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

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FREE RESOURCES TO LEARN DATA ENGINEERING
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Understanding Data Engineering from Datacamp

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Data Engineering Free Books

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Big Data of Data Engineering Free book

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The Data Engineer’s Guide to Apache Spark

https://news.1rj.ru/str/datasciencefun/783

Data Engineering with Python

https://news.1rj.ru/str/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 👍👍

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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://news.1rj.ru/str/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 😍

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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😍

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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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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://news.1rj.ru/str/datasciencefun

ENJOY LEARNING 👍👍

𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍

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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 👍👍

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍

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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 :)

20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !!

𝐋𝐞𝐭𝐬 𝐝𝐢𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 4 𝐩𝐚𝐫𝐭𝐬

1. Data Processing and Transformation
2. Performance Tuning and Optimization
3. Data Pipeline Development
4. Debugging and Error Handling

𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧:

1. Explain how you would handle large datasets in PySpark. How do you optimize a PySpark job for performance?
2. How would you join two large datasets (say 100GB each) in PySpark efficiently?
3. Given a dataset with millions of records, how would you identify and remove duplicate rows using PySpark?
4. You are given a DataFrame with nested JSON. How would you flatten the JSON structure in PySpark?
5. How do you handle missing or null values in a DataFrame? What strategies would you use in different scenarios?

𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧:

6. How do you debug and optimize PySpark jobs that are taking too long to complete?
7. Explain what a shuffle operation is in PySpark and how you can minimize its impact on performance.
8. Describe a situation where you had to handle data skew in PySpark. What steps did you take?
9. How do you handle and optimize PySpark jobs in a YARN cluster environment?
10. Explain the difference between repartition() and coalesce() in PySpark. When would you use each?

𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭:

11. Describe how you would implement an ETL pipeline in PySpark for processing streaming data.
12. How do you ensure data consistency and fault tolerance in a PySpark job?
13. You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you do this in PySpark?
14. How would you orchestrate and manage a complex PySpark job with multiple stages?
15. Explain how you would handle schema evolution in PySpark while reading and writing data.

𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠:

16. Have you encountered out-of-memory errors in PySpark? How did you resolve them?
17. What steps would you take if a PySpark job fails midway through execution? How do you recover from it?
18. You encounter a Spark task that fails repeatedly due to data corruption in one of the partitions. How would you handle this?
19. Explain a situation where you used custom UDFs (User Defined Functions) in PySpark. What challenges did you face, and how did you overcome them?
20. Have you had to debug a PySpark (Python + Apache Spark) job that was producing incorrect results?

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

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