#Dag In Data Engineering

End-to-End Data Engineering Pipeline (Simple View) Ever wondered how raw data becomes business insights? 🤔 Here’s the real flow every Data Engineer works with 👇 📥 Source → 🚚 Ingestion → 🪣 Storage → 🔄 Transformation → 🏢 Warehouse → 📊 BI → 💡 Insights 💡 This is the backbone of every data-driven company! ✨ If you're starting your journey, understand THIS first — everything builds on top of it. 👇 Follow for more simple Data Engineering concepts #DataEngineering #DataPipeline #BeginnerDataEngineer #DataEngineerRoadmap #ETLProcess LearnDataEngineering PythonForData SQLLearning Snowflake ApacheAirflow BigData TechCareer WomenInTech DataAnalytics CareerGrowth

Confused between becoming a Data Scientist or an AI Engineer? Both roles are powerful—but require different skills, tools, and thinking. Comment “Roles” and I’ll send you a detailed roadmap for both 🚀 Got questions or feeling stuck? Drop your doubts in the comments—I’ll personally help you get clarity and move forward on your journey. #datascientist #datascience #ai #aiengineer #careergrowth

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This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year. 1️⃣ Learn SQL SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database. It’s also a very easy coding language to learn, so I would start there. Use Interview Master to learn and practice SQL (link in bio): → Learn SQL: www.interviewmaster.ai/content/sql → Practice SQL: www.interviewmaster.ai/home 2️⃣ Start building Product Sense & Business Sense Product sense & business sense basically means you know how to use Data to solve real problems. I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world. I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job. 3️⃣ Learn Statistics How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well. → Descriptive statistics → Common distributions → Probability and Bayes’ Theorem → Basic Machine Learning models → Experimentation concepts → A/B experiment design Check out Stanford’s Introduction to Statistics, which is free on Coursera. 4️⃣ Learn Python Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3. I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio). 5️⃣ Use AI-assisted coding tools Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity. And also many companies are evaluating you on your use of AI during interviews. #datascience #datascientist

After working as a data engineer, here are 5 things I wish I knew earlier: 1. It’s not just SQL or Python Data engineering isn’t about syntax It’s about moving data reliably between systems and transforming it correctly along the way 2. Testing data is surprisingly hard Testing backend code is straightforward → input vs expected output In data engineering → massive datasets, multiple columns, edge cases… validating correctness is a real challenge 3. It gets harder as you grow Junior role → write SQL / PySpark pipelines. Senior role → design architecture, ensure data governance, manage scalability, reliability, and costs. 4. “Pipelines once built are done” — wrong Data pipelines break. Schemas change. Upstream systems fail. Maintenance and monitoring are ongoing responsibilities, not one-time work. 5. “More tools = better engineer” — myth Knowing 10 tools doesn’t matter. Understanding fundamentals (data modeling, distributed systems, trade-offs) is what actually scales your career. If you focus only on coding, you’ll plateau early. If you understand data systems, you’ll grow fast. 💾 Save this for when the role starts feeling more complex than expected 💬 Comment if you’ve felt this shift already 🔁 Follow to keep your thinking sharp as you grow in data engineering

🚀 Day 1: Noob to Pro Data Engineer 🚀 Started my journey today! 🔥 Learned about Apache Spark and how it helps solve the 3V problem (Volume, Velocity, Variety). Also compared Hadoop vs. Spark—turns out Spark is way faster! ⚡ 💡 Key Takeaways: ✅ Spark processes data in-memory, making it much faster than Hadoop. ✅ Hadoop is great for batch processing, but Spark shines in real-time analytics. ✅ Practiced SQL on LeetCode & started working on my Azure Data Engineering project. [Azure, cloud, learn, study, hardwork, consistency, hustle, motivation, job, employment, Microsoft azure, hadoop, dpark, daily vlog, daily study, unemployment, mnc, jio, corporate]

Comment roadmap to get sent my free and complete data engineering roadmap!

Data engineers make data work. Here’s your roadmap to become a data engineer. Learn, build, and grow with Codementor 🫶

High demand of Data Engineers in 2026🚀 Comment "Link" for the details🚀 (Data engineering, Data analyst, Data science, DevOps, Software engineering, Data analytics, job switch, data engineering roadmap, data engineering roadmap for 2026) #dataengineering #devops #softwareengineering #explorepage #dataengineer

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