once your domain reputation is down, even your transactional emails start getting flagged and won't get delivered anymore. So how do you make your email in the primary? 1. Protect your main domain. Never send cold emails from your primary domain. We use 70+ secondary domains to keep our brand safe and our main inbox clean. 2. Distribute volume across multiple mailboxes. Set up 140+ mailboxes across those domains. Keep it under 50 sends per day per domain. High volume too early = instant red flag. 3. Get your technical foundation bulletproof. Set up SPF, DKIM, and DMARC authentication. Without proper technical set-up, you're flagged as suspicious by default. 4. Warm up. Send nothing for 2 weeks. Use premium warm-up tools to build trust gradually with ESPs. Ramp slowly to avoid triggering their spam filters. Patience here pays dividends later. 5. Natural variation. Use Spintax or tools like Twain to introduce variations in your messaging. Even small variations help you avoid the repetition triggers that scream "mass email blast" to spam filters. Remember, list quality plus message still matter most. Even with perfect infrastructure, if your list is off and your message is weak, you'll still land in spam. Deliverability gets you to the inbox, but the relevance keeps you there. Monitor everything rigorously. Use tools to track your sender reputation across all ESPs. We check deliverability rates daily (it's that critical). Infrastructure gets you to the inbox, but your targeting plus messaging determines what happens next. I've put together a 7-day GTM crash course that includes our exact setup, authentication templates, and the monitoring systems we use to protect the campaigns of our 70 clients. Reply with "SETUP" if you want access before your next campaign goes live.

Your cold email just went straight to trash. Here's why: Your email infrastructure is failing you, not just your copy. Forget about templates and clever writing tricks. If your infrastructure is broken, it doesn't matter what you say. The technical foundation of your cold email system determines whether your message even gets seen. Most cold emailers obsess over subject lines while ignoring deliverability issues that guarantee inbox failure. Want responses? Focus on: 1. Reputation - Build sender credibility through proper domain setup and warming 2. Deliverability - Implement technical protocols (SPF, DKIM, DMARC) to avoid spam filters 3. Automation - Create systems that scale without triggering provider penalties Bad Example: "I set up a Gmail account yesterday, uploaded 500 contacts, and blasted my brilliant pitch. Why did I get zero responses and my account got suspended?" Better Example: "I registered multiple secondary domains, implemented proper authentication protocols, warmed my sending IP for 30 days with graduated volume, and segmented my list based on engagement metrics. My reply rates hit 12% and my account health remains pristine." Stop obsessing over word choice when your emails aren't even reaching inboxes. Instead, start building proper email infrastructure. If you want help getting more clients or fixing your current cold outreach system - DM me "email"