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Red:📈 Análisis del canal de Telegram Data Engineers
El canal Data Engineers (@sql_engineer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 10 884 suscriptores, ocupando la posición 18 004 en la categoría Educación y el puesto 35 805 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 10 884 suscriptores.
Según los últimos datos del 26 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 277, y en las últimas 24 horas de 8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 11.06%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.83% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 204 visualizaciones. En el primer día suele acumular 308 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- Intereses temáticos: El contenido se centra en temas clave como sql, learning, analytic, engineer, link:-.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Free Data Engineering Ebooks & Courses”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
10 884
Suscriptores
+824 horas
+357 días
+27730 días
Archivo de publicaciones
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𝟯 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍
Want to break into Data Science or Tech?
Python is the #1 skill you need — and starting is easier than you think.🧑💻✨️
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𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐢𝐠 𝐝𝐚𝐭𝐚 - 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐛𝐞𝐬𝐭 𝐟𝐫𝐢𝐞𝐧𝐝.
Whether you're building data pipelines, transforming terabytes of logs, or cleaning data for analytics, PySpark helps you scale Python across distributed systems with ease.
Here are a few PySpark fundamentals every Data Engineer should be confident with:
𝟏. 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐥𝐲
spark.read.csv(), json(), parquet()
Choose the right format for performance.
𝟐. 𝐂𝐨𝐫𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧𝐬
map, flatMap, filter, union
Understand how these shape your RDDs or DataFrames.
𝟑. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞
groupBy, agg, .count()
Use them to build clean summaries and insights from raw data.
𝟒. 𝐂𝐨𝐥𝐮𝐦𝐧 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬
withColumn() is a go-to tool for feature engineering or adding derived columns.
Data Engineering is about building scalable, reliable, and efficient systems-and PySpark makes that possible when you're working with huge datasets.
React ♥️ for more
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Repost from Python Projects & Resources
𝐄𝐚𝐫𝐧 𝐅𝐑𝐄𝐄 𝐎𝐫𝐚𝐜𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐂𝐥𝐨𝐮𝐝, 𝐀𝐈 & 𝐃𝐚𝐭𝐚!😍
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📖 Data Engineering Roadmap 2025
𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟 (𝗔𝗪𝗦 𝗥𝗗𝗦, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟, 𝗔𝘇𝘂𝗿𝗲 𝗦𝗤𝗟)
💡 Why? Cloud-managed databases are the backbone of modern data platforms.
✅ Serverless, scalable, and cost-efficient
✅ Automated backups & high availability
✅ Works seamlessly with cloud data pipelines
𝟮. 𝗱𝗯𝘁 (𝗗𝗮𝘁𝗮 𝗕𝘂𝗶𝗹𝗱 𝗧𝗼𝗼𝗹) – 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝗟𝗧
💡 Why? Transform data inside your warehouse (Snowflake, BigQuery, Redshift).
✅ SQL-based transformation – easy to learn
✅ Version control & modular data modeling
✅ Automates testing & documentation
𝟯. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 – 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻
💡 Why? Automate and schedule complex ETL/ELT workflows.
✅ DAG-based orchestration for dependency management
✅ Integrates with cloud services (AWS, GCP, Azure)
✅ Highly scalable & supports parallel execution
𝟰. 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 – 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗖𝗜𝗗 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀
💡 Why? Solves data consistency & reliability issues in Apache Spark & Databricks.
✅ Supports ACID transactions in data lakes
✅ Schema evolution & time travel
✅ Enables incremental data processing
𝟱. 𝗖𝗹𝗼𝘂𝗱 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 (𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲, 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, 𝗥𝗲𝗱𝘀𝗵𝗶𝗳𝘁)
💡 Why? Centralized, scalable, and powerful for analytics.
✅ Handles petabytes of data efficiently
✅ Pay-per-use pricing & serverless architecture
𝟲. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 – 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴
💡 Why? For real-time event-driven architectures.
✅ High-throughput
𝟳. 𝗣𝘆𝘁𝗵𝗼𝗻 & 𝗦𝗤𝗟 – 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
💡 Why? Every data engineer must master these!
✅ SQL for querying, transformations & performance tuning
✅ Python for automation, data processing, and API integrations
𝟴. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 – 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜
💡 Why? The go-to platform for big data processing & machine learning on the cloud.
✅ Built on Apache Spark for fast distributed computing
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𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍
Breaking into Data Analytics isn’t just about knowing the tools — it’s about answering the right questions with confidence🧑💻✨️
Whether you’re aiming for your first role or looking to level up your career, these real interview questions will test your skills📊📌
𝐋𝐢𝐧𝐤👇:-
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Don’t just learn — prepare smart✅️
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📘 SQL Challenges for Data Analytics – With Explanation 🧠
(Beginner ➡️ Advanced)
1️⃣ Select Specific Columns
SELECT name, email FROM users;name and email columns from the users table.
✔️ Used when you don’t want all columns from a table.
2️⃣ Filter Records with WHERE
SELECT * FROM users WHERE age > 30;WHERE clause filters rows where age is greater than 30.
✔️ Used for applying conditions on data.
3️⃣ ORDER BY Clause
SELECT * FROM users ORDER BY registered_at DESC;registered_at in descending order.
✔️ Helpful to get latest data first.
4️⃣ Aggregate Functions (COUNT, AVG)
SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users;COUNT(*) counts total rows (users).
- AVG(age) calculates the average age.
✔️ Used for quick stats from tables.
5️⃣ GROUP BY Usage
SELECT city, COUNT(*) AS user_count FROM users GROUP BY city;city and counts users in each group.
✔️ Use when you want grouped summaries.
6️⃣ JOIN Tables
SELECT users.name, orders.amount
FROM users
JOIN orders ON users.id = orders.user_id;users and orders on matching IDs.
✔️ Essential when combining data from multiple tables.
7️⃣ Use of HAVING
SELECT city, COUNT(*) AS total
FROM users
GROUP BY city
HAVING COUNT(*) > 5;WHERE, but used with aggregates. This filters cities with more than 5 users.
✔️ **Use HAVING after GROUP BY.**
8️⃣ Subqueries
SELECT * FROM users
WHERE salary > (SELECT AVG(salary) FROM users);SELECT name,
CASE
WHEN age < 18 THEN 'Teen'
WHEN age <= 40 THEN 'Adult'
ELSE 'Senior'
END AS age_group
FROM users;SELECT name, city, score,
RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank
FROM users;10 886
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You just need clear info you can trust.
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+5
⌨️ HTML Lists Knick Knacks
Here is a list of fun things you can do with lists in HTML 😁
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𝐒𝐭𝐚𝐫𝐭 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — 𝟏𝟎𝟎% 𝐅𝐫𝐞𝐞 & 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲😍
Want to dive into data analytics but don’t know where to start?🧑💻✨️
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.📊📌
𝐋𝐢𝐧𝐤👇:-
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No prior experience needed — just curiosity✅️
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Adaptive Query Execution (AQE) in Apache Spark is a feature introduced to improve query performance dynamically at runtime, based on actual data statistics collected during execution.
This makes Spark smarter and more efficient, especially when dealing with real-world messy data where planning ahead (at compile time) might be misleading.
🔍 Importance of AQE in Spark
Runtime Optimization:
AQE adapts the execution plan on the fly using real-time stats, fixing issues that static planning can't predict.
Better Join Strategy:
If Spark detects at runtime that one table is smaller than expected, it can switch to a broadcast join instead of a slower shuffle join.
Improved Resource Usage:
By optimizing stage sizes and join plans, AQE avoids unnecessary shuffling and memory usage, leading to faster execution and lower cost.
🪓 Handling Data Skew with AQE
Data skew occurs when some partitions (e.g., specific keys) have much more data than others, slowing down those tasks.
AQE handles this using:
Skew Join Optimization:
AQE detects skewed partitions and breaks them into smaller sub-partitions, allowing Spark to process them in parallel instead of waiting on one giant slow task.
Automatic Repartitioning:
It can dynamically adjust partition sizes for better load balancing, reducing the "straggler" effect from skew.
💡 Example:
If a join key like customer_id = 12345 appears millions of times more than others, Spark can split just that key’s data into chunks, while keeping others untouched. This makes the whole join process more balanced and efficient.
In summary, AQE improves performance, handles skew gracefully, and makes Spark queries more resilient and adaptive—especially useful in big, uneven datasets.
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Repost from Artificial Intelligence
𝟰 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗟𝗲𝗮𝗿𝗻 & 𝗘𝗮𝗿𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀😍
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From coding essentials to data analytics, programming foundations, and business insights — these handpicked free courses will help you gain practical, in-demand skills fast.🧑🎓📌
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Perfect for beginners and professionals looking to upskill without spending a dime.✅️
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ETL vs ELT – Explained Using Apple Juice analogy! 🍎🧃
We often hear about ETL and ELT in the data world — but how do they actually apply in tools like Excel and Power BI?
Let’s break it down with a simple and relatable analogy 👇
✅ ETL (Extract → Transform → Load)
🧃 First you make the juice, then you deliver it
➡️ Apples → Juice → Truck
🔹 In Power BI / Excel:
You clean and transform the data in Power Query
Then load the final data into your report or sheet
💡 That’s ETL – transformation happens before loading
✅ ELT (Extract → Load → Transform)
🍏 First you deliver the apples, and make juice later
➡️ Apples → Truck → Juice
🔹 In Power BI / Excel:
You load raw data into your model or sheet
Then transform it using DAX, formulas, or pivot tables
💡 That’s ELT – transformation happens after loading
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Repost from Python Projects & Resources
𝟓 𝐅𝐫𝐞𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐨𝐝𝐢𝐧𝐠😍
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If I were planning for Data Engineering interviews in the upcoming months then I will prepare this way ⛵
1. Learn important SQL concepts
Go through all key topics in SQL like joins, CTEs, window functions, group by, having etc.
2. Solve 50+ recently asked SQL queries
Practice queries from real interviews. focus on tricky joins, aggregations and filtering.
3. Solve 50+ Python coding questions
Focus on:
List, dictionary, string problems, File handling, Algorithms (sorting, searching, etc.)
4. Learn PySpark basics
Understand: RDDs, DataFrames , Datasets & Spark SQL
5. Practice 20 top PySpark coding tasks
Work on real coding examples using PySpark -data filtering, joins, aggregations, etc.
6. Revise Data Warehousing concepts
Focus on:
Star and snowflake schema
Normalization and denormalization
7. Understand the data model used in your project
Know the structure of your tables and how they connect.
8. Practice explaining your project
Be ready to talk about: Architecture, Tools used, Pipeline flow & Business value
9. Review cloud services used in your project
For AWS, Azure, GCP:
Understand what services you used, why you used them nd how they work.
10. Understand your role in the project
Be clear on what you did technically . What problems you solved and how.
11. Prepare to explain the full data pipeline
From data ingestion to storage to processing - use examples.
12. Go through common Data Engineer interview questions
Practice answering questions about ETL, SQL, Python, Spark, cloud etc.
13. Read recent interview experiences
Check LinkedIn , GeeksforGeeks, Medium for company-specific interview experiences.
14. Prepare for high-level system design
questions.
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𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝘇𝘂𝗿𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟯 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗠𝗼𝗱𝘂𝗹𝗲𝘀!😍
Start Mastering Azure Machine Learning — 100% Free!💥
Want to get into AI and Machine Learning using Azure but don’t know where to begin?📊📌
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These official Microsoft Learn modules are all you need — hands-on, beginner-friendly, and backed with certificates🧑🎓📜
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Data Engineers – Don’t Just Learn Tools. Learn This:
So you’re learning:
– Spark ✅
– Airflow ✅
– dbt ✅
– Kafka ✅
But here’s a hard truth 👇
🧠 Tools change. Principles don’t.
Top 1% Data Engineers focus on:
🔸 Data modeling – Understand star vs snowflake, SCDs, normalization.
🔸 Data contracts – Build reliable pipelines, not spaghetti code.
🔸 System design – Think like a backend engineer. Learn how data flows.
🔸 Observability – Logging, metrics, lineage. Be the one who finds data bugs.
💥 Want to level up? Do this:
✅ Build a mini data warehouse from scratch (on DuckDB + Airflow)
✅ Join open-source data eng projects
✅ Read “The Data Engineering Cookbook” (free)
📈 Don’t just run pipelines. Architect them.
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If you want to Excel as a Data Analyst and land a high-paying job, master these essential skills:
1️⃣ Data Extraction & Processing:
• SQL – SELECT, JOIN, GROUP BY, CTE, WINDOW FUNCTIONS
• Python/R for Data Analysis – Pandas, NumPy, Matplotlib, Seaborn
• Excel – Pivot Tables, VLOOKUP, XLOOKUP, Power Query
2️⃣ Data Cleaning & Transformation:
• Handling Missing Data – COALESCE(), IFNULL(), DROPNA()
• Data Normalization – Removing duplicates, standardizing formats
• ETL Process – Extract, Transform, Load
3️⃣ Exploratory Data Analysis (EDA):
• Descriptive Statistics – Mean, Median, Mode, Variance, Standard Deviation
• Data Visualization – Bar Charts, Line Charts, Heatmaps, Histograms
4️⃣ Business Intelligence & Reporting:
• Power BI & Tableau – Dashboards, DAX, Filters, Drill-through
• Google Data Studio – Interactive reports
5️⃣ Data-Driven Decision Making:
• A/B Testing – Hypothesis testing, P-values
• Forecasting & Trend Analysis – Time Series Analysis
• KPI & Metrics Analysis – ROI, Churn Rate, Customer Segmentation
6️⃣ Data Storytelling & Communication:
• Presentation Skills – Explain insights to non-technical stakeholders
• Dashboard Best Practices – Clean UI, relevant KPIs, interactive visuals
7️⃣ Bonus: Automation & AI Integration
• SQL Query Optimization – Improve query performance
• Python Scripting – Automate repetitive tasks
• ChatGPT & AI Tools – Enhance productivity
Like this post if you need a complete tutorial on all these topics! 👍❤️
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#dataanalysts
