𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

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Enjoy Learning ✅️

Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career.

📊Introduction to Data Engineering

✅Overview of Data Engineering & its importance
✅Key responsibilities & skills of a Data Engineer
✅Difference between Data Engineer, Data Scientist & Data Analyst
✅Data Engineering tools & technologies

📊Programming for Data Engineering

✅Python
✅SQL
✅Java/Scala
✅Shell scripting

📊Database System & Data Modeling

✅Relational Databases: design, normalization & indexing
✅NoSQL Databases: key-value stores, document stores, column-family stores & graph database
✅Data Modeling: conceptual, logical & physical data model
✅Database Management Systems & their administration

📊Data Warehousing and ETL Processes

✅Data Warehousing concepts: OLAP vs. OLTP, star schema & snowflake schema
✅ETL: designing, developing & managing ETL processe
✅Tools & technologies: Apache Airflow, Talend, Informatica, AWS Glue
✅Data lakes & modern data warehousing solution

📊Big Data Technologies

✅Hadoop ecosystem: HDFS, MapReduce, YARN
✅Apache Spark: core concepts, RDDs, DataFrames & SparkSQL
✅Kafka and real-time data processing
✅Data storage solutions: HBase, Cassandra, Amazon S3

📊Cloud Platforms & Services

✅Introduction to cloud platforms: AWS, Google Cloud Platform, Microsoft Azure
✅Cloud data services: Amazon Redshift, Google BigQuery, Azure Data Lake
✅Data storage & management on the cloud
✅Serverless computing & its applications in data engineering

📊Data Pipeline Orchestration

✅Workflow orchestration: Apache Airflow, Luigi, Prefect
✅Building & scheduling data pipelines
✅Monitoring & troubleshooting data pipelines
✅Ensuring data quality & consistency

📊Data Integration & API Development

✅Data integration techniques & best practices
✅API development: RESTful APIs, GraphQL
✅Tools for API development: Flask, FastAPI, Django
✅Consuming APIs & data from external sources

📊Data Governance & Security

✅Data governance frameworks & policies
✅Data security best practices
✅Compliance with data protection regulations
✅Implementing data auditing & lineage

📊Performance Optimization & Troubleshooting

✅Query optimization techniques
✅Database tuning & indexing
✅Managing & scaling data infrastructure
✅Troubleshooting common data engineering issues

📊Project Management & Collaboration

✅Agile methodologies & best practices
✅Version control systems: Git & GitHub
✅Collaboration tools: Jira, Confluence, Slack
✅Documentation & reporting

Resources for Data Engineering
1️⃣Python: https://t.me/pythonanalyst

2️⃣SQL: https://t.me/sqlanalyst

3️⃣Excel: https://t.me/excel_analyst

4️⃣Free DE Courses: https://t.me/free4unow_backup/569

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

All the best 👍👍

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Complete topics & subtopics of #SQL for Data Engineer role:-

𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅:
SQL keywords
Data types
Operators
SQL statements (SELECT, INSERT, UPDATE, DELETE)

𝟮. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗗𝗟):
CREATE TABLE
ALTER TABLE
DROP TABLE
Truncate table

𝟯. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗠𝗟):
SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs)
INSERT statement
UPDATE statement
DELETE statement

𝟰. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
SUM, AVG, COUNT, MIN, MAX
GROUP BY clause
HAVING clause

𝟱. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀:
Primary Key
Foreign Key
Unique
NOT NULL
CHECK

𝟲. 𝗝𝗼𝗶𝗻𝘀:
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
Self Join
Cross Join

𝟳. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀:
Types of subqueries (scalar, column, row, table)
Nested subqueries
Correlated subqueries

𝟴. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER)
Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD)
Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD)
Conditional functions (CASE, COALESCE, NULLIF)

𝟵. 𝗩𝗶𝗲𝘄𝘀:
Creating views
Modifying views
Dropping views

𝟭𝟬. 𝗜𝗻𝗱𝗲𝘅𝗲𝘀:
Creating indexes
Using indexes for query optimization

𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀:
ACID properties
Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT)
Transaction isolation levels

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:
Data integrity constraints (referential integrity, entity integrity)
GRANT and REVOKE statements (granting and revoking permissions)
Database security best practices

𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
Creating stored procedures
Executing stored procedures
Creating functions
Using functions in queries

𝟭𝟰. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
Query optimization techniques (using indexes, optimizing joins, reducing subqueries)
Performance tuning best practices

𝟭𝟱. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Recursive queries
Pivot and unpivot operations
Window functions (Row_number, rank, dense_rank, lead & lag)
CTEs (Common Table Expressions)
Dynamic SQL

Here you can find quick SQL Revision Notes👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

Like for more

Hope it helps :)

𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍

- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/3E2wYNr

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FREE RESOURCES TO LEARN DATA ENGINEERING
👇👇

Big Data and Hadoop Essentials free course

https://bit.ly/3rLxbul

Data Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE
[4.6 stars out of 5]

https://bit.ly/3fGRjLu

Understanding Data Engineering from Datacamp

https://clnk.in/soLY

Data Engineering Free Books

https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf

https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf

Big Data of Data Engineering Free book

https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf

https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf

The Data Engineer’s Guide to Apache Spark

https://t.me/datasciencefun/783?single

Data Engineering with Python

https://t.me/pythondevelopersindia/343

Data Engineering Projects -

1.End-To-End From Web Scraping to Tableau  https://lnkd.in/ePMw63ge

2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J

3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq

4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3

5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR

6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD

7. YouTube Data Analysis 
   (End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF

8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY

9. Sentiment analysis Twitter:
    Kafka and Spark Structured Streaming -  https://lnkd.in/esVAaqtU

ENJOY LEARNING 👍👍

𝟯 𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍

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Planning for Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.


𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

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

All the best 👍👍

Discussion Group: https://t.me/freshersgrp

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿?

Here is a complete week-by-week roadmap that can help

𝗪𝗲𝗲𝗸 𝟭: Learn programming - Python for data manipulation, and Java for big data frameworks.

𝗪𝗲𝗲𝗸 𝟮-𝟯: Understand database concepts and databases like MongoDB.

𝗪𝗲𝗲𝗸 𝟰-𝟲: Start with data warehousing (ETL), Big Data (Hadoop) and Data pipelines (Apache AirFlow)

𝗪𝗲𝗲𝗸 𝟲-𝟴: Go for advanced topics like cloud computing and containerization (Docker).

𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬: Participate in Kaggle competitions, build projects and develop communication skills.

𝗪𝗲𝗲𝗸 𝟭𝟭: Create your resume, optimize your profiles on job portals, seek referrals and apply.

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

All the best 👍👍

𝟰 𝗙𝗥𝗘𝗘 𝗕𝗲𝘀𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝗮𝘃𝗮 𝗘𝗮𝘀𝗶𝗹𝘆 😍

Level up your Java skills without getting overwhelmed

All of them are absolutely free, designed by experienced educators and top tech creators

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Complete Data Engineering Roadmap to keep yourself in the hunt in job market.

1. I will Learn SQL
--variables, data types, Aggregate functions
-- Various joins, data analysis
-- data wrangling, operators like(union, intersect etc.)
--Advanced SQL(Regex, Having, PIVOT)
--Windowing functions, CTE
--finally performance optimizations.

2. I will learn Python...
-- Basic functions, constructors, Lists, Tuples, Dictionaries
-- Loops (IF, When, FOR), functional programming
-- Libraries like(Pandas, Numpy, scikit-learn etc)

3. Learn distributed computing...
--Hadoop versions/hadoop architecture
--fault tolerance in hadoop
--Read/understand about Mapreduce processing.
--learn optimizations used in mapreduce etc.

4. Learn data ingestion tools...
--Learn Sqoop/ Kafka/NIFi
--Understand their functionality and job running mechanism.

5. i ll Learn data processing/NOSQL....
--Spark architecture/ RDD/Dataframes/datasets.
--lazy evaluation, DAGs/ Lineage graph/optimization techniques
--YARN utilization/ spark streaming etc.

6. Learn data warehousing.....
--Understand how HIve store and process the data
--different File formats/ compression Techniques.
--partitioning/ Bucketing.
--different UDF's available in Hive.
--SCD concepts.
--Ex Hbase. cassandra

7. Learn job Orchestration...
--Learn Airflow/Oozie
--learn about workflow/ CRON etc.

8. Learn Cloud Computing....
--Learn Azure/AWS/ GCP.
--understand the significance of Cloud in #dataengineering
--Learn Azure synapse/Redshift/Big query
--Learn Ingestion tools/pipeline tools like ADF etc.

9. Learn basics of CI/ CD and Linux commands....
--Read about Kubernetes/Docker. And how crucial they are in data.
--Learn about basic commands like copy data/export in Linux.

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

Like if you need similar content 😄👍

Hope this helps you 😊

𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to build your tech career without breaking the bank?💰

These 3 completely free courses are all you need to begin your journey in programming and data analysis📊

𝐋𝐢𝐧𝐤👇:-

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Learn at your own pace, sharpen your skills, and showcase your progress on LinkedIn or your resume. Let’s dive in!✅️

10 Data Engineering Projects to build your portfolio.

1. Olympic Data Analytics using Azure
https://lnkd.in/gHNyz_Bg

2. Uber Data Analytics using GCP.
https://lnkd.in/gqE-Y4HS

3. Stock Market Real-time Data Analysis using Kafka
https://lnkd.in/gknh7ZEr

4. Twitter Data Pipeline using Airflow
https://lnkd.in/g7YPnH7G

5. Smart City End to End project using AWS
https://lnkd.in/gh2eWF66

6. Realtime Data Streaming using spark and Kafka
https://lnkd.in/gjH2efgz

7. Zillow Data Analytics - Python, ETL
https://lnkd.in/gvEVZHPR

8. End to end Azure Project
https://lnkd.in/gCVZtNB5

9. End to end project using snowlake
https://lnkd.in/g96n6NbA

10. Data pipeline using Data Fusion
https://lnkd.in/gR5pkeRw

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

Hope this helps you 😊

If you've read so far, do LIKE the post👍

𝗔𝗜 & 𝗠𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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Enroll Now & Get Certified 🎓

Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Python Interview Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Join for more: https://t.me/datasciencefun

ENJOY LEARNING 👍👍

𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍

JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills. 

- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
 
𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/4gHGofl

Enroll For FREE & Get Certified 🎓

10 Data Engineering architectures asked in Interviews.

1. Hadoop
2. Hive
3. Hbase
4. Kafka
5. Spark
6. Airflow
7. Bigquery
8. Snowflake
9. Databricks
10. MongoDB

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

All the best 👍👍

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Complete topics & subtopics of #SQL for Data Engineer role:-

𝟭. 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 𝗦𝘆𝗻𝘁𝗮𝘅:
SQL keywords
Data types
Operators
SQL statements (SELECT, INSERT, UPDATE, DELETE)

𝟮. 𝗗𝗮𝘁𝗮 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗗𝗟):
CREATE TABLE
ALTER TABLE
DROP TABLE
Truncate table

𝟯. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (𝗗𝗠𝗟):
SELECT statement (SELECT, FROM, WHERE, ORDER BY, GROUP BY, HAVING, JOINs)
INSERT statement
UPDATE statement
DELETE statement

𝟰. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
SUM, AVG, COUNT, MIN, MAX
GROUP BY clause
HAVING clause

𝟱. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀:
Primary Key
Foreign Key
Unique
NOT NULL
CHECK

𝟲. 𝗝𝗼𝗶𝗻𝘀:
INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL OUTER JOIN
Self Join
Cross Join

𝟳. 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀:
Types of subqueries (scalar, column, row, table)
Nested subqueries
Correlated subqueries

𝟴. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
String functions (CONCAT, LENGTH, SUBSTRING, REPLACE, UPPER, LOWER)
Date and time functions (DATE, TIME, TIMESTAMP, DATEPART, DATEADD)
Numeric functions (ROUND, CEILING, FLOOR, ABS, MOD)
Conditional functions (CASE, COALESCE, NULLIF)

𝟵. 𝗩𝗶𝗲𝘄𝘀:
Creating views
Modifying views
Dropping views

𝟭𝟬. 𝗜𝗻𝗱𝗲𝘅𝗲𝘀:
Creating indexes
Using indexes for query optimization

𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀:
ACID properties
Transaction management (BEGIN, COMMIT, ROLLBACK, SAVEPOINT)
Transaction isolation levels

𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:
Data integrity constraints (referential integrity, entity integrity)
GRANT and REVOKE statements (granting and revoking permissions)
Database security best practices

𝟭𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀:
Creating stored procedures
Executing stored procedures
Creating functions
Using functions in queries

𝟭𝟰. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
Query optimization techniques (using indexes, optimizing joins, reducing subqueries)
Performance tuning best practices

𝟭𝟱. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Recursive queries
Pivot and unpivot operations
Window functions (Row_number, rank, dense_rank, lead & lag)
CTEs (Common Table Expressions)
Dynamic SQL

Here you can find quick SQL Revision Notes👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

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