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

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.iss.one/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.

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.

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

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.

📘 SQL Challenges for Data Analytics – With Explanation 🧠

(Beginner ➡️ Advanced)

1️⃣ Select Specific Columns

SELECT name, email FROM users;

This fetches only the 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;

The 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;

Sorts all users based on 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;

Explanation:
- 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;

Groups data by 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;

Fetches user names along with order amounts by joining 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;

Like 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);

Finds users whose salary is above the average. The subquery calculates the average salary first.

✔️ Nested queries for dynamic filtering9️⃣ CASE Statementnt**

SELECT name,  
  CASE  
    WHEN age < 18 THEN 'Teen'  
    WHEN age <= 40 THEN 'Adult'  
    ELSE 'Senior'  
  END AS age_group  
FROM users;

Adds a new column that classifies users into categories based on age.
✔️ Powerful for conditional logic.

🔟 Window Functions (Advanced)

SELECT name, city, score,  
  RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank  
FROM users;

Ranks users by score *within each city*.

SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075