𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍

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20 recently asked 𝗣𝗬𝗧𝗛𝗢𝗡 questions for Data Engineers.

1. Design a Python script to process and transform large CSV files from multiple sources daily.
2. Write Python code to identify and handle missing values in a dataset.
3. Implement a Python solution to store large volumes of time-series data efficiently using an appropriate format.
4. Create a Python-based system to process streaming data from IoT devices in real-time.
5. Write a Python ETL script to extract data from a SQL database, transform it, and load it into a NoSQL database.
6. Implement error handling in a Python data pipeline when an unexpected data type is encountered.
7. Write Python code to validate incoming data for consistency and accuracy.
8. Optimize a Python script processing large datasets to reduce runtime.
9. Create a Python function to merge multiple large datasets without memory overflow.
10. Write a Python script to automate the daily backup of data stored in a cloud bucket.
11. Implement parallel processing in Python for handling large-scale data operations.
12. Write a Python program to monitor and log the performance of a data pipeline.
13. Implement a Python solution to remove duplicates from a large dataset efficiently.
14. Write a Python script to connect to an API, fetch data, and store it in a database.
15. Implement a Python function to generate summary statistics for a large dataset.
16. Write a Python script to clean and standardize a dataset with inconsistent formats.
17. Implement a Python-based incremental data load from a source system to a data warehouse.
18. Write Python code to detect and remove outliers from a dataset.
19. Implement a Python pipeline to process and analyze log files in real-time.
20. Write Python code to create and manage partitions in a large dataset for faster querying.

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Roadmap to crack product-based companies for Big Data Engineer role:

1. Master Python, Scala/Java
2. Ace Apache Spark, Hadoop ecosystem
3. Learn data storage (SQL, NoSQL), warehousing
4. Expertise in data streaming (Kafka, Flink/Storm)
5. Master workflow management (Airflow)
6. Cloud skills (AWS, Azure or GCP)
7. Data modeling, ETL/ELT processes
8. Data viz tools (Tableau, Power BI)
9. Problem-solving, communication, attention to detail
10. Projects, certifications (AWS, Azure, GCP)
11. Practice coding, system design interviews

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𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍

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7 Baby steps to learn Python:

1. Learn the basics: Start with the fundamentals of Python programming language, such as data types, variables, operators, control structures, and functions.

2. Write simple programs: Start writing simple programs to practice what you have learned. Start with small programs that solve basic problems, such as calculating the factorial of a number, checking whether a number is prime or not, or finding the sum of a sequence of numbers.

3. Work on small projects: Start working on small projects that interest you. These can be simple projects, such as creating a calculator, building a basic game, or automating a task. By working on small projects, you can develop your programming skills and gain confidence.

4. Learn from other people's code: Look at other people's code and try to understand how it works. You can find many open-source projects on platforms like GitHub. Analyze the code, see how it's structured, and try to figure out how the program works.

5. Read Python documentation: Python has extensive documentation, which is very helpful for beginners. Read the documentation to learn more about Python libraries, modules, and functions.

6. Participate in online communities: Participate in online communities like StackOverflow, Reddit, or Python forums. These communities have experienced programmers who can help you with your doubts and questions.

7. Keep practicing: Practice is the key to becoming a good programmer. Keep working on projects, practicing coding problems, and experimenting with different techniques. The more you practice, the better you'll get.

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Data engineering interviews will be 10x easier if you learn these tools in sequence👇

➤ 𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀
- SQL is very important
- Learn Python Funddamentals
- Pandas and Numpy Library in Python.

➤ 𝗢𝗻-𝗣𝗿𝗲𝗺 𝘁𝗼𝗼𝗹𝘀
- Learn Pyspark - In Depth (Processing tool)
- Hadoop (Distrubuted Storage)
- Hive (Datawarehouse)
- Hbase (NoSQL Database)
- Airflow (Orchestration)
- Kafka (Streaming platform)
- CICD for production readiness

➤ 𝗖𝗹𝗼𝘂𝗱 (𝗔𝗻𝘆 𝗼𝗻𝗲)
- AWS
- Azure
- GCP

➤ Do a couple of projects to get a good feel of it.

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Top 30 Data Engineering Interview Questions

𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸
- What is the difference between transformations and actions in Spark, and can you provide an example?
- How can data partitioning be optimized for performance in Spark?
- What is the difference between cache() and persist() in Spark, and when would you use each?

𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮
- How does Kafka partitioning enable scalability and load balancing?
- How does Kafka’s replication mechanism provide durability and fault tolerance?
- How would you manage Kafka consumer rebalancing to minimize data loss?

𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄
- What are dynamic DAGs in Airflow, and what benefits do they offer?
- What are Airflow pools, and how do they help control task concurrency?
- How do you implement time-based and event-based triggers for DAGs in Airflow?

𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴
- How would you design a data warehouse schema for an e-commerce platform?
- What is the difference between OLAP and OLTP, and how do they complement each other?
- What are materialized views, and how do they improve query performance?

𝗖𝗜/𝗖𝗗
- How do you integrate automated testing into a CI/CD pipeline for ETL jobs?
- How do you manage environment-specific configurations in a CI/CD pipeline?
- How is version control managed for database schemas and ETL scripts in a CI/CD pipeline?

𝗦𝗤𝗟
- How do you write a query to fetch the top 5 highest salaries in each department?
- What’s the difference between the HAVING and WHERE clauses in SQL?
- How do you handle NULL values in SQL, and how do they affect aggregate functions?

𝗣𝘆𝘁𝗵𝗼𝗻
- How do you handle large datasets in Python, and which libraries would you use for performance?
- What are context managers in Python, and how do they help with resource management?
- How do you manage and log errors in Python-based ETL pipelines?

𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀
- How would you optimize a Databricks job using Spark SQL on large datasets?
- What is Delta Lake in Databricks, and how does it ensure data consistency?
- How do you manage and secure access to Databricks clusters for multiple users?

𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆
- What are linked services in Azure Data Factory, and how do they facilitate data integration?
- How do you use mapping data flows in Azure Data Factory to transform and filter data?
- How do you monitor and troubleshoot failures in Azure Data Factory pipelines?

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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