Data Engineers
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Free Data Engineering Ebooks & Courses
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Tarmoq:📈 Telegram kanali Data Engineers analitikasi
Data Engineers (@sql_engineer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 10 884 obunachidan iborat bo'lib, Taʼlim toifasida 18 004-o'rinni va Hindiston mintaqasida 35 805-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 10 884 obunachiga ega bo‘ldi.
26 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 277 ga, so‘nggi 24 soatda esa 8 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 11.06% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.83% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 204 marta ko‘riladi; birinchi sutkada odatda 308 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent sql, learning, analytic, engineer, link:- kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Free Data Engineering Ebooks & Courses”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Obunachilar
+824 soatlar
+357 kunlar
+27730 kunlar
Postlar arxiv
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Stop obsessing over Python and SQL skills.
Here are 5 non-technical skills that make exceptional data analysts:
- Business Acumen
Understand the industry you're in. Know your company's goals, challenges, and KPIs. Your analyses should drive business decisions, not just process data.
- Storytelling
Data without context is just noise. Learn to craft compelling narratives around your insights. Use analogies, visuals, and clear language to make complex data accessible.
- Stakeholder Management
Navigate office politics and build relationships. Know how to manage expectations, handle difficult personalities, and align your work with stakeholders' priorities.
- Problem-Solving
Develop ability for identifying the real problem behind the data request. Often, the question asked isn’t the one that truly needs solving. It’s your job as a data analyst to dig deeper, challenge assumptions, and uncover the actual business challenge.
Technical skills may get you started, but it’s the soft skills that truly advance your career. These are the skills that turn a good analyst into an essential part of the team.
The best data analysts aren't just number crunchers - they guide the strategy that drives the business forward.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
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Greetings from PVR Cloud Tech!! 🌈
🚀 Kickstart Your Career in Azure Data Engineering – The Smart Way in 2025!
📌 Start Date: 27th September 2025
⏰ Time: 8 PM – 9 PM IST | Saturday
🔹 Course Content :
https://drive.google.com/file/d/1YufWV0Ru6SyYt-oNf5Mi5H8mmeV_kfP-/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/CONhbkkRrnB8MK7GjXbXS4?mode=ems_copy_t
📥 Register Now:
https://forms.gle/EP6XG8NvJkXh7sjw9
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Team
PVR Cloud Tech :)
+91-9346060794
10 884
🤡Most crypto channels just throw charts and hype at you.
This one gives clear, real moves instead.
Know what to buy, when to sell, and how to avoid costly mistakes.
New to crypto or already trading? Get clear moves, not noise.
👉 Join now and trade smarter:
https://t.me/+3xRw-RoEHhk0ZDJi
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🚀 Step-by-Step Guide to Become a Data Engineer in 2025 🛠️📂
1️⃣ Start with Programming Basics
Learn Python or Java — essential for scripting, automation & handling data.
2️⃣ Understand Databases
Master SQL for querying, plus NoSQL (MongoDB, Cassandra) for unstructured data.
3️⃣ Learn Data Warehousing
Get comfy with ETL, OLAP, Star/Snowflake schemas. Tools: Snowflake, Redshift, BigQuery.
4️⃣ Work with Big Data Tools
Explore Hadoop, Spark, Kafka — key for large-scale data processing.
5️⃣ Get Hands-On with Cloud Platforms
Focus on AWS, Azure, or GCP — master data services like S3, Lambda, Glue, BigQuery.
6️⃣ Practice Building Data Pipelines
Use Apache Airflow, dbt, or Prefect to build and orchestrate workflows end-to-end.
7️⃣ Version Control & CI/CD
Learn GitHub, Docker, Jenkins for collaboration and deployment.
8️⃣ Build a Strong Portfolio
Show off pipeline projects, cloud workflows, and architecture diagrams.
9️⃣ Apply for Data Engineering Roles
Look for titles like Data Engineer, ETL Developer, Cloud Data Engineer.
🔟 Keep Growing & Learning
Dive into real-time streaming, data security, optimization, and advanced data modeling.
——————————
🔥 In 2025, top skills include cloud computing, big data, ETL, programming (Python/Java), and data warehousing. Focus where demand is highest!
💡 Start small, build projects, experiment on free cloud tiers, and stay updated with emerging tech.
💬 Tap ❤️ for more!
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Greetings from PVR Cloud Tech!! 🌈
🚀 Kickstart Your Career in *Azure Data Engineering* – The Smart Way in 2025!
📌 Start Date: 15th September 2025
⏰ Time: 9 PM – 10 PM IST | Monday
🔹 Course Content:
https://drive.google.com/file/d/1YufWV0Ru6SyYt-oNf5Mi5H8mmeV_kfP-/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/JezGFEebk2G3TsZPzTsbZP
📥 Register Now:
https://forms.gle/8f6hzRCQJmShf5Eo8
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Cheers.
Team PVR Cloud Tech :)
+91-9346060794
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🚀 Walk-in Hiring Drive Alert! 🚀
AccioJob x Sceniuz are hiring for Data Analyst & Data Engineer roles!
* Graduation Year: Open to All
* Degree: BTech / BE / BCA / BSC / MTech /ME / MCA / MSC
* CTC: 3–6 LPA
* Offline Assesment at AccioJob partnered campus in Mumbai
👉🏻 Data Analyst: https://go.acciojob.com/47HSHh
👉🏻 Data Engineer: https://go.acciojob.com/PnRTK2
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+7
⌨️ MongoDB Cheat Sheet
MongoDB is a flexible, document-orientated, NoSQL database program that can scale to any enterprise volume without compromising search performance.This Post includes a MongoDB cheat sheet to make it easy for our followers to work with MongoDB. Working with databases Working with rows Working with Documents Querying data from documents Modifying data in documents Searching
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Amazon Interview Process for Data Scientist position
📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.
📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.
📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.
📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
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🚀 PyTorch vs TensorFlow – Which Should YOU Choose?
If you’re starting in AI or planning to build real-world apps, this is the big question.
👉 PyTorch – simple, feels like Python, runs instantly. Perfect for learning, experiments, and research.
👉 TensorFlow – built by Google, comes with a full production toolkit (mobile, web, cloud). Perfect for apps at scale.
✨ Developer Experience: PyTorch is beginner-friendly. TensorFlow has improved with Keras but still leans towards production use.
📊 Research vs Production: 75% of research papers use PyTorch, but TensorFlow powers large-scale deployments.
💡 Think of it like this:
PyTorch = Notebook for experiments ✍️
TensorFlow = Office suite for real apps 🏢
So the choice is simple:
Learning & Research → PyTorch
Scaling & Deployment → TensorFlow
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ChatGPT Prompt to learn any skill
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information
(Tap on above text to copy)10 884
Greetings from PVR Cloud Tech!! 🌈
🚀 Kickstart Your Career in Azure Data Engineering – The Smart Way in 2025!
📌 Start Date: 30th August 2025
⏰ Time: 7 AM – 8 AM IST | Saturday
🔹 Course Content :
https://drive.google.com/file/d/1YufWV0Ru6SyYt-oNf5Mi5H8mmeV_kfP-/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/JezGFEebk2G3TsZPzTsbZP
📥 Register Now:
https://forms.gle/6cRFoVHJBE6TubZJ7
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Cheers.
Team PVR Cloud Tech :)
+91-9346060794
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Q: How do you import data from various sources (Excel, SQL Server, CSV) into Power BI?
A: Here’s how to handle multi-source imports in Power BI Desktop:
1. Excel:
° Go to Home > Get Data > Excel
° Select your file & sheets or tables
2. CSV:
° Choose Get Data > Text/CSV
° Browse and load the file
3. SQL Server:
° Select Get Data > SQL Server
° Enter server/database name
° Use a query or select tables directly
4. Combine Sources:
° Use Power Query to transform, merge, or append tables
° Create relationships in the Model view
Pro Tip:
Use consistent data types and naming to make transformations smoother across sources!
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📌 🚀 How to Build a Personal Brand as a Data Analyst
Want to stand out in the competitive job market? Build your personal brand using these strategies:
✅ 1. Share Your Work Publicly – Post SQL/Python projects on LinkedIn, Medium, or GitHub.
✅ 2. Engage with Data Communities – Follow & contribute to Kaggle, DataCamp, or Analytics Vidhya.
✅ 3. Write About Data – Share blog posts on real-world data insights & case studies.
✅ 4. Present at Meetups/Webinars – Gain visibility & network with industry experts.
✅ 5. Optimize LinkedIn & GitHub – Highlight your skills, certifications, and projects.
💡 Start with one personal branding activity this week.
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Repost from Generative AI
𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀😍
Generative AI is no longer just a buzzword—it’s a career-maker🧑💻📌
Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.🖥
𝐋𝐢𝐧𝐤👇:-
http://pdlink.in/4fKT5pL
If you’re looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance
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FREE RESOURCES TO LEARN DATA ENGINEERING
👇👇
Big Data and Hadoop Essentials free course
https://bit.ly/3rLxbul
Data Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE
[4.6 stars out of 5]
https://bit.ly/3fGRjLu
Understanding Data Engineering from Datacamp
https://clnk.in/soLY
Data Engineering Free Books
https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf
https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf
Big Data of Data Engineering Free book
https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf
https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf
The Data Engineer’s Guide to Apache Spark
https://t.me/datasciencefun/783?single
Data Engineering with Python
https://t.me/pythondevelopersindia/343
Data Engineering Projects -
1.End-To-End From Web Scraping to Tableau https://lnkd.in/ePMw63ge
2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J
3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq
4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3
5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR
6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD
7. YouTube Data Analysis
(End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF
8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY
9. Sentiment analysis Twitter:
Kafka and Spark Structured Streaming - https://lnkd.in/esVAaqtU
ENJOY LEARNING 👍👍
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𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍
In today’s data-driven world, Power BI has become one of the most in-demand tools for businesses〽️📊
The best part? You don’t need to spend a fortune—there are free and affordable courses available online to get you started.💥🧑💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4mDvgDj
Start learning today and position yourself for success in 2025!✅️
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Roadmap to Become a Data Engineer in 10 Stages
Stage 1 → SQL & Database Fundamentals
Stage 2 → Python for Data Engineering (Pandas, PySpark)
Stage 3 → Data Modelling & ETL/ELT Design (Star Schema, CDC, DWH)
Stage 4 → Big Data Tools (Apache Spark, Kafka, Hive)
Stage 5 → Cloud Platforms (Azure / AWS / GCP)
Stage 6 → Data Orchestration (Airflow, ADF, Prefect, DBT)
Stage 7 → Data Lakes & Warehouses (Delta Lake, Snowflake, BigQuery)
Stage 8 → Monitoring, Testing & Governance (Great Expectations, DataDog)
Stage 9 → Real-Time Pipelines (Kafka, Flink, Kinesis)
Stage 10 → CI/CD & DevOps for Data (GitHub Actions, Terraform, Docker)
🏁 Congrats! You’re a Data Engineer.
Notes:
👉 You don’t need to learn everything at once.
👉 Build around one stack, skip a few steps if you’re just starting out.
👉 Master fundamentals first, then move to the cloud.
The key is consistency → take it step by step and grow your skill set!
