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Data Engineers
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Mostrar más📈 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 10 343 suscriptores, ocupando la posición 19 399 en la categoría Educación y el puesto 40 316 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 10 343 suscriptores.
Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 225, y en las últimas 24 horas de 9, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 11.49%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.44% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 188 visualizaciones. En el primer día suele acumular 252 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 07 junio, 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.
10 343
Suscriptores
+924 horas
+527 días
+22530 días
Archivo de publicaciones
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You don’t need to be a genius to profit from crypto.
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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😍
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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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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!😍
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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
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Repost from Python Projects & Resources
𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍
If you can answer these Python questions, you’re already ahead of 90% of candidates.🧑💻✨️
These aren’t your average textbook questions. These are real interview questions asked in top MNCs — designed to test how deeply you understand Python.📊📍
𝐋𝐢𝐧𝐤👇:-
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This is the smart way to prepare✅️
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ML Engineer vs AI Engineer
ML Engineer / MLOps
-Focuses on the deployment of machine learning models.
-Bridges the gap between data scientists and production environments.
-Designing and implementing machine learning models into production.
-Automating and orchestrating ML workflows and pipelines.
-Ensuring reproducibility, scalability, and reliability of ML models.
-Programming: Python, R, Java
-Libraries: TensorFlow, PyTorch, Scikit-learn
-MLOps: MLflow, Kubeflow, Docker, Kubernetes, Git, Jenkins, CI/CD tools
AI Engineer / Developer
- Applying AI techniques to solve specific problems.
- Deep knowledge of AI algorithms and their applications.
- Developing and implementing AI models and systems.
- Building and integrating AI solutions into existing applications.
- Collaborating with cross-functional teams to understand requirements and deliver AI-powered solutions.
- Programming: Python, Java, C++
- Libraries: TensorFlow, PyTorch, Keras, OpenCV
- Frameworks: ONNX, Hugging Face
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Repost from Python Projects & Resources
𝟱 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗡𝗲𝗲𝗱𝗲𝗱!)😍
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Best For: Beginners ready to dive into real machine learning✅️
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SQL Cheatsheet 📝
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.
1. Database Basics
-
CREATE DATABASE db_name;
- USE db_name;
2. Tables
- Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype);
- Drop Table: DROP TABLE table_name;
- Alter Table: ALTER TABLE table_name ADD column_name datatype;
3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);
4. Select Queries
- Basic Select: SELECT * FROM table_name;
- Select Specific Columns: SELECT col1, col2 FROM table_name;
- Select with Condition: SELECT * FROM table_name WHERE condition;
5. Update Data
- UPDATE table_name SET col1 = value1 WHERE condition;
6. Delete Data
- DELETE FROM table_name WHERE condition;
7. Joins
- Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;
- Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;
- Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;
8. Aggregations
- Count: SELECT COUNT(*) FROM table_name;
- Sum: SELECT SUM(col) FROM table_name;
- Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col;
9. Sorting & Limiting
- Order By: SELECT * FROM table_name ORDER BY col ASC|DESC;
- Limit Results: SELECT * FROM table_name LIMIT n;
10. Indexes
- Create Index: CREATE INDEX idx_name ON table_name (col);
- Drop Index: DROP INDEX idx_name;
11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);
12. Views
- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
- Drop View: DROP VIEW view_name;10 345
Repost from Python Projects & Resources
𝗧𝗼𝗽 𝟱 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗠𝗮𝘀𝘁𝗲𝗿𝘆😍
Want to become a Data Analyst but don’t know where to start? 🧑💻✨️
You don’t need to spend thousands on courses. In fact, some of the best free learning resources are already on YouTube — taught by industry professionals who break down everything step by step.📊📌
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Top 20 #SQL INTERVIEW QUESTIONS
1️⃣ Explain Order of Execution of SQL query
2️⃣ Provide a use case for each of the functions Rank, Dense_Rank & Row_Number ( 💡 majority struggle )
3️⃣ Write a query to find the cumulative sum/Running Total
4️⃣ Find the Most selling product by sales/ highest Salary of employees
5️⃣ Write a query to find the 2nd/nth highest Salary of employees
6️⃣ Difference between union vs union all
7️⃣ Identify if there any duplicates in a table
8️⃣ Scenario based Joins question, understanding of Inner, Left and Outer Joins via simple yet tricky question
9️⃣ LAG, write a query to find all those records where the transaction value is greater then previous transaction value
1️⃣ 0️⃣ Rank vs Dense Rank, query to find the 2nd highest Salary of employee
( Ideal soln should handle ties)
1️⃣ 1️⃣ Write a query to find the Running Difference (Ideal sol'n using windows function)
1️⃣ 2️⃣ Write a query to display year on year/month on month growth
1️⃣ 3️⃣ Write a query to find rolling average of daily sign-ups
1️⃣ 4️⃣ Write a query to find the running difference using self join (helps in understanding the logical approach, ideally this question is solved via windows function)
1️⃣ 5️⃣ Write a query to find the cumulative sum using self join
(you can use windows function to solve this question)
1️⃣6️⃣ Differentiate between a clustered index and a non-clustered index?
1️⃣7️⃣ What is a Candidate key?
1️⃣8️⃣What is difference between Primary key and Unique key?
1️⃣9️⃣What's the difference between RANK & DENSE_RANK in SQL?
2️⃣0️⃣ Whats the difference between LAG & LEAD in SQL?
Access SQL Learning Series for Free: https://t.me/sqlspecialist/523
Hope it helps :)
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Repost from Generative AI
𝟯 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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Common Data Cleaning Techniques for Data Analysts
Remove Duplicates:
Purpose: Eliminate repeated rows to maintain unique data.
Example: SELECT DISTINCT column_name FROM table;
Handle Missing Values:
Purpose: Fill, remove, or impute missing data.
Example:
Remove: df.dropna() (in Python/Pandas)
Fill: df.fillna(0)
Standardize Data:
Purpose: Convert data to a consistent format (e.g., dates, numbers).
Example: Convert text to lowercase: df['column'] = df['column'].str.lower()
Remove Outliers:
Purpose: Identify and remove extreme values.
Example: df = df[df['column'] < threshold]
Correct Data Types:
Purpose: Ensure columns have the correct data type (e.g., dates as datetime, numeric values as integers).
Example: df['date'] = pd.to_datetime(df['date'])
Normalize Data:
Purpose: Scale numerical data to a standard range (0 to 1).
Example: from sklearn.preprocessing import MinMaxScaler; df['scaled'] = MinMaxScaler().fit_transform(df[['column']])
Data Transformation:
Purpose: Transform or aggregate data for better analysis (e.g., log transformations, aggregating columns).
Example: Apply log transformation: df['log_column'] = np.log(df['column'] + 1)
Handle Categorical Data:
Purpose: Convert categorical data into numerical data using encoding techniques.
Example: df['encoded_column'] = pd.get_dummies(df['category_column'])
Impute Missing Values:
Purpose: Fill missing values with a meaningful value (e.g., mean, median, or a specific value).
Example: df['column'] = df['column'].fillna(df['column'].mean())
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Share with credits: https://t.me/sqlspecialist
Hope it helps :)
