📝 Interview Tip of the Day!

💡 Know the Basics of SQL: Expect SQL questions on joins, group by, and subqueries.

✅ Pro Tip: Practice writing clean, efficient SQL code.

🗣 Prepare: Be ready to walk through SQL logic verbally!

What is CRUD?

CRUD stands for Create, Read, Update, and Delete. It represents the basic operations that can be performed on data in a database.

Examples in SQL:

1. Create:
Adding new records to a table.

    INSERT INTO students (id, name, age)
    VALUES (1, 'John Doe', 20);
    
    

2. Read:
Retrieving data from a table.

    SELECT * FROM students;
    
    

3. Update:

Modifying existing records.

    UPDATE students
    SET age = 21
    WHERE id = 1;
    
    

4. Delete:

Removing records.

DELETE FROM students
WHERE id = 1;

⏰ MySQL Data Types

MySQL provides a variety of data types to store different kinds of data. These are categorized into three main groups:

1. Numeric Data Types:
- INT, BIGINT, SMALLINT, TINYINT: For whole numbers.
- DECIMAL, FLOAT, DOUBLE: For real numbers with decimal points.
- BIT: For binary values.
- Example:

            CREATE TABLE numeric_example (
                id INT,
                amount DECIMAL(10, 2)
            );
            
            

1. String Data Types:
- CHAR, VARCHAR: For fixed and variable-length strings.
- TEXT: For large text.
- BLOB: For binary large objects like images.
- Example:

            CREATE TABLE string_example (
                name VARCHAR(100),
                description TEXT
            );
            
            

1. Date and Time Data Types:
- DATE, DATETIME, TIMESTAMP: For date and time values.
- YEAR: For storing a year.
- Example:

                CREATE TABLE datetime_example (
                    created_at DATETIME,
                    year_of_joining YEAR
                );
                
                

Interview Questions:

- Q1: What is the difference between CHAR and VARCHAR?
A1: CHAR has a fixed length, while VARCHAR has a variable length. VARCHAR is more storage-efficient for varying-length data.
- Q2: When should you use DECIMAL instead of FLOAT?
A2: Use DECIMAL for precise calculations (e.g., financial data) and FLOAT for approximate values where precision is less critical.

📊 How to Present Data Projects Effectively!

💡 Start with a Clear Objective: Clearly define the purpose of your presentation at the outset to set expectations and context.

✅ Pro Tip: A strong opening statement can grab your audience's attention right away!

🔍 Quick Note of the Day!

💡 Python: Basic Data Types

Familiarize yourself with basic data types in Python: integers, floats, strings, and booleans.

✅ Pro Tip: Understanding data types is crucial for effective data manipulation!

⏰ VIEWS in SQL

Definition

A view is a virtual table based on the result of a SELECT query.

Features

- Does not store data; it retrieves data from underlying tables.
- Simplifies complex queries.

Syntax

CREATE VIEW view_name AS
SELECT columns
FROM table_name
WHERE condition;

Example

Create a view to show high-salaried employees:

CREATE VIEW HighSalaryEmployees AS
SELECT name, salary
FROM employees
WHERE salary > 100000;

Use the view:

SELECT * FROM HighSalaryEmployees;

Interview Questions

1. What is the difference between a table and a view?
- A table stores data physically; a view does not.
2. Can you update data through a view?
- Yes, if the view is updatable (no joins, no aggregate functions, etc.).
3. What are the advantages of using views?
- Simplifies complex queries, enhances security, and provides abstraction.

During Interview: Spark, Hadoop, Kafka, Airflow, SQL, Python, Azure, Data Modeling, etc..

Actual Job: Mostly filtering data with SQL and writing ETL scripts

Still we have to keep up-skill because competition is growing and now in-depth knowledge is in demand.

🎯 Master the Math & Stats for Data Engineering Success!

📊 Mathematics and statistics are the backbone of data analytics, powering pattern recognition, predictions, and problem-solving in interviews. Let’s make your prep easy and effective!

💡 Why it Matters?

Key concepts ensure precision and help you tackle complex analytical challenges like a pro.

📚 Syllabus Snapshot

🔢 Basic Statistics:

✅ Mean, Median, Mode

✅ Standard Deviation & Variance

✅ Normal Distribution

✅ Percentile & Quintiles

✅ Correlation & Regression Analysis

➕ Basic Math:

✅ Arithmetic (Sum, Subtraction, Division, Multiplication)

✅ Probability, Percentages & Ratios

✅ Weighted Average & Cumulative Sum

✅ Linear Equations & Matrices

✨ Quick Tip: Focus on these concepts, and you'll ace any data analytics interview!

📌 Save this post & start practicing today!

#MathForData #StatisticsForData #DataInterviewTips

Data Engineering Top Interview Question.pdf

#dataengineering

SQL Essentials for Quick Revision

🚀 SELECT
Retrieve data from one or more tables.

🎯 WHERE Clause
Filter records based on specific conditions.

🔄 ORDER BY
Sort query results in ascending (ASC) or descending (DESC) order.

📊 Aggregation Functions

MIN, MAX, AVG, COUNT: Summarize data.

Window Functions: Perform calculations across a dataset without grouping rows.


🔑 GROUP BY
Group data based on one or more columns and apply aggregate functions.

🔗 JOINS

INNER JOIN: Fetch matching rows from both tables.

LEFT JOIN: All rows from the left table and matching rows from the right.

RIGHT JOIN: All rows from the right table and matching rows from the left.

FULL JOIN: Combine rows when there is a match in either table.

SELF JOIN: Join a table with itself.


🧩 Common Table Expressions (CTE)
Simplify complex queries with temporary result sets.

Quick SQL Revision Notes 📌
Master these concepts for interviews and projects!

#SQL #DataEngineer #QuickNotes

SQL vs Pyspark.pdf

🚀 Master SQL for Data Engineer and Ace Interviews

To succeed as a Data Analyst, focus on these essential SQL topics:

1️⃣ Fundamental SQL Commands
SELECT, FROM, WHERE

GROUP BY, HAVING, LIMIT


2️⃣ Advanced Querying Techniques
Joins: LEFT, RIGHT, INNER, SELF, CROSS

Aggregate Functions: SUM(), MAX(), MIN(), AVG()

Window Functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG(), SUM() OVER()

Conditional Logic & Pattern Matching:

CASE statements for conditions

LIKE for pattern matching


Complex Queries: Subqueries, Common Table Expressions (CTEs), temporary tables


3️⃣ Performance Tuning
Optimize queries for better performance

Learn indexing strategies


4️⃣ Practical Applications
Solve case studies from Ankit Bansal's YouTube channel

Watch 10-15 minute tutorials, practice along for hands-on learning


5️⃣ End-to-End Projects
Search "Data Analysis End-to-End Projects Using SQL" on YouTube

Practice the full process: data extraction ➡️ cleaning ➡️ analysis


6️⃣ Real-World Data Analysis
Analyze real datasets for insights

Practice cleaning, handling missing values, and dealing with outliers


7️⃣ Advanced Data Manipulation
Use advanced SQL functions for transforming raw data into insights

Practice combining data from multiple sources


8️⃣ Reporting & Dashboards
Build impactful reports and dashboards using SQL and Power BI


9️⃣ Interview Preparation
Practice common SQL interview questions

Solve exercises and coding challenges


🔑 Pro Tip: Hands-on practice is key! Apply these steps to real projects and datasets to strengthen your expertise and confidence.

#SQL #DataEngineer #CareerGrowth

🔥 Working with Intersect and Except in SQL

When dealing with datasets in SQL, you often need to find common records in two tables or determine the differences between them. For these purposes, SQL provides two useful operators: INTERSECT and EXCEPT. Let’s take a closer look at how they work.

🔻 The INTERSECT Operator
The INTERSECT operator is used to find rows that are present in both queries. It works like the intersection of sets in mathematics, returning only those records that exist in both datasets.

Example:

SELECT column1, column2
FROM table1
INTERSECT
SELECT column1, column2
FROM table2;

This will return rows that appear in both table1 and table2.

Key Points:
- The INTERSECT operator automatically removes duplicate rows from the result.
- The selected columns must have compatible data types.

🔻 The EXCEPT Operator
The EXCEPT operator is used to find rows that are present in the first query but not in the second. This is similar to the difference between sets, returning only those records that exist in the first dataset but are missing from the second.

Example:

SELECT column1, column2
FROM table1
EXCEPT
SELECT column1, column2
FROM table2;

Here, the result will include rows that are in table1 but not in table2.

Key Points:
- The EXCEPT operator also removes duplicate rows from the result.
- As with INTERSECT, the columns must have compatible data types.

📊 What’s the Difference Between UNION, INTERSECT, and EXCEPT?
- UNION combines all rows from both queries, excluding duplicates.
- INTERSECT returns only the rows present in both queries.
- EXCEPT returns rows from the first query that are not found in the second.

📌 Real-Life Examples
1. Finding common customers. Use INTERSECT to identify customers who have made purchases both online and in physical stores.
2. Determining unique products. Use EXCEPT to find products that are sold in one store but not in another.

By using INTERSECT and EXCEPT, you can simplify data analysis and work more flexibly with sets, making it easier to solve tasks related to finding intersections and differences between datasets.

Happy querying!

Life of a Data Engineer.....


Business user : Can we add a filter on this dashboard. This will help us track a critical metric.
me : sure this should be a quick one.

Next day :

I quickly opened the dashboard to find the column in the existing dashboard's data sources. -- column not found

Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..).

Then comes the pipeline changes , data model changes , dashboard changes , validation/testing.

Finally deploying to production and a simple email to the user that the filter has been added.

A small change in the front end but a lot of work in the backend to bring that column to life.

Never underestimate data engineers and data pipelines 💪

Data Engineering Roadmap

Quick comparison

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SQL Mindmap