𝗕𝗿𝗲𝗮𝗸 𝗜𝗻𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲😍

If you’re serious about AI, you can’t skip Deep Learning—and this FREE course from MIT is one of the best ways to start👨‍💻📌

Offered by MIT’s top researchers and engineers, this online course is open to everyone, no matter where you live or work🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3H6cggR

Why wait to get started when you can learn from MIT for free?✅️

Here are some commonly asked SQL interview questions along with brief answers:

1. What is SQL?
- SQL stands for Structured Query Language, used for managing and manipulating relational databases.

2. What are the types of SQL commands?
- SQL commands can be broadly categorized into four types: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), and Transaction Control Language (TCL).

3. What is the difference between CHAR and VARCHAR data types?
- CHAR is a fixed-length character data type, while VARCHAR is a variable-length character data type. CHAR will always occupy the same amount of storage space, while VARCHAR will only use the necessary space to store the actual data.

4. What is a primary key?
- A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures data integrity by enforcing uniqueness and can be used to establish relationships between tables.

5. What is a foreign key?
- A foreign key is a column or a set of columns in one table that refers to the primary key in another table. It establishes a relationship between two tables and ensures referential integrity.

6. What is a JOIN in SQL?
- JOIN is used to combine rows from two or more tables based on a related column between them. There are different types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

7. What is the difference between INNER JOIN and OUTER JOIN?
- INNER JOIN returns only the rows that have matching values in both tables, while OUTER JOIN (LEFT, RIGHT, FULL) returns all rows from one or both tables, with NULL values in columns where there is no match.

8. What is the difference between GROUP BY and ORDER BY?
- GROUP BY is used to group rows that have the same values into summary rows, typically used with aggregate functions like SUM, COUNT, AVG, etc., while ORDER BY is used to sort the result set based on one or more columns.

9. What is a subquery?
- A subquery is a query nested within another query, used to return data that will be used in the main query. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.

10. What is normalization in SQL?
- Normalization is the process of organizing data in a database to reduce redundancy and dependency. It involves dividing large tables into smaller tables and defining relationships between them to improve data integrity and efficiency.

Around 90% questions will be asked from sql in data analytics interview, so please make sure to practice SQL skills using websites like stratascratch. ☺️💪

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

Data Analytics :- https://pdlink.in/3Fq7E4p

Data Science :- https://pdlink.in/4iSWjaP

SQL :- https://pdlink.in/3EyjUPt

Python :- https://pdlink.in/4c7hGDL

Web Dev :- https://bit.ly/4ffFnJZ

AI :- https://pdlink.in/4d0SrTG

Enroll For FREE & Get Certified 🎓

Netflix Analytics Engineer Interview Experience:


SQL Questions:

1️⃣ SQL Question 1: Identify VIP Users for Netflix

Question: To better cater to its most dedicated users, Netflix would like to identify its “VIP users” - those who are most active in terms of the number of hours of content they watch. Write a SQL query that will retrieve the top 10 users with the most watched hours in the last month.

Tables:
• users table: user_id (integer), sign_up_date (date), subscription_type (text)
• watching_activity table: activity_id (integer), user_id (integer), date_time (timestamp), show_id (integer), hours_watched (float)

2️⃣ SQL Question 2: Analyzing Ratings For Netflix Shows

Question: Given a table of user ratings for Netflix shows, calculate the average rating for each show within a given month. Assume that there is a column for user_id, show_id, rating (out of 5 stars), and date of review. Order the results by month and then by average rating (descending order).

Tables:
• show_reviews table: review_id (integer), user_id (integer), review_date (timestamp), show_id (integer), stars (integer)

3️⃣ SQL Question 3: What does EXCEPT / MINUS SQL commands do?

Question: Explain the purpose and usage of the EXCEPT (or MINUS in some SQL dialects) SQL commands.

4️⃣ SQL Question 4: Filter Netflix Users Based on Viewing History and Subscription Status

Question: You are given a database of Netflix’s user viewing history and their current subscription status. Write a SQL query to find all active customers who watched more than 10 episodes of a show called “Stranger Things” in the last 30 days.

Tables:
• users table: user_id (integer), active (boolean)
• viewing_history table: user_id (integer), show_id (integer), episode_id (integer), watch_date (date)
• shows table: show_id (integer), show_name (text)

5️⃣ SQL Question 5: What does it mean to denormalize a database?

Question: Explain the concept and implications of denormalizing a database.

6️⃣ SQL Question 6: Filter and Match Customer’s Viewing Records

Question: As a data analyst at Netflix, you are asked to analyze the customer’s viewing records. You confirmed that Netflix is especially interested in customers who have been continuously watching a particular genre - ‘Documentary’ over the last month. The task is to find the name and email of those customers who have viewed more than five ‘Documentary’ movies within the last month. ‘Documentary’ could be a part of a broader genre category in the genre field (for example, ‘Documentary, History’). Therefore, the matching pattern could occur anywhere within the string.

Tables:
• movies table: movie_id (integer), title (text), genre (text), release_year (integer)
• customer table: user_id (integer), name (text), email (text), last_movie_watched (integer), date_watched (date)

Here you can find essential SQL Interview Resources👇
https://t.me/mysqldata

Like this post if you need more 👍❤️

Hope it helps :)

We have now reached 85K subscribers on WhatsApp

Thank you guys❤️

Do subscribe if you haven’t yet for

BEST DATA ENGINEERING CONTENT

https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗖𝗼𝗱𝗶𝗻𝗴 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Looking to kickstart your coding journey with Python? 🐍

Whether you’re an aspiring data analyst, a student, or preparing for tech roles, these free Python courses are perfect for beginners!📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4jtpf9M

These platforms offer high-quality learning — no fees, no catch✅️

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Google :- https://pdlink.in/3H2YJX7

Microsoft :- https://pdlink.in/4iq8QlM

Infosys :- https://pdlink.in/4jsHZXf

IBM :- https://pdlink.in/3QyJyqk

Cisco :- https://pdlink.in/4fYr1xO

Enroll For FREE & Get Certified 🎓

𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!

These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Hio2Vg

Enroll For FREE & Get Certified🎓️

𝗙𝗥𝗘𝗘 𝗧𝗔𝗧𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽😍

Gain Real-World Data Analytics Experience with TATA – 100% Free!

This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst — no experience required!

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3FyjDgp

Enroll For FREE & Get Certified🎓️

𝟰 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗙𝗿𝗲𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗔𝗜/𝗠𝗟 & 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 😍

Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners🚀

Learning tech doesn’t have to be overwhelming—especially when you have a roadmap to guide you!📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45wfx2V

Enjoy Learning ✅️

Data Analyst vs Data Engineer vs Data Scientist ✅

Skills required to become a Data Analyst 👇

- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.


Skills required to become a Data Engineer: 👇

- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.


Skills required to become a Data Scientist: 👇

- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.

Bonus Skills Across All Roles:

- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.me/DataSimplifier

Like this post for more content like this 👍♥️

Share with credits: https://t.me/sqlspecialist

Hope it helps :)

𝟴 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍

🎓 Learn Data Science for Free from the World’s Best Universities🚀

Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Hfpwjc

All The Best 👍

🔹 🔥 Pro Tips for Aspiring Data Engineers

1. Learn SQL deeply – it's still the foundation of everything
2. Understand data formats: JSON, Parquet, Avro, ORC
3. Master Apache Spark — it's everywhere
4. Learn to use Airflow for orchestrating workflows
5. Practice writing ETL pipelines — build your own mini data warehouse
6. Get comfortable with cloud platforms (start with AWS/GCP free tiers)
7. Version-control your work using Git + DVC for data versioning
8. Learn Docker & Kubernetes basics — modern data infra depends on it
9. Explore real-time processing: Kafka, Flink, and Spark Streaming
10. Follow best practices for data modeling — star/snowflake schemas, SCDs, etc

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗼𝗮𝗱𝗺𝗮𝗽😍

🎯 Want to Master Data Science in Just 3 Months?📊

Feeling overwhelmed by the sheer volume of resources and don’t know where to start? You’re not alone🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/43uHPrX

This FREE GitHub roadmap is a game-changer for anyone✅️

Learning and Practicing SQL: Resources and Platforms

1. https://sqlbolt.com/
2. https://sqlzoo.net/
3. https://www.codecademy.com/learn/learn-sql
4. https://www.w3schools.com/sql/
5. https://www.hackerrank.com/domains/sql
6. https://www.windowfunctions.com/
7. https://selectstarsql.com/
8. https://quip.com/2gwZArKuWk7W
9. https://leetcode.com/problemset/database/
10. http://thedatamonk.com/

🔍 Mastering Spark: 20 Interview Questions Demystified!

1️⃣ MapReduce vs. Spark: Learn how Spark achieves 100x faster performance compared to MapReduce.
2️⃣ RDD vs. DataFrame: Unravel the key differences between RDD and DataFrame, and discover what makes DataFrame unique.
3️⃣ DataFrame vs. Datasets: Delve into the distinctions between DataFrame and Datasets in Spark.
4️⃣ RDD Operations: Explore the various RDD operations that power Spark.
5️⃣ Narrow vs. Wide Transformations: Understand the differences between narrow and wide transformations in Spark.
6️⃣ Shared Variables: Discover the shared variables that facilitate distributed computing in Spark.
7️⃣ Persist vs. Cache: Differentiate between the persist and cache functionalities in Spark.
8️⃣ Spark Checkpointing: Learn about Spark checkpointing and how it differs from persisting to disk.
9️⃣ SparkSession vs. SparkContext: Understand the roles of SparkSession and SparkContext in Spark applications.
🔟 spark-submit Parameters: Explore the parameters to specify in the spark-submit command.
1️⃣1️⃣ Cluster Managers in Spark: Familiarize yourself with the different types of cluster managers available in Spark.
1️⃣2️⃣ Deploy Modes: Learn about the deploy modes in Spark and their significance.
1️⃣3️⃣ Executor vs. Executor Core: Distinguish between executor and executor core in the Spark ecosystem.
1️⃣4️⃣ Shuffling Concept: Gain insights into the shuffling concept in Spark and its importance.
1️⃣5️⃣ Number of Stages in Spark Job: Understand how to decide the number of stages created in a Spark job.
1️⃣6️⃣ Spark Job Execution Internals: Get a peek into how Spark internally executes a program.
1️⃣7️⃣ Direct Output Storage: Explore the possibility of directly storing output without sending it back to the driver.
1️⃣8️⃣ Coalesce and Repartition: Learn about the applications of coalesce and repartition in Spark.
1️⃣9️⃣ Physical and Logical Plan Optimization: Uncover the optimization techniques employed in Spark's physical and logical plans.
2️⃣0️⃣ Treereduce and Treeaggregate: Discover why treereduce and treeaggregate are preferred over reduceByKey and aggregateByKey in certain scenarios.

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

𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍

𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇

S&P Global :- https://pdlink.in/3ZddwVz

IBM :- https://pdlink.in/4kDmMKE

TVS Credit :- https://pdlink.in/4mI0JVc

Sutherland :- https://pdlink.in/4mGYBgg

Other Jobs :- https://pdlink.in/44qEIDu

Apply before the link expires 💫

𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to Boost Your Resume with In-Demand Python Skills?👨‍💻

In today’s tech-driven world, Python is one of the most in-demand programming languages across data science, software development, and machine learning📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Hnx3wh

Enjoy Learning ✅️