Latest Tiger Analytics coding Interview Questions & Answers | Data Engineer Prep 2024

Top Tiger Analytics SQL Interview Questions and Boost Your Data Engineering Career in 2024!
Are you preparing for a data engineering interview at Tiger Analytics? You've come to the right place! This comprehensive guide covers essential pyspark interview questions and provides in-depth answers to help you succeed in your upcoming interview. Whether you're a seasoned professional or just starting your career in data engineering, this video is packed with valuable insights to give you a competitive edge.

🔍 What You'll Learn:
Recent pyspark questions for Tiger Analytics interviews
Real-world data engineering scenarios and solutions
Expert tips for acing your technical interview
Insider knowledge on Tiger Analytics's interview process
Hands-on practice with sample SQL queries

📊 Sample Data :
Let's dive into a practical example that you might encounter in your Tiger Analytics interview:

data = [('TV', '2016-11-27', 800),('TV', '2016-11-30', 900),('TV', '2016-12-29', 500),('TV', '2017-11-20', 400),('FRIDGE', '2016-10-11', 760),('FRIDGE', '2016-10-13', 400),('FRIDGE', '2016-11-27',460)]

schema = ['product','sale_date','amount']

data1 = [('20d75c97-5fee-11e8-92c7-67fe1c388607',['A:X:M', 'B:Y:N', 'C:Z:O', 'D:W:P', 'E:V:Q','A:W:P']),('20d75c98-5fee-11e8-92c7-5f0316c1a74f',['A:X:M', 'B:W:N', 'C:L:O']),('20d75c99-5fee-11e8-92c7-d9bfa897a151',['A:X:M', 'F:Y:N', 'H:Z:O','A:W:P'])]
schema1 = ['uniqueid','status_value']


data2 = [(123, 'impression', '07/18/2022 11:36:12'),(123, 'impression', '07/18/2022 11:37:12'),(123, 'click', '07/18/2022 11:37:42'),(234, 'impression', '07/18/2022 14:15:12'),(234, 'click', '07/18/2022 14:16:12')]
schema2 = ['app_id','event_type','timestamp']




🚀 Key Topics Covered:
Advanced pyspark conceps
Aggregation and window functions
Data modeling best practices
Performance tuning for large datasets
Dealing with data inconsistencies and duplicates

💡 Interview Success Strategies:
Understand the interviewer's perspective
Showcase your problem-solving skills
Communicate your thought process effectively
Demonstrate your knowledge of data engineering principles
Highlight relevant projects and experiences

🔗 Additional Resources:
To further enhance your preparation, check out these playlists:

PySpark playlist :    • PySpark and Databricks  
Azure Datafactory playlist :    • Azure Data Factory Tutorial  
PySpark RealTime Scenarios playlist :    • PySpark Real Time Scenarios  
Azure Data Factory RealTime Scenarios playlist :    • Azure Data Factory RealTime Scenarios  
PySpark Interview Question :    • PySpark Interview Series  
Scenario Based Interview Question :    • Scenario Based Interview Question  
Unit Testing in PySpark :    • UnitTesting PySpark  

🌟 Why This Video Stands Out:
Up-to-date content reflecting Tiger Analytics's latest interview trends
In-depth explanations of complex SQL concepts
Real-world examples from experienced data engineers
Interactive coding demonstrations
Tips for both technical and soft skills required for success

👨‍💻 About the Instructor:
As an experienced data engineer and interview coach, I've helped countless professionals land their dream jobs at top tech companies like Tiger Analytics. My practical approach and industry insights will give you the confidence to excel in your interview.

🔔 Stay Connected:
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📧 Need Personalized Guidance?
For one-on-one coaching or specific interview questions, feel free to reach out:
Email: [email protected]
Topmate: https://topmate.io/pritamsaha627

Remember, success in data engineering interviews comes from consistent practice and a deep understanding of core concepts. This video is your first step towards acing your Tiger Analytics interview and launching your dream career in data engineering.
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