Data Engineering Interview coming up? This may help you
๐ Tech Round 1
โข DSA (Arrays, Strings): 1- 2 questions (easy to medium level)
โข SQL: Answered 3-5 SQL questions, working with complex queries.
โข Spark Fundamentals: Discussed core concepts of Apache Spark, including its role in big data processing.
๐ Tech Round 2
โข DSA (Arrays, Stack): Worked on problems related to arrays and stack, demonstrating my algorithmic thinking and problem-solving skills.
โข SQL: Tackled advanced SQL queries, focusing on query optimization and data manipulation techniques.
โข Spark Internals: Delved into Spark's internal workings and how it scales for large datasets.
๐ Hiring Manager Round
โข Data Modeling: Designed a data model for Uber and discussed approaches to managing real-world scenarios.
โข Team Dynamics & Project Management: Engaged in scenario-based questions, showcasing my understanding of team collaboration and project management.
โข Previous Project Experiences: Highlighted my contributions, challenges faced, and the impact of my work in past projects.
๐ HR Round
โข Work Culture: Discussed salary, benefits, and growth opportunities, work culture, and company values.
Here, you can find Data Engineering Resources ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐ Tech Round 1
โข DSA (Arrays, Strings): 1- 2 questions (easy to medium level)
โข SQL: Answered 3-5 SQL questions, working with complex queries.
โข Spark Fundamentals: Discussed core concepts of Apache Spark, including its role in big data processing.
๐ Tech Round 2
โข DSA (Arrays, Stack): Worked on problems related to arrays and stack, demonstrating my algorithmic thinking and problem-solving skills.
โข SQL: Tackled advanced SQL queries, focusing on query optimization and data manipulation techniques.
โข Spark Internals: Delved into Spark's internal workings and how it scales for large datasets.
๐ Hiring Manager Round
โข Data Modeling: Designed a data model for Uber and discussed approaches to managing real-world scenarios.
โข Team Dynamics & Project Management: Engaged in scenario-based questions, showcasing my understanding of team collaboration and project management.
โข Previous Project Experiences: Highlighted my contributions, challenges faced, and the impact of my work in past projects.
๐ HR Round
โข Work Culture: Discussed salary, benefits, and growth opportunities, work culture, and company values.
Here, you can find Data Engineering Resources ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐2
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ๐
Want to gain real-world experience and make your resume stand out?
These 100% free & remote virtual internships will help you develop in-demand skills from top global companies!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4bajU4J
Enroll Now & Get Certfied ๐
Want to gain real-world experience and make your resume stand out?
These 100% free & remote virtual internships will help you develop in-demand skills from top global companies!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4bajU4J
Enroll Now & Get Certfied ๐
Do these basics and get going for Data Engineering !!
๐ต SQL
-- Aggregations with GROUP BY
-- Joins (INNER, LEFT, FULL OUTER)
-- Window functions
-- Common table expressions
๐ต Data Modeling
-- Normalization and 3rd Normal Form
-- Fact, Dimension, and Aggregate Tables
-- Efficient Table Designs (Cumulative)
๐ต Python
-- Loops, If Statements
-- Complex Data Types (MAP, ARRAY, STRUCT)
๐ต Data Quality
-- Data Checks
-- Write-Audit-Publish Pattern
๐ต Distributed Compute
-- MapReduce
-- Partitioning, Skew, Spilling to Disk
๐ต SQL
-- Aggregations with GROUP BY
-- Joins (INNER, LEFT, FULL OUTER)
-- Window functions
-- Common table expressions
๐ต Data Modeling
-- Normalization and 3rd Normal Form
-- Fact, Dimension, and Aggregate Tables
-- Efficient Table Designs (Cumulative)
๐ต Python
-- Loops, If Statements
-- Complex Data Types (MAP, ARRAY, STRUCT)
๐ต Data Quality
-- Data Checks
-- Write-Audit-Publish Pattern
๐ต Distributed Compute
-- MapReduce
-- Partitioning, Skew, Spilling to Disk
๐4โค1
20 ๐ซ๐๐๐ฅ-๐ญ๐ข๐ฆ๐ ๐ฌ๐๐๐ง๐๐ซ๐ข๐จ-๐๐๐ฌ๐๐ ๐ข๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !!
๐๐๐ญ๐ฌ ๐๐ข๐ฏ๐ข๐๐ ๐ญ๐ก๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง 4 ๐ฉ๐๐ซ๐ญ๐ฌ
1. Data Processing and Transformation
2. Performance Tuning and Optimization
3. Data Pipeline Development
4. Debugging and Error Handling
๐๐๐ญ๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ ๐๐ง๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง:
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?
๐๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐ฎ๐ง๐ข๐ง๐ ๐๐ง๐ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
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?
๐๐๐ญ๐ ๐๐ข๐ฉ๐๐ฅ๐ข๐ง๐ ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐ฆ๐๐ง๐ญ:
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.
๐๐๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐๐ง๐ ๐๐ซ๐ซ๐จ๐ซ ๐๐๐ง๐๐ฅ๐ข๐ง๐ :
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 ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !!
๐๐๐ญ๐ฌ ๐๐ข๐ฏ๐ข๐๐ ๐ญ๐ก๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง 4 ๐ฉ๐๐ซ๐ญ๐ฌ
1. Data Processing and Transformation
2. Performance Tuning and Optimization
3. Data Pipeline Development
4. Debugging and Error Handling
๐๐๐ญ๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ ๐๐ง๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง:
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?
๐๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐ฎ๐ง๐ข๐ง๐ ๐๐ง๐ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
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?
๐๐๐ญ๐ ๐๐ข๐ฉ๐๐ฅ๐ข๐ง๐ ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐ฆ๐๐ง๐ญ:
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.
๐๐๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐๐ง๐ ๐๐ซ๐ซ๐จ๐ซ ๐๐๐ง๐๐ฅ๐ข๐ง๐ :
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 ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐ฅ1
๐ฑ ๐๐ฒ๐๐ ๐๐๐ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
1)Python for Data Science
2)SQL & Relational Databases
3)Applied Data Science with Python
4)Machine Learning with Python
5)Data Analysis with Python
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/3QyJyqk
Enroll For FREE & Get Certified๐
1)Python for Data Science
2)SQL & Relational Databases
3)Applied Data Science with Python
4)Machine Learning with Python
5)Data Analysis with Python
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/3QyJyqk
Enroll For FREE & Get Certified๐
๐1
10 Pyspark questions to clear your interviews.
1. How do you deploy PySpark applications in a production environment?
2. What are some best practices for monitoring and logging PySpark jobs?
3. How do you manage resources and scheduling in a PySpark application?
4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results).
5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark.
6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark?
8. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue.
9. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join?
10. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data
Remember: Donโt just mug up these questions, practice them on your own to build problem-solving skills and clear interviews easily
Here, you can find Data Engineering Resources ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
1. How do you deploy PySpark applications in a production environment?
2. What are some best practices for monitoring and logging PySpark jobs?
3. How do you manage resources and scheduling in a PySpark application?
4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results).
5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark.
6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark?
8. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue.
9. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join?
10. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data
Remember: Donโt just mug up these questions, practice them on your own to build problem-solving skills and clear interviews easily
Here, you can find Data Engineering Resources ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐2
๐ข๐ฟ๐ฎ๐ฐ๐น๐ฒ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ | ๐ฆ๐ค๐ ๐
SQL is a must-have skill for Data Science, Analytics, and Data Engineering roles!
Mastering SQL can boost your resume, help you land high-paying roles, and make you stand out in Data Science & Analytics!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4bjJaFv
Enroll Now & Get Certfied ๐
SQL is a must-have skill for Data Science, Analytics, and Data Engineering roles!
Mastering SQL can boost your resume, help you land high-paying roles, and make you stand out in Data Science & Analytics!
๐๐ข๐ง๐ค๐:-
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Enroll Now & Get Certfied ๐
๐1
๐๐๐ซ๐ ๐๐ซ๐ 20 ๐ซ๐๐๐ฅ-๐ญ๐ข๐ฆ๐ ๐๐ฉ๐๐ซ๐ค ๐ฌ๐๐๐ง๐๐ซ๐ข๐จ-๐๐๐ฌ๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost?
2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation?
3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website.
4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly?
5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this?
6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application.
7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it.
8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application?
9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events?
10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results?
11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams.
12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it?
13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records?
14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention?
15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse.
16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently?
17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production?
18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency?
19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset.
20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why?
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost?
2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation?
3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website.
4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly?
5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this?
6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application.
7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it.
8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application?
9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events?
10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results?
11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams.
12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it?
13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records?
14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention?
15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse.
16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently?
17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production?
18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency?
19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset.
20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why?
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐1
Cisco Kafka interview questions for Data Engineers 2024.
โค How do you create a topic in Kafka using the Confluent CLI?
โค Explain the role of the Schema Registry in Kafka.
โค How do you register a new schema in the Schema Registry?
โค What is the importance of key-value messages in Kafka?
โค Describe a scenario where using a random key for messages is beneficial.
โค Provide an example where using a constant key for messages is necessary.
โค Write a simple Kafka producer code that sends JSON messages to a topic.
โค How do you serialize a custom object before sending it to a Kafka topic?
โค Describe how you can handle serialization errors in Kafka producers.
โค Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
โค How do you handle deserialization errors in Kafka consumers?
โค Explain the process of deserializing messages into custom objects.
โค What is a consumer group in Kafka, and why is it important?
โค Describe a scenario where multiple consumer groups are used for a single topic.
โค How does Kafka ensure load balancing among consumers in a group?
โค How do you send JSON data to a Kafka topic and ensure it is properly serialized?
โค Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
โค Explain how you can work with CSV data in Kafka, including serialization and deserialization.
โค Write a Kafka producer code snippet that sends CSV data to a topic.
โค Write a Kafka consumer code snippet that reads and processes CSV data from a topic.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
โค How do you create a topic in Kafka using the Confluent CLI?
โค Explain the role of the Schema Registry in Kafka.
โค How do you register a new schema in the Schema Registry?
โค What is the importance of key-value messages in Kafka?
โค Describe a scenario where using a random key for messages is beneficial.
โค Provide an example where using a constant key for messages is necessary.
โค Write a simple Kafka producer code that sends JSON messages to a topic.
โค How do you serialize a custom object before sending it to a Kafka topic?
โค Describe how you can handle serialization errors in Kafka producers.
โค Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
โค How do you handle deserialization errors in Kafka consumers?
โค Explain the process of deserializing messages into custom objects.
โค What is a consumer group in Kafka, and why is it important?
โค Describe a scenario where multiple consumer groups are used for a single topic.
โค How does Kafka ensure load balancing among consumers in a group?
โค How do you send JSON data to a Kafka topic and ensure it is properly serialized?
โค Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
โค Explain how you can work with CSV data in Kafka, including serialization and deserialization.
โค Write a Kafka producer code snippet that sends CSV data to a topic.
โค Write a Kafka consumer code snippet that reads and processes CSV data from a topic.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐2
Forwarded from Data Science Projects
๐ง๐ผ๐ฝ ๐ ๐ก๐๐ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐๐ & ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ ๐
GE:- https://pdlink.in/3DmQsf4
United:- https://pdlink.in/3F6ZwVW
Birlasoft :- https://pdlink.in/41B0umg
KPMG:- https://pdlink.in/4ifHDCB
Lightcast:- https://pdlink.in/4gXt3im
Barlcays :- https://pdlink.in/4bpnvfm
Apply before the link expires ๐ซ
GE:- https://pdlink.in/3DmQsf4
United:- https://pdlink.in/3F6ZwVW
Birlasoft :- https://pdlink.in/41B0umg
KPMG:- https://pdlink.in/4ifHDCB
Lightcast:- https://pdlink.in/4gXt3im
Barlcays :- https://pdlink.in/4bpnvfm
Apply before the link expires ๐ซ
๐1
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ?
Here is a complete week-by-week roadmap that can help
๐ช๐ฒ๐ฒ๐ธ ๐ญ: Learn programming - Python for data manipulation, and Java for big data frameworks.
๐ช๐ฒ๐ฒ๐ธ ๐ฎ-๐ฏ: Understand database concepts and databases like MongoDB.
๐ช๐ฒ๐ฒ๐ธ ๐ฐ-๐ฒ: Start with data warehousing (ETL), Big Data (Hadoop) and Data pipelines (Apache AirFlow)
๐ช๐ฒ๐ฒ๐ธ ๐ฒ-๐ด: Go for advanced topics like cloud computing and containerization (Docker).
๐ช๐ฒ๐ฒ๐ธ ๐ต-๐ญ๐ฌ: Participate in Kaggle competitions, build projects and develop communication skills.
๐ช๐ฒ๐ฒ๐ธ ๐ญ๐ญ: 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 ๐๐
Here is a complete week-by-week roadmap that can help
๐ช๐ฒ๐ฒ๐ธ ๐ญ: Learn programming - Python for data manipulation, and Java for big data frameworks.
๐ช๐ฒ๐ฒ๐ธ ๐ฎ-๐ฏ: Understand database concepts and databases like MongoDB.
๐ช๐ฒ๐ฒ๐ธ ๐ฐ-๐ฒ: Start with data warehousing (ETL), Big Data (Hadoop) and Data pipelines (Apache AirFlow)
๐ช๐ฒ๐ฒ๐ธ ๐ฒ-๐ด: Go for advanced topics like cloud computing and containerization (Docker).
๐ช๐ฒ๐ฒ๐ธ ๐ต-๐ญ๐ฌ: Participate in Kaggle competitions, build projects and develop communication skills.
๐ช๐ฒ๐ฒ๐ธ ๐ญ๐ญ: 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 ๐๐
๐3
How to become a data engineer in 2025:
โก๏ธ Learn SQL
โก๏ธ Learn Python
โก๏ธ Learn Spark
โก๏ธ Learn ETL/ELT
โก๏ธ Learn data modelling
Then use what you've learnt and build in public
โก๏ธ Learn SQL
โก๏ธ Learn Python
โก๏ธ Learn Spark
โก๏ธ Learn ETL/ELT
โก๏ธ Learn data modelling
Then use what you've learnt and build in public
โค5๐1
๐ Mastering Spark: 20 Interview Questions Demystified!
1๏ธโฃ MapReduce vs. Spark: Learn how Spark achieves 100x faster performance compared to MapReduce.
2๏ธโฃ RDD vs. DataFrame: Unravel the key differences between RDD and DataFrame, and discover what makes DataFrame unique.
3๏ธโฃ DataFrame vs. Datasets: Delve into the distinctions between DataFrame and Datasets in Spark.
4๏ธโฃ RDD Operations: Explore the various RDD operations that power Spark.
5๏ธโฃ Narrow vs. Wide Transformations: Understand the differences between narrow and wide transformations in Spark.
6๏ธโฃ Shared Variables: Discover the shared variables that facilitate distributed computing in Spark.
7๏ธโฃ Persist vs. Cache: Differentiate between the persist and cache functionalities in Spark.
8๏ธโฃ Spark Checkpointing: Learn about Spark checkpointing and how it differs from persisting to disk.
9๏ธโฃ SparkSession vs. SparkContext: Understand the roles of SparkSession and SparkContext in Spark applications.
๐ spark-submit Parameters: Explore the parameters to specify in the spark-submit command.
1๏ธโฃ1๏ธโฃ Cluster Managers in Spark: Familiarize yourself with the different types of cluster managers available in Spark.
1๏ธโฃ2๏ธโฃ Deploy Modes: Learn about the deploy modes in Spark and their significance.
1๏ธโฃ3๏ธโฃ Executor vs. Executor Core: Distinguish between executor and executor core in the Spark ecosystem.
1๏ธโฃ4๏ธโฃ Shuffling Concept: Gain insights into the shuffling concept in Spark and its importance.
1๏ธโฃ5๏ธโฃ Number of Stages in Spark Job: Understand how to decide the number of stages created in a Spark job.
1๏ธโฃ6๏ธโฃ Spark Job Execution Internals: Get a peek into how Spark internally executes a program.
1๏ธโฃ7๏ธโฃ Direct Output Storage: Explore the possibility of directly storing output without sending it back to the driver.
1๏ธโฃ8๏ธโฃ Coalesce and Repartition: Learn about the applications of coalesce and repartition in Spark.
1๏ธโฃ9๏ธโฃ Physical and Logical Plan Optimization: Uncover the optimization techniques employed in Spark's physical and logical plans.
2๏ธโฃ0๏ธโฃ Treereduce and Treeaggregate: Discover why treereduce and treeaggregate are preferred over reduceByKey and aggregateByKey in certain scenarios.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
1๏ธโฃ MapReduce vs. Spark: Learn how Spark achieves 100x faster performance compared to MapReduce.
2๏ธโฃ RDD vs. DataFrame: Unravel the key differences between RDD and DataFrame, and discover what makes DataFrame unique.
3๏ธโฃ DataFrame vs. Datasets: Delve into the distinctions between DataFrame and Datasets in Spark.
4๏ธโฃ RDD Operations: Explore the various RDD operations that power Spark.
5๏ธโฃ Narrow vs. Wide Transformations: Understand the differences between narrow and wide transformations in Spark.
6๏ธโฃ Shared Variables: Discover the shared variables that facilitate distributed computing in Spark.
7๏ธโฃ Persist vs. Cache: Differentiate between the persist and cache functionalities in Spark.
8๏ธโฃ Spark Checkpointing: Learn about Spark checkpointing and how it differs from persisting to disk.
9๏ธโฃ SparkSession vs. SparkContext: Understand the roles of SparkSession and SparkContext in Spark applications.
๐ spark-submit Parameters: Explore the parameters to specify in the spark-submit command.
1๏ธโฃ1๏ธโฃ Cluster Managers in Spark: Familiarize yourself with the different types of cluster managers available in Spark.
1๏ธโฃ2๏ธโฃ Deploy Modes: Learn about the deploy modes in Spark and their significance.
1๏ธโฃ3๏ธโฃ Executor vs. Executor Core: Distinguish between executor and executor core in the Spark ecosystem.
1๏ธโฃ4๏ธโฃ Shuffling Concept: Gain insights into the shuffling concept in Spark and its importance.
1๏ธโฃ5๏ธโฃ Number of Stages in Spark Job: Understand how to decide the number of stages created in a Spark job.
1๏ธโฃ6๏ธโฃ Spark Job Execution Internals: Get a peek into how Spark internally executes a program.
1๏ธโฃ7๏ธโฃ Direct Output Storage: Explore the possibility of directly storing output without sending it back to the driver.
1๏ธโฃ8๏ธโฃ Coalesce and Repartition: Learn about the applications of coalesce and repartition in Spark.
1๏ธโฃ9๏ธโฃ Physical and Logical Plan Optimization: Uncover the optimization techniques employed in Spark's physical and logical plans.
2๏ธโฃ0๏ธโฃ Treereduce and Treeaggregate: Discover why treereduce and treeaggregate are preferred over reduceByKey and aggregateByKey in certain scenarios.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
๐2
๐ฐ ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐!๐
Want to stand out as an AI developer?โจ๏ธ
These 4 AI certifications will help you build expertise, understand AI ethics, and develop impactful solutions! ๐ก๐ค
๐๐ข๐ง๐ค๐:-
https://pdlink.in/41hvSoy
Perfect for Beginners & Developers Looking to Upskill!โ ๏ธ
Want to stand out as an AI developer?โจ๏ธ
These 4 AI certifications will help you build expertise, understand AI ethics, and develop impactful solutions! ๐ก๐ค
๐๐ข๐ง๐ค๐:-
https://pdlink.in/41hvSoy
Perfect for Beginners & Developers Looking to Upskill!โ ๏ธ
๐1
One day or Day one. You decide.
Data Engineer edition.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench and write my first query.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my data pipelines.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Apache Airflow and set up my first DAG.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master big data tools.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start a Spark tutorial and process my first dataset.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn cloud data services.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Sign up for an Azure or AWS account and deploy my first data pipeline.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Engineer.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to data engineering job postings.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will start preparing for the interviews.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start preparing from today itself without any procrastination
Here, you can find Data Engineering Resources ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
Data Engineer edition.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench and write my first query.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my data pipelines.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Apache Airflow and set up my first DAG.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master big data tools.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start a Spark tutorial and process my first dataset.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn cloud data services.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Sign up for an Azure or AWS account and deploy my first data pipeline.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Engineer.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to data engineering job postings.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will start preparing for the interviews.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start preparing from today itself without any procrastination
Here, you can find Data Engineering Resources ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐6
๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ & ๐๐!๐
Want to boost your career with in-demand skills like ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐, ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐ฃ๐๐๐ต๐ผ๐ป, ๐ฎ๐ป๐ฑ ๐ฆ๐ค๐?๐
These ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ provide hands-on learning with interactive labs and certifications ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ to enhance your ๐ฅ๐ฒ๐๐๐บ๐ฒ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Xrrouh
Perfect for beginners & professionals looking to upgrade their expertiseโtaught by industry experts!โ ๏ธ
Want to boost your career with in-demand skills like ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐, ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐ฃ๐๐๐ต๐ผ๐ป, ๐ฎ๐ป๐ฑ ๐ฆ๐ค๐?๐
These ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ provide hands-on learning with interactive labs and certifications ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ to enhance your ๐ฅ๐ฒ๐๐๐บ๐ฒ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Xrrouh
Perfect for beginners & professionals looking to upgrade their expertiseโtaught by industry experts!โ ๏ธ
๐1
5 Pandas Functions to Handle Missing Data
๐น fillna() โ Fill missing values with a specific value or method
๐น interpolate() โ Fill NaNs with interpolated values (e.g., linear, time-based)
๐น ffill() โ Forward-fill missing values with the previous valid entry
๐น bfill() โ Backward-fill missing values with the next valid entry
๐น dropna() โ Remove rows or columns with missing values
#Pandas
๐น fillna() โ Fill missing values with a specific value or method
๐น interpolate() โ Fill NaNs with interpolated values (e.g., linear, time-based)
๐น ffill() โ Forward-fill missing values with the previous valid entry
๐น bfill() โ Backward-fill missing values with the next valid entry
๐น dropna() โ Remove rows or columns with missing values
#Pandas
๐2
SNOWFLAKES AND DATABRICKS
Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?
๐ ๐๐ง๐จ๐ฐ๐๐ฅ๐๐ค๐
โ๏ธ ๐๐๐ญ๐ฎ๐ซ๐: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.
โ๏ธ ๐๐ญ๐ซ๐๐ง๐ ๐ญ๐ก๐ฌ: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
โ๏ธ Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.
โ๏ธ ๐ ๐ฅ๐๐ฑ๐ข๐๐ข๐ฅ๐ข๐ญ๐ฒ: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.
โ๏ธ ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ : While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.
๐ ๐๐๐ญ๐๐๐ซ๐ข๐๐ค๐ฌ
โ๏ธ ๐๐จ๐ซ๐: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.
โ๏ธ ๐๐ญ๐จ๐ซ๐๐ ๐: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.
๐ ๐๐๐ฒ ๐๐๐ค๐๐๐ฐ๐๐ฒ๐ฌ
โ๏ธ ๐๐ข๐ฌ๐ญ๐ข๐ง๐๐ญ ๐๐๐๐๐ฌ: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.
โ๏ธ ๐๐ง๐จ๐ฐ๐๐ฅ๐๐ค๐โ๐ฌ ๐๐๐๐๐ฅ ๐๐ฌ๐ ๐๐๐ฌ๐: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.
โ๏ธ ๐๐๐ญ๐๐๐ซ๐ข๐๐ค๐ฌ ๐๐จ๐ซ ๐๐จ๐ฆ๐ฉ๐ฅ๐๐ฑ ๐๐๐ง๐๐ฌ๐๐๐ฉ๐๐ฌ: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricksโwith its schema-on-read techniqueโmay be more advantageous.
๐ ๐๐จ๐ง๐๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง:
Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.
Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?
๐ ๐๐ง๐จ๐ฐ๐๐ฅ๐๐ค๐
โ๏ธ ๐๐๐ญ๐ฎ๐ซ๐: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.
โ๏ธ ๐๐ญ๐ซ๐๐ง๐ ๐ญ๐ก๐ฌ: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
โ๏ธ Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.
โ๏ธ ๐ ๐ฅ๐๐ฑ๐ข๐๐ข๐ฅ๐ข๐ญ๐ฒ: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.
โ๏ธ ๐๐๐ญ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐ : While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.
๐ ๐๐๐ญ๐๐๐ซ๐ข๐๐ค๐ฌ
โ๏ธ ๐๐จ๐ซ๐: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.
โ๏ธ ๐๐ญ๐จ๐ซ๐๐ ๐: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.
๐ ๐๐๐ฒ ๐๐๐ค๐๐๐ฐ๐๐ฒ๐ฌ
โ๏ธ ๐๐ข๐ฌ๐ญ๐ข๐ง๐๐ญ ๐๐๐๐๐ฌ: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.
โ๏ธ ๐๐ง๐จ๐ฐ๐๐ฅ๐๐ค๐โ๐ฌ ๐๐๐๐๐ฅ ๐๐ฌ๐ ๐๐๐ฌ๐: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.
โ๏ธ ๐๐๐ญ๐๐๐ซ๐ข๐๐ค๐ฌ ๐๐จ๐ซ ๐๐จ๐ฆ๐ฉ๐ฅ๐๐ฑ ๐๐๐ง๐๐ฌ๐๐๐ฉ๐๐ฌ: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricksโwith its schema-on-read techniqueโmay be more advantageous.
๐ ๐๐จ๐ง๐๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง:
Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.
๐5
๐๐ฟ๐ฎ๐ฐ๐ธ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐๐ถ๐๐ต ๐ง๐ต๐ถ๐ ๐๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐๐๐ถ๐ฑ๐ฒ!๐
Preparing for a Data Analytics interview?โจ๏ธ
๐ Donโt waste time searchingโthis guide has everything you need to ace your interview!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4h6fSf2
Get a structured roadmap Now โ
Preparing for a Data Analytics interview?โจ๏ธ
๐ Donโt waste time searchingโthis guide has everything you need to ace your interview!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4h6fSf2
Get a structured roadmap Now โ