System Design round of

Data Engineering Mock Interview - Second Round

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Join Ankur Ranjan, an experienced Data Engineering professional with over 5 years of experience, and Manoj for an exciting and informative Data Engineering mock interview session.

If you're preparing for a Data Engineering interview, this is the perfect opportunity to enhance your skills and increase your chances of success. The mock interview simulates a real-life scenario and provides valuable insights and guidance. The topics covered include discussion on SQL vs NoSQL, Git Practices, Deployment methodology in the Data Engineering etc.

It covered questions from Apache Spark, SQL, Airflow, File Formats, ETL pipelines, data modelling, database technologies, cloud platforms, and more. You'll get to see how professionals tackle technical questions and problem-solving challenges in a structured and efficient manner.

By watching this mock interview, you'll learn effective strategies to approach technical questions and problem-solving scenarios, gain familiarity with the data engineering interview process and format, enhance your communication skills and ability to articulate your thoughts clearly, identify areas of improvement, receive expert feedback on your performance, boost your confidence, and reduce nervousness for future interviews.

This mock interview suits all levels of experience, whether you're a fresh graduate, a career changer, or a seasoned professional looking to improve your interview skills. Don't miss out on this invaluable learning experience! Subscribe to our channel and hit the notification bell to be notified when the mock interview is released. Stay tuned for a deep dive into the world of data engineering.

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🔅 To book a Mock interview - https://topmate.io/ankur_ranjan/15155

🔅 LinkedIn -   / thebigdatashow  

🔅 Instagram -   / ranjan_anku  

🔅 Ankur(Interviewer) 's LinkedIn profile -   / thebigdatashow  

🔅 Manoj (Interviewee)'s LinkedIn profile -   / manoj-t-dataengineer  

Chapters:

00:00 - Introduction
03:21 - Reason behind migrating the streaming project from Spring Boot to DataLake
04:22 - Source of streaming project
04:56 - What is Kinesis?
05:36 - Retention period of data in Kinesis
06:02 - Where does the serialisation happen? Is it at Producer side or at consumer application?
06:30 - Understanding Transformation layer
08:32 - Does Kinesis also have key, value concepts ?
08:42 - How Kinesis works as a distributed streaming platform ? How does message distribution happen across the partitions in kinesis ?
09:20 - Scenario based question
12:10 - Understanding on Sink part
12:47 - Usage of Airflow
13:20 - Advantage of using Airflow over cron job
14:39 - What is task in airflow?
16:14 - What is hooks in airflow?
16:48 - Scenario Based Question
17:48 - What do you understand by scheduler?
18:12 - How do you secure your airflow?
18:56 - System Design Question
41:42 - Design streaming application from interviewer view



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