Kamal Sharma

Kamal SharmaSep 15, 2026

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝘁𝗵𝗲𝘀𝗲 𝟮𝟱 𝗗𝗦𝗔 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝘆𝗼𝘂'𝗹𝗹 𝘀𝗼𝗹𝘃𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀.   Most people prepare DSA by solving more questions, completing more sheets, and memorizing solutions. It feels like progress, but interviews rarely reward memorization.   Because interviewers don't test how many problems you've solved. They test how quickly you recognize the underlying pattern.   For example, Two Sum relies on hashing, Product of Array Except Self follows the prefix-suffix pattern, and Subarray Sum Equals K combines prefix sums with a HashMap. The problems look different, but the thinking behind them is connected.   Similarly, Merge Intervals is built on sorting and greedy thinking, while Rotate Array is simply an array reversal pattern.   That difference is not practice. It is pattern recognition.   Strong candidates don't start by searching for the solution. They identify the pattern first and then adapt it to the problem.   If you look closely, most DSA interview questions map to a small set of patterns:   • Hashing • Two Pointers • Sliding Window • Prefix Sum • Greedy • Binary Search • Dynamic Programming • Graphs & Trees   Interviews simply combine these patterns in different ways. That's why questions feel new, but the logic is not.   This is where most candidates struggle. They try to solve more problems instead of mastering the patterns that keep repeating.   You don't need 500 random questions. You need a focused set that builds pattern recognition and problem-solving intuition.   Most coding interviews are just combinations of these patterns.   —   Which DSA pattern do you find hardest to recognize?  For free Mentorship :- https://lnkd.in/gSbBdZ2z Save this before your next coding interview.   📲 Daily tech insights + verified job opportunities:   WhatsApp: https://lnkd.in/gdW2Ypqd Telegram: https://lnkd.in/ghHEj9wF Follow Kamal Sharma for interview-focused coding content that actually works. #DSA #LeetCode #CodingInterviews #Python #SoftwareEngineering

Kamal SharmaSep 16, 2026

𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. 𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗶𝗰𝗵 𝘁𝗼𝗼𝗹 𝘁𝗼 𝘂𝘀𝗲.   Someone says, “I’m not good at coding.”   Another person opens Cursor + Claude Code, builds the first version and keeps moving.   That’s the real advantage AI is creating.   AI doesn’t magically give you the skill.   𝗜𝘁 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝘁𝗵𝗲 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗵𝗮𝘃𝗶𝗻𝗴 𝗮𝗻 𝗶𝗱𝗲𝗮 𝗮𝗻𝗱 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗶𝘁.   The mistake is trying to learn every AI tool.   Instead, start with the problem you’re trying to solve:   → Can’t code? → Cursor + Claude Code → Can’t edit videos? → Runway + CapCut AI → Can’t research faster? → Perplexity + Genspark → Can’t build an app? → Lovable + Bolt → Can’t design UI? → Figma AI + Uizard → Can’t build AI agents? → n8n + Flowise → Can’t brainstorm? → ChatGPT + Gemini → Can’t automate work? → Zapier + Make → Can’t create presentations? → Gamma + Canva AI → Can’t grow on LinkedIn? → Taplio + AuthoredUp → Can’t learn faster? → NotebookLM + ChatGPT → Can’t build an online business? → Shopify Magic + Durable AI   The goal isn’t to collect 100 AI tools.   𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝗶𝘀 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗵𝗮𝘁 𝗵𝗲𝗹𝗽𝘀 𝘆𝗼𝘂 𝗺𝗼𝘃𝗲 𝗳𝗮𝘀𝘁𝗲𝗿.   So stop asking:   “Which AI tool should I learn?”   Start asking:   𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗮𝗺 𝗜 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲?   Then find the right tool, build a workflow and improve it every week.   𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗲𝗳𝗳𝗼𝗿𝘁. 𝗕𝘂𝘁 𝗶𝘁 𝘄𝗶𝗹𝗹 𝗿𝗲𝘄𝗮𝗿𝗱 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗺𝗼𝘃𝗲, 𝘁𝗲𝘀𝘁 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝗱 𝗳𝗮𝘀𝘁𝗲𝗿.   📌 Save this list. You’ll probably need it the next time you’re stuck.   Follow Kamal Sharma for more AI tools, tech insights and practical resources.   Image credits :- Rathnakumar Udayakumar #AI #AITools #ArtificialIntelligence #Productivity #Tech #AITips #FutureOfWork #Automation

Kamal SharmaJul 12, 2026

𝐉𝐎𝐈𝐍𝐬 𝐥𝐨𝐨𝐤 𝐬𝐢𝐦𝐩𝐥𝐞 𝐢𝐧 𝐒𝐐𝐋. 𝐁𝐮𝐭 𝐢𝐧 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬, 𝐭𝐡𝐞𝐲 𝐚𝐫𝐞 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐫𝐞𝐚𝐬𝐨𝐧𝐬 𝐜𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞𝐬 𝐠𝐞𝐭 𝐫𝐞𝐣𝐞𝐜𝐭𝐞𝐝. After reviewing multiple Data Engineering interviews, one pattern becomes very clear. Most candidates can write JOIN queries, but very few actually understand what those joins are doing to the data. At the surface level, writing an INNER JOIN or LEFT JOIN feels straightforward. But interviews don’t test whether you can write syntax. They test whether you understand relationships, edge cases, and the impact of your decisions. That difference is not SQL knowledge. It is data thinking. Two candidates can solve the same problem, but the outcome changes based on one decision. Using an INNER JOIN might give correct-looking results while silently dropping important data. Using a LEFT JOIN with proper handling can reveal missing records, edge cases, and actual business insights. This is where most candidates get filtered out. They write queries that “work,” but miss what the data is actually saying. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐡𝐢𝐟𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬. JOINs are not just about combining tables. They are about understanding how data connects, what gets excluded, and what that means for decision-making. Because in real-world systems, missing data is often more important than visible data. And strong Data Engineers are not just writing queries. They are uncovering what others miss. 𝐈𝐟 𝐲𝐨𝐮𝐫 𝐉𝐎𝐈𝐍𝐬 𝐚𝐫𝐞 𝐨𝐧𝐥𝐲 𝐫𝐞𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐫𝐞𝐬𝐮𝐥𝐭𝐬, 𝐚𝐫𝐞 𝐭𝐡𝐞𝐲 𝐚𝐥𝐬𝐨 𝐫𝐞𝐯𝐞𝐚𝐥𝐢𝐧𝐠 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬? Comment “JOIN” and I’ll share the SQL cheat sheet with you. Follow Kamal Sharma for more content like this. #DataEngineering #SQL #SQLInterview #TechCareers #Analytics