Data Engineering Tools

Interview questions for Data Architect and Data Engineer positions:

Design and Architecture


1.⁠ ⁠Design a data warehouse architecture for a retail company.
2.⁠ ⁠How would you approach data governance in a large organization?
3.⁠ ⁠Describe a data lake architecture and its benefits.
4.⁠ ⁠How do you ensure data quality and integrity in a data warehouse?
5.⁠ ⁠Design a data mart for a specific business domain (e.g., finance, healthcare).


Data Modeling and Database Design


1.⁠ ⁠Explain the differences between relational and NoSQL databases.
2.⁠ ⁠Design a database schema for a specific use case (e.g., e-commerce, social media).
3.⁠ ⁠How do you approach data normalization and denormalization?
4.⁠ ⁠Describe entity-relationship modeling and its importance.
5.⁠ ⁠How do you optimize database performance?


Data Security and Compliance


1.⁠ ⁠Describe data encryption methods and their applications.
2.⁠ ⁠How do you ensure data privacy and confidentiality?
3.⁠ ⁠Explain GDPR and its implications on data architecture.
4.⁠ ⁠Describe access control mechanisms for data systems.
5.⁠ ⁠How do you handle data breaches and incidents?


Data Engineer Interview Questions!!


Data Processing and Pipelines


1.⁠ ⁠Explain the concepts of batch processing and stream processing.
2.⁠ ⁠Design a data pipeline using Apache Beam or Apache Spark.
3.⁠ ⁠How do you handle data integration from multiple sources?
4.⁠ ⁠Describe data transformation techniques (e.g., ETL, ELT).
5.⁠ ⁠How do you optimize data processing performance?


Big Data Technologies


1.⁠ ⁠Explain Hadoop ecosystem and its components.
2.⁠ ⁠Describe Spark RDD, DataFrame, and Dataset.
3.⁠ ⁠How do you use NoSQL databases (e.g., MongoDB, Cassandra)?
4.⁠ ⁠Explain cloud-based big data platforms (e.g., AWS, GCP, Azure).
5.⁠ ⁠Describe containerization using Docker.


Data Storage and Retrieval


1.⁠ ⁠Explain data warehousing concepts (e.g., fact tables, dimension tables).
2.⁠ ⁠Describe column-store and row-store databases.
3.⁠ ⁠How do you optimize data storage for query performance?
4.⁠ ⁠Explain data caching mechanisms.
5.⁠ ⁠Describe graph databases and their applications.


Behavioral and Soft Skills


1.⁠ ⁠Can you describe a project you led and the challenges you faced?
2.⁠ ⁠How do you collaborate with cross-functional teams?
3.⁠ ⁠Explain your experience with Agile development methodologies.
4.⁠ ⁠Describe your approach to troubleshooting complex data issues.
5.⁠ ⁠How do you stay up-to-date with industry trends and technologies?


Additional Tips


1.⁠ ⁠Review the company's technology stack and be prepared to discuss relevant tools and technologies.
2.⁠ ⁠Practice whiteboarding exercises to improve your design and problem-solving skills.
3.⁠ ⁠Prepare examples of your experience with data architecture and engineering concepts.
4.⁠ ⁠Demonstrate your ability to communicate complex technical concepts to non-technical stakeholders.
5.⁠ ⁠Show enthusiasm and passion for data architecture and engineering.

𝟳 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗠𝗮𝘀𝘁𝗲𝗿😍

If you’re serious about becoming a data analyst, there’s no skipping SQL. It’s not just another technical skill — it’s the core language for data analytics.📊

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This guide covers 7 key SQL concepts that every beginner must learn✅️

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

𝗔𝗰𝗲 𝗬𝗼𝘂𝗿 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟯𝟬 𝗠𝗼𝘀𝘁-𝗔𝘀𝗸𝗲𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀! 😍

🤦🏻‍♀️Struggling with SQL interviews? Not anymore!📍

SQL interviews can be challenging, but preparation is the key to success. Whether you’re aiming for a data analytics role or just brushing up, this resource has got your back!🎊

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Understand the power of Data Lakehouse Architecture for 𝗙𝗥𝗘𝗘 here...


🚨𝗢𝗹𝗱 𝘄𝗮𝘆
• Complicated ETL processes for data integration.
• Silos of data storage, separating structured and unstructured data.
• High data storage and management costs in traditional warehouses.
• Limited scalability and delayed access to real-time insights.

✅𝗡𝗲𝘄 𝗪𝗮𝘆
• Streamlined data ingestion and processing with integrated SQL capabilities.
• Unified storage layer accommodating both structured and unstructured data.
• Cost-effective storage by combining benefits of data lakes and warehouses.
• Real-time analytics and high-performance queries with SQL integration.

The shift?

Unified Analytics and Real-Time Insights > Siloed and Delayed Data Processing

Leveraging SQL to manage data in a data lakehouse architecture transforms how businesses handle data.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

🌈 Greetings from PVR CLOUD TECH!

📔 Course : Azure Data Engineering


🗓 Date: 4th August 2025

🕗 Time: 9 PM to 10 PM IST | Monday

Duration: 3 Months

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Thanks,
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Top 100 Python Interview Questions 🚀🔥

𝟲 𝗙𝗿𝗲𝗲 𝗙𝘂𝗹𝗹 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗪𝗮𝘁𝗰𝗵 𝗥𝗶𝗴𝗵𝘁 𝗡𝗼𝘄😍

Ready to level up your tech game without spending a rupee? These 6 full-length courses are beginner-friendly, 100% free, and packed with practical knowledge📚🧑‍🎓

Whether you want to code in Python, hack ethically, or build your first Android app — these videos are your shortcut to real tech skills📱💻

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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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Hope it helps :)

𝟯 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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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?

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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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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;

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✅️

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

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.📊📍

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This is the smart way to prepare✅️

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

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.