Here’s a detailed breakdown of critical roles and their associated responsibilities:


🔘 Data Engineer: Tailored for Data Enthusiasts

1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.

🔘 Data Scientist: Suited for Analytical Minds

6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.

🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts

9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.

🔘 ML Engineer: Tailored for Deployment Enthusiasts

15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.

These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤

What it is: A powerful open-source platform designed to automate deploying, scaling, and operating application containers.

𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭:
- Organizes containers into groups for easier management.
- Automates tasks like scaling and load balancing.

𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐑𝐮𝐧𝐭𝐢𝐦𝐞:
- Software responsible for launching and managing containers.
- Ensures containers run efficiently and securely.

𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲:
- Implements measures to protect against unauthorized access and malicious activities.
- Includes features like role-based access control and encryption.

𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲:
- Tools to monitor system health, performance, and resource usage.
- Helps identify and troubleshoot issues quickly.

𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠:
- Manages network communication between containers and external systems.
- Ensures connectivity and security between different parts of the system.

𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬:
- Handles tasks related to the underlying infrastructure, such as provisioning and scaling.
- Automates repetitive tasks to streamline operations and improve efficiency.

- 𝐊𝐞𝐲 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬:
- Cluster Management: Handles grouping and managing multiple containers.
- Container Runtime: Software that runs containers and manages their lifecycle.
- Security: Implements measures to protect containers and the overall system.
- Monitoring & Observability: Tools to track and understand system behavior and performance.
- Networking: Manages communication between containers and external networks.
- Infrastructure Operations: Handles tasks like provisioning, scaling, and maintaining the underlying infrastructure.

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Netflix Analytics Engineer Interview Question (SQL) 🚀
---

### Scenario Overview
Netflix wants to analyze user engagement with their platform. Imagine you have a table called netflix_data with the following columns:
- user_id: Unique identifier for each user
- subnoscription_plan: Type of subnoscription (e.g., Basic, Standard, Premium)
- genre: Genre of the content the user watched (e.g., Drama, Comedy, Action)
- timestamp: Date and time when the user watched a show
- watch_duration: Length of time (in minutes) a user spent watching
- country: User’s country

The main objective is to figure out how to get insights into user behavior, such as which genres are most popular or how watch duration varies across subnoscription plans.

---

### Typical Interview Question

> “Using the netflix_data table, find the top 3 genres by average watch duration in each subnoscription plan, and return both the genre and the average watch duration.”

This question tests your ability to:
1. Filter or group data by subnoscription plan.
2. Calculate average watch duration within each group.
3. Sort results to find the “top 3” within each group.
4. Handle tie situations or edge cases (e.g., if there are fewer than 3 genres).

---

### Step-by-Step Approach

1. Group and Aggregate
Use the GROUP BY clause to group by subnoscription_plan and genre. Then, use an aggregate function like AVG(watch_duration) to get the average watch time for each combination.

2. Rank Genres
You can utilize a window function—commonly ROW_NUMBER() or RANK()—to assign a ranking to each genre within its subnoscription plan, based on the average watch duration. For example:

   AVG(watch_duration) OVER (PARTITION BY subnoscription_plan ORDER BY AVG(watch_duration) DESC)
   

(Note that in many SQL dialects, you’ll need a subquery because you can’t directly apply an aggregate in the ORDER BY of a window function.)

3. Select Top 3
After ranking rows in each partition (i.e., subnoscription plan), pick only the top 3 by watch duration. This could look like:

   SELECT subnoscription_plan,
          genre,
          avg_watch_duration
   FROM (
       SELECT subnoscription_plan,
              genre,
              AVG(watch_duration) AS avg_watch_duration,
              ROW_NUMBER() OVER (
                  PARTITION BY subnoscription_plan 
                  ORDER BY AVG(watch_duration) DESC
              ) AS rn
       FROM netflix_data
       GROUP BY subnoscription_plan, genre
   ) ranked
   WHERE rn <= 3;
   

4. Validate Results
- Make sure each subnoscription plan returns up to 3 genres.
- Check for potential ties. Depending on the question, you might use RANK() or DENSE_RANK() to handle ties differently.
- Confirm the data type and units for watch_duration (minutes, seconds, etc.).

---

### Key Takeaways
- Window Functions: Essential for ranking or partitioning data.
- Aggregations & Grouping: A foundational concept for Analytics Engineers.
- Data Validation: Always confirm you’re interpreting columns (like watch_duration) correctly.

By mastering these techniques, you’ll be better prepared for SQL interview questions that delve into real-world scenarios—especially at a data-driven company like Netflix.