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Spark running out of memory is not just β€œjob failed.” The interviewer wants to know if you understand what actually happens. Possible outcomes: β€’ executor OOM β†’ executor crashes β€’ task retries on a
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A query working on sample data means almost nothing. Production changes everything. The real reasons queries become painfully slow: β€’ bad execution plan β€’ missing indexes β€’ full table scans β€’ expens
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If you're a Data Engineer in 2026, and still saying: β€œAI will not affect my job” you’re already behind. Not because AI will replace Data Engineers. But because the definition of a strong engineer
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A lot of engineers think: more tools = seniority. That’s not what changes at senior levels. The real shift is: from writing code β†’ to understanding systems. Senior engineers think about: - failure
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The #1 Mistake to Avoid When Starting in Data Engineering This is the mistake I will definitely avoid if I were to start learning data engineering again: I will not start with tools. Neither Spark,
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This is one of the biggest problems in AI agent systems. The demo works perfectly. Then in production: the agent chooses the wrong tool once… and the entire workflow collapses. That’s why production
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A lot of engineers think AI agents = LangChain CrewAI AutoGen frameworks That’s not system design. Good interview answers usually start with: What decision should the agent make? What context does i
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A lot of β€œslow Spark jobs” are actually: bad JOIN problems. One wrong join condition can silently explode: shuffle size partitions memory usage runtime And suddenly: 10 million rows becomes billions.
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One of the biggest mistakes in Spark interviews: People jump directly to: increasing executors increasing memory repartitioning data without understanding WHY the job is slow. That’s not debugging.
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Most people think RAG = embeddings + vector DB + LLM. That works… until it doesn’t. When queries get messy When context is noisy When scale increases β†’ everything starts breaking Real systems don’t
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This is a very common mistake. People think hallucination = model problem. It’s often not. --- In RAG systems, hallucination usually happens because: β€’ wrong context is retrieved β€’ relevant docum
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This is a classic system design thinking question. Most people try to solve it at the application level. But the control is not in the app. It’s in the OS. --- Platforms like Netflix use system-l
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About @codedatahub

Omkar is a Instagram growing creator in the Digital creator category. With 116 followers and 12 posts, codedatahub's account shows a follower-to-following ratio of 4.6:1. View all content anonymously on Pictame - no login required.

codedatahub's engagement rate sits at 13.72%. That's 174% above what you'd typically see for accounts with 1K-10K followers (average 5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 58% are videos and 42% are photos. Notably, videos tend to get 1.5x more engagement than photos. New content goes up pretty much every day β€” a very consistent posting habit. Captions lean detailed, averaging 694 characters.

13.72% engagement rate β€” 174% above average for 1K-10K follower accounts.
58% videos, 42% photos. Videos get 1.5x more engagement here.
Posts every day β€” very consistent.
4.6:1 follower-to-following ratio.

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@codedatahub

Omkar

PublicDigital creator
Posts
12
Followers
116
Top Hashtags
#1#art#anime#amor#beautiful#beach#baby#beauty

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#interiordesign#illustration#home#tiktok#christmas#instapic#photoshoot#igers#artwork#catsofinstagram#architecture
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Data Engineering β€’ AI AI Engineer @ford | ex-Walmart Helping you crack DE interviews SQL β€’ Pipelines β€’ System thinki...

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