Data Engineers

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网络:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert 印度38 949 教育19 167...

📈 Telegram 频道 Data Engineers 的分析概览

频道 Data Engineers (@sql_engineer) 英语语言赛道中的是活跃参与者。目前社区聚集了 10 421 名订阅者，在教育类别中位列第 19 167，并在印度地区排名第 38 949 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 10 421 名订阅者。

根据 23 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 189，过去 24 小时变化为 9，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 14.46%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 0 次浏览，首日通常累积 0 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 0。
主题关注点： 内容集中在 sql, learning, analytic, engineer, link:- 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“Free Data Engineering Ebooks & Courses”

凭借高频更新（最新数据采集于 24 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为教育类别中的关键影响点。

10 421

订阅者

+924 小时

+77 天

+18930 天

1 507

帖子浏览量

无数据24 小时

无数据48 小时

14.46%

参与率

无数据

每日帖子数

Ads index

beta

数据加载中...

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六月 '26

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+170

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+177

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+921

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+1 260

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+650

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+759

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日期	订阅者增长	提及	频道
24 六月	+9
23 六月	+9
22 六月	+2
21 六月	+3
20 六月	+1
19 六月	+1
18 六月	+1
17 六月	+4
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15 六月	+3
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13 六月	+12
12 六月	+9
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09 六月	+12
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07 六月	+9
06 六月	+4
05 六月	+9
04 六月	+5
03 六月	+9
02 六月	+6
01 六月	+5

频道帖子

🚀Greetings from PVR Cloud Tech!! 🌈 🔥 Do you want to become a Master in Azure Cloud Data Engineering? If you're ready to build in-demand skills and unlock exciting career opportunities, this is the perfect place to start! 📌 Start Date: 1st June 2026 ⏰ Time: 09 PM – 10 PM IST | Monday 🔗 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐥𝐢𝐯𝐞 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬? 👉 Message us on WhatsApp: https://wa.me/917032678595?text=Interested_to_join_Azure_Data_Engineering_live_sessions 🔹 Course Content: https://drive.google.com/file/d/1QKqhRMHx2SDNDTmPAf3₅4fA6LljKHm6/view 📱 Join WhatsApp Group: https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk 📥 Register Now: https://forms.gle/LidHPdfxvNeg9LpeA Team PVR Cloud Tech :) +91-9346060794

2	🚀 Top Skills Every Data Engineer Should Learn 📊🔥 🧠 1. SQL Mastery ✔ Complex Queries ✔ JOINS & Window Functions ✔ Query Optimization ✔ Data Modeling ✔ Stored Procedures 🐍 2. Programming Skills ✔ Python for Automation ✔ APIs & JSON ✔ Data Processing Scripts ✔ Error Handling 🛠 Libraries to Learn: ✔ Pandas ✔ PySpark ✔ Requests ⚡ 3. ETL & Data Pipelines ✔ Extract, Transform, Load ✔ Workflow Automation ✔ Scheduling Jobs ✔ Monitoring Pipelines 🛠 Tools to Learn: ✔ Apache Airflow ✔ dbt ✔ Prefect ☁️ 4. Cloud Platforms ✔ Cloud Storage ✔ Data Lakes ✔ Scalable Processing ✔ Cloud Security Basics 🛠 Platforms to Learn: ✔ AWS ✔ Microsoft Azure ✔ Google Cloud Platform 📊 5. Big Data Technologies ✔ Distributed Computing ✔ Real-Time Streaming ✔ Batch Processing ✔ Scalable Systems 🛠 Technologies to Learn: ✔ Apache Spark ✔ Hadoop ✔ Apache Kafka 🗄 6. Databases & Warehousing ✔ Relational Databases ✔ NoSQL Databases ✔ Data Warehouses ✔ Schema Design 🛠 Databases to Learn: ✔ PostgreSQL ✔ MongoDB ✔ Snowflake ✔ BigQuery 🔄 7. DevOps & Deployment ✔ Version Control ✔ Containerization ✔ CI/CD Basics ✔ Deployment Automation 🛠 Tools to Learn: ✔ Git ✔ Docker ✔ Kubernetes 💡 Data Engineers don’t just move data… they build the backbone of modern AI & analytics systems. 💬 Tap ❤️ if this helped you!	1 636
3	📈 FREE Live Masterclass for Future Business Analysts! 📊 4 Steps to Become a Successful Business Analyst in 2026 📅 May 20th, 2026 ⏰ 7:00 PM 🌐 English 🎟️ 90 Minutes of Career Guidance & Industry Insights 💡 Learn: ✔ Core Business Analytics Skills & AI usage ✔ Real-World Case Studies ✔ Career Roadmap for 2026 ✔ Tools Used by Top Companies 🔥 Perfect for: Students \| Freshers \| Working Professionals \| Career Switchers 📌 Register Now: https://rebrand.ly/Business-analyst-webinar	1 549
4	What is the difference between data scientist, data engineer, data analyst and business intelligence? 🧑🔬 Data Scientist Focus: Using data to build models, make predictions, and solve complex problems. Cleans and analyzes data Builds machine learning models Answers “Why is this happening?” and “What will happen next?” Works with statistics, algorithms, and coding (Python, R) Example: Predict which customers are likely to cancel next month 🛠️ Data Engineer Focus: Building and maintaining the systems that move and store data. Designs and builds data pipelines (ETL/ELT) Manages databases, data lakes, and warehouses Ensures data is clean, reliable, and ready for others to use Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP) Example: Create a system that collects app data every hour and stores it in a warehouse 📊 Data Analyst Focus: Exploring data and finding insights to answer business questions. Pulls and visualizes data (dashboards, reports) Answers “What happened?” or “What’s going on right now?” Works with SQL, Excel, and tools like Tableau or Power BI Less coding and modeling than a data scientist Example: Analyze monthly sales and show trends by region 📈 Business Intelligence (BI) Professional Focus: Helping teams and leadership understand data through reports and dashboards. Designs dashboards and KPIs (key performance indicators) Translates data into stories for non-technical users Often overlaps with data analyst role but more focused on reporting Tools: Power BI, Looker, Tableau, Qlik Example: Build a dashboard showing company performance by department 🧩 Summary Table Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers 🎯 In short: Data Engineers build the roads. Data Scientists drive smart cars to predict traffic. Data Analysts look at traffic data to see patterns. BI Professionals show everyone the traffic report on a screen.	1 849
5	✅ Skills Required to Become a Data Engineer ⚙️🚀 🧠 PROGRAMMING 1. Python (Data Pipelines) 2. Java / Scala 3. Object-Oriented Programming 4. Scripting (Automation) 5. Debugging Skills 6. Code Optimization 7. API Handling 8. Version Control (Git) 🗄️ DATABASES 1. SQL (Advanced Queries) 2. NoSQL (MongoDB, Cassandra) 3. Database Design 4. Data Modeling 5. Indexing Partitioning 6. Query Optimization 7. Data Warehousing 8. OLTP vs OLAP ⚙️ ETL / ELT 1. Data Extraction 2. Data Transformation 3. Data Loading 4. Pipeline Building 5. Workflow Automation 6. Data Integration 7. Batch Processing 8. Real-time Processing ☁️ BIG DATA TECHNOLOGIES 1. Hadoop 2. Spark 3. Kafka 4. Hive 5. Flink 6. Distributed Systems 7. Cluster Computing 8. Stream Processing ☁️ CLOUD PLATFORMS 1. AWS (S3, Redshift, Glue) 2. Azure (Data Factory, Synapse) 3. Google Cloud (BigQuery) 4. Cloud Storage 5. Serverless Architecture 6. Data Lakes 7. Security IAM 8. Cost Optimization 📊 DATA PIPELINES 1. Building Scalable Pipelines 2. Data Orchestration (Airflow) 3. Scheduling Jobs 4. Monitoring Pipelines 5. Error Handling 6. Logging Systems 7. Data Reliability 8. Performance Tuning 🧱 DATA ARCHITECTURE 1. Data Lakes 2. Data Warehouses 3. Lakehouse Architecture 4. Schema Design 5. Data Governance 6. Data Security 7. Metadata Management 8. Scalability Planning 🔍 DEVOPS TOOLS 1. Docker 2. Kubernetes 3. CI/CD Pipelines 4. Linux Basics 5. Shell Scripting 6. Git GitHub 7. Monitoring Tools 8. Infrastructure as Code 💬 Tap ❤️ if this helped you follow for more Data Engineering content!	1 874
6	Every day you login... Work.. and logout. Days become months. Months become years. But nothing changes. Same role. Same work. Same pay. Meanwhile, others are moving into Cloud & Data Engineering… building real systems and earning better. If you are looking to get into Azure Data Engineering then.. 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 3 months 𝗟𝗶𝘃𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 📌 Start Date: 20th April 2026 ⏰ Time: 9 PM – 10 PM IST \| Monday 👉 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐮𝐬 𝐨𝐧 𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩: https://wa.me/917032678595?text=Interested_to_join_Azure_Data_Engineering_live_sessions 🔹 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://forms.gle/DRXEhvyG9ENDsNYR9 🎟️ 𝗝𝗼𝗶𝗻 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽 𝗚𝗿𝗼𝘂𝗽: https://chat.whatsapp.com/GCG3Si7vhrJD1evV9NAbhL 🏀 𝗖𝗼𝘂𝗿𝘀𝗲 𝗖𝗼𝗻𝘁𝗲𝗻𝘁: https://drive.google.com/file/d/1QKqhRMHx2SDNDTmPAf3_54fA6LljKHm6/view	514
7	🧠 SQL Interview Question (Running Total of Sales) 📌 sales(order_id, order_date, amount) ❓ Ques : 👉 Calculate the running total of sales for each day 👉 Return order_date, daily_sales, running_total 🧩 How Interviewers Expect You to Think • Aggregate sales per day 📊 • Use window function for cumulative sum • Order data correctly for running calculation 💡 SQL Solution WITH daily_sales AS ( SELECT order_date, SUM(amount) AS daily_sales FROM sales GROUP BY order_date ) SELECT order_date, daily_sales, SUM(daily_sales) OVER ( ORDER BY order_date ) AS running_total FROM daily_sales; 🔥 Why This Question Is Powerful • Tests window functions (must-know) 🧠 • Very common in real-world reporting • Frequently asked in analyst & BI roles ❤️ React for more SQL interview questions 🚀	1 994
8	🔰 Python function with an example	1 709

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