Here's what the average data engineering interview looks like:

- 1 hour algorithms in Python
Here you will be asked irrelevant questions about dynamic programming, linked lists, and inverting trees

- 1 hour SQL
Here you will be asked niche questions about recursive CTEs that you've used once in your ten year career

- 1 hour data architecture
Here you will be asked about CAP theorem, lambda vs kappa, and a bunch of other things that ChatGPT probably could answer in a heartbeat

- 1 hour behavioral
Here you will be asked about how to play nicely with your coworkers. This is the most relevant interview in my opinion

- 1 hour project deep dive
Here you will be asked to make up a story about something you did or did not do in the past that was a technical marvel

- 4 hour take home assignment
Here you will be asked to build their entire data engineering stack from scratch over a weekend because why hire data engineers when you can submit them to tests?

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

All the best 👍👍

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.

SQL Interview Resources: t.me/mysqldata

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.

Join for more: https://news.1rj.ru/str/datasciencefun

ENJOY LEARNING 👍👍

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱😍

Master industry-standard tools like Excel, SQL, Tableau, and more.

Gain hands-on experience through real-world projects designed to mimic professional challenges

𝗟𝗶𝗻𝗸👇 :- 

https://pdlink.in/4jxUW2K

All The Best 🎉

Learn This Concept to be proficient in PySpark.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- PySpark Architecture
- SparkContext and SparkSession
- RDDs (Resilient Distributed Datasets)
- DataFrames
- Transformations and Actions
- Lazy Evaluation

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Reading Data from CSV, JSON, Parquet
- DataFrame Operations
- Filtering, Selecting, and Aggregating Data
- Joins and Merging DataFrames
- Working with Null Values

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗖𝗼𝗹𝘂𝗺𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Defining and Using UDFs (User Defined Functions)
- Column Operations (Select, Rename, Drop)
- Handling Complex Data Types (Array, Map)
- Working with Dates and Timestamps

𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗵𝘂𝗳𝗳𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Understanding Partitions
- Repartitioning and Coalescing
- Managing Shuffle Operations
- Optimizing Partition Sizes for Performance

𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮:
- When to Cache or Persist
- Memory vs Disk Caching
- Checking Storage Levels

𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗪𝗶𝘁𝗵 𝗦𝗤𝗟:
- Spark SQL Introduction
- Creating Temp Views
- Running SQL Queries
- Optimizing SQL Queries with Catalyst Optimizer
- Working with Hive Tables in PySpark

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Data Cleaning and Preparation
- Handling Missing Values
- Data Normalization and Transformation
- Working with Categorical Data

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Broadcasting Variables
- Accumulators
- PySpark Window Functions
- PySpark with Machine Learning (MLlib)
- Working with Streaming Data (Spark Streaming)

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸:
- Understanding Job, Stage, and Task
- Tungsten Execution Engine
- Memory Management and Garbage Collection
- Tuning Parallelism
- Using Spark UI for Performance Monitoring

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

All the best 👍👍

𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁!😍

Want to break into Data Analytics but don’t know where to start?

Follow this step-by-step roadmap to build real-world skills! ✅

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3CHqZg7

🎯 Start today & build a strong career in Data Analytics! 🚀

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.

𝗙𝗿𝗲𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗕𝘆 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍

- JP Morgan 
- Accenture
- Walmart
- Tata Group
- Accenture

𝗟𝗶𝗻𝗸 👇:-

https://pdlink.in/3WTGGI8

Enroll For FREE & Get Certified🎓

ChatGPT Prompt to learn any skill
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information

(Tap on above text to copy)

𝟱 𝗠𝘂𝘀𝘁-𝗗𝗼 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀!😍

If you’re aiming for a Data Analyst, Business Analyst, or Data Scientist role, mastering SQL is non-negotiable. 📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4aUoeER

Don’t just learn SQL—apply it with real-world projects!✅️

Complete Python topics required for the Data Engineer role:

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:

- Python Syntax
- Data Types
- Lists
- Tuples
- Dictionaries
- Sets
- Variables
- Operators
- Control Structures:
- if-elif-else
- Loops
- Break & Continue try-except block
- Functions
- Modules & Packages

➤ 𝗣𝗮𝗻𝗱𝗮𝘀:

- What is Pandas & imports?
- Pandas Data Structures (Series, DataFrame, Index)
- Working with DataFrames:
-> Creating DFs
-> Accessing Data in DFs Filtering & Selecting Data
-> Adding & Removing Columns
-> Merging & Joining in DFs
-> Grouping and Aggregating Data
-> Pivot Tables

- Input/Output Operations with Pandas:
-> Reading & Writing CSV Files
-> Reading & Writing Excel Files
-> Reading & Writing SQL Databases
-> Reading & Writing JSON Files
-> Reading & Writing - Text & Binary Files

➤ 𝗡𝘂𝗺𝗽𝘆:

- What is NumPy & imports?
- NumPy Arrays
- NumPy Array Operations:
- Creating Arrays
- Accessing Array Elements
- Slicing & Indexing
- Reshaping, Combining & Arrays
- Arithmetic Operations
- Broadcasting
- Mathematical Functions
- Statistical Functions

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗡𝘂𝗺𝗽𝘆 are more than enough for Data Engineer role.

All the best 👍👍

Understanding ETL Data Pipelines.pdf

𝐒𝐞𝐜𝐨𝐧𝐝 𝐫𝐨𝐮𝐧𝐝 𝐨𝐟 𝐂𝐚𝐩𝐠𝐞𝐦𝐢𝐧𝐢 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
:
:
:
1. Describe your work experience.
2. Provide a detailed explanation of a project, including the data sources, file formats, and methods for file reading.
3. Discuss the transformation techniques you have utilized, offering an example and explanation.
4. Explain the process of reading web API data in Spark, including detailed code explanation.
5. How do you convert lists into data frames?
6. What is the method for reading JSON files in Spark?
7. How do you handle complex data? When is it appropriate to use the "explode" function?
8. How do you determine the continuation of a process and identify necessary transformations for complex data?
9. What actions do you take if a Spark job fails? How do you troubleshoot and find a solution?
10. How do you address performance issues? Explain a scenario where a job is slow and how you would diagnose and resolve it.
11. Given a dataframe with a "department" column, explain how you would add a new employee to a department, specifying their salary and increment.
12. Explain the scenario for finding the highest salary using SQL.
13. If you have three data frames, write SQL queries to join them based on a common column.
14. When is it appropriate to use partitioning or bucketing in Spark? How do you determine when to use each technique? How do you assess cardinality?
15. How do you check for improper memory allocation?

Data Engineering Tools

𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗖𝗮𝗻 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗚𝗲𝘁 𝗬𝗼𝘂 𝗛𝗶𝗿𝗲𝗱!😍

Want to land a Data Analyst or SQL-based job?

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4hCYob9

🚀 Start working on these projects today & boost your SQL skills! 💻

Interview questions for Data Architect and Data Engineer positions:

Design and Architecture


1.⁠ ⁠Design a data warehouse architecture for a retail company.
2.⁠ ⁠How would you approach data governance in a large organization?
3.⁠ ⁠Describe a data lake architecture and its benefits.
4.⁠ ⁠How do you ensure data quality and integrity in a data warehouse?
5.⁠ ⁠Design a data mart for a specific business domain (e.g., finance, healthcare).


Data Modeling and Database Design


1.⁠ ⁠Explain the differences between relational and NoSQL databases.
2.⁠ ⁠Design a database schema for a specific use case (e.g., e-commerce, social media).
3.⁠ ⁠How do you approach data normalization and denormalization?
4.⁠ ⁠Describe entity-relationship modeling and its importance.
5.⁠ ⁠How do you optimize database performance?


Data Security and Compliance


1.⁠ ⁠Describe data encryption methods and their applications.
2.⁠ ⁠How do you ensure data privacy and confidentiality?
3.⁠ ⁠Explain GDPR and its implications on data architecture.
4.⁠ ⁠Describe access control mechanisms for data systems.
5.⁠ ⁠How do you handle data breaches and incidents?


Data Engineer Interview Questions!!


Data Processing and Pipelines


1.⁠ ⁠Explain the concepts of batch processing and stream processing.
2.⁠ ⁠Design a data pipeline using Apache Beam or Apache Spark.
3.⁠ ⁠How do you handle data integration from multiple sources?
4.⁠ ⁠Describe data transformation techniques (e.g., ETL, ELT).
5.⁠ ⁠How do you optimize data processing performance?


Big Data Technologies


1.⁠ ⁠Explain Hadoop ecosystem and its components.
2.⁠ ⁠Describe Spark RDD, DataFrame, and Dataset.
3.⁠ ⁠How do you use NoSQL databases (e.g., MongoDB, Cassandra)?
4.⁠ ⁠Explain cloud-based big data platforms (e.g., AWS, GCP, Azure).
5.⁠ ⁠Describe containerization using Docker.


Data Storage and Retrieval


1.⁠ ⁠Explain data warehousing concepts (e.g., fact tables, dimension tables).
2.⁠ ⁠Describe column-store and row-store databases.
3.⁠ ⁠How do you optimize data storage for query performance?
4.⁠ ⁠Explain data caching mechanisms.
5.⁠ ⁠Describe graph databases and their applications.


Behavioral and Soft Skills


1.⁠ ⁠Can you describe a project you led and the challenges you faced?
2.⁠ ⁠How do you collaborate with cross-functional teams?
3.⁠ ⁠Explain your experience with Agile development methodologies.
4.⁠ ⁠Describe your approach to troubleshooting complex data issues.
5.⁠ ⁠How do you stay up-to-date with industry trends and technologies?


Additional Tips


1.⁠ ⁠Review the company's technology stack and be prepared to discuss relevant tools and technologies.
2.⁠ ⁠Practice whiteboarding exercises to improve your design and problem-solving skills.
3.⁠ ⁠Prepare examples of your experience with data architecture and engineering concepts.
4.⁠ ⁠Demonstrate your ability to communicate complex technical concepts to non-technical stakeholders.
5.⁠ ⁠Show enthusiasm and passion for data architecture and engineering.

𝗣𝗪𝗖 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 (𝗗𝗮𝘁𝗮 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫) ⭐→

The whole interview process had 3 rounds of 1 hour each.

🔸 The first round was an extensive discussion about the projects I was handling and a few coding questions on SQL & Python.

There were questions like the following:
→ Optimisation techniques used in projects; Issues faced in the project; Hadoop questions.

🔸 After clearing this round, I moved on to the next round, which was a Case-Study based round.

I was asked scenario-based questions & the interviewer asked multiple questions on Spark, like:
→ Spark job process; Optimizations of spark; Sqoop interview questions.

After this, I was asked a few Coding questions & SQL coding questions, which I successfully answered.

🔸 Lastly, there was a Managerial Round where I was asked a lot of technical and advanced questions like:
→ Architecture of spark, hive, Hadoop; Overview of MapReduce job process; Joins to use in spark; Broadcast join & lastly Different joins available.

𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗳𝗿𝗼𝗺 𝗚𝗹𝗼𝗯𝗮𝗹 𝗚𝗶𝗮𝗻𝘁𝘀!😍

Want real-world experience in 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜?

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4hZlkAW

🔗 Save & share this post with someone who needs it!

Data engineering Interview questions: Accenture


Q1.Which Integration Runtime (IR) should be used for copying data from an on-premise database to Azure?

Q2.Explain the differences between a Scheduled Trigger and a Tumbling Window Trigger in Azure Data Factory. When would you use each?

Q3. What is Azure Data Factory (ADF), and how does it enable ETL and ELT processes in a cloud environment?

Q4.Describe Azure Data Lake and its role in a data architecture. How does it differ from Azure Blob Storage?

Q5. What is an index in a database table? Discuss different types of indexes and their impact on query performance.

Q6.Given two datasets, explain how the number of records will vary for each type of join (Inner Join, Left Join, Right Join, Full Outer Join).

Q7.What are the Control Flow activities in the Azure Data Factory? Explain how they differ from Data Flow activities and their typical use cases.

Q8. Discuss key concepts in data modeling, including normalization and denormalization. How do security concerns influence your choice of Synapse table types in a given scenario? Provide an example of a scenario-based ADF pipeline.

Q9. What are the different types of Integration Runtimes (IR) in Azure Data Factory? Discuss their use cases and limitations.

Q10.How can you mask sensitive data in the Azure SQL Database? What are the different masking techniques available?

Q11.What is Azure Integration Runtime (IR), and how does it support data movement across different networks?

Q12.Explain Slowly Changing Dimension (SCD) Type 1 in a data warehouse. How does it differ from SCD Type 2?

Q13.SQL questions on window functions - rolling sum and lag/lead based. How do window functions differ from traditional aggregate functions?

𝗙𝗥𝗘𝗘 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

1)Business Analysis – Foundation
2)Business Analysis Fundamentals
3)The Essentials of Business & Risk Analysis 
4)Master Microsoft Power BI 

𝗟𝗶𝗻𝗸 👇:-

https://pdlink.in/4hHxBdW

Enroll For FREE & Get Certified🎓

Two Commonly Asked Pyspark Inrerview Questions!!:


Scenario 1: Handling Missing Values


Interviewer: "How would you handle missing values in a PySpark DataFrame?"


Candidate:

from pyspark.sql.functions import when, isnan

# Load the DataFrame
df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

# Check for missing values
missing_count = df.select([count(when(isnan(c), c)).alias(c) for c in df.columns])

# Replace missing values with mean
from pyspark.sql.functions import mean
mean_values = df.agg(*[mean(c).alias(c) for c in df.columns])
df_filled = df.fillna(mean_values)

# Save the cleaned DataFrame
df_filled.write.csv("path/to/cleaned/data.csv", header=True)

Interviewer: "That's correct! Can you explain why you used the fillna() method?"


Candidate: "Yes, fillna() replaces missing values with the specified value, in this case, the mean of each column."


*Scenario 2: Data Aggregation*


Interviewer: "How would you aggregate data by category and calculate the average sales amount?"


Candidate:

# Load the DataFrame
df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

# Aggregate data by category
from pyspark.sql.functions import avg
df_aggregated = df.groupBy("category").agg(avg("sales").alias("avg_sales"))

# Sort the results
df_aggregated_sorted = df_aggregated.orderBy("avg_sales", ascending=False)

# Save the aggregated DataFrame
df_aggregated_sorted.write.csv("path/to/aggregated/data.csv", header=True)

Interviewer: "Great answer! Can you explain why you used the groupBy() method?"


Candidate: "Yes, groupBy() groups the data by the specified column, in this case, 'category', allowing us to perform aggregation operations."

𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 – 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲!😍

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