Python/Data Engineering Interview Preparation

3-Day Learning Plan

Day 1: Python Mastery

Focus: Advanced Python concepts, data structures, algorithms, and coding exercises.

1. Topics to Cover:
   1. Advanced Python:
      1. List, dict, and set comprehensions.
      2. Decorators, context managers (with statements).
      3. Generators and iterators.
   2. OOP Concepts:
      1. Abstract classes and interfaces.
      2. Polymorphism and inheritance.
      3. Design patterns: Singleton, Factory, etc.
   3. Async Programming:
      1. asyncio, coroutines, async/await.
   4. Error Handling and Logging:
      1. Custom exceptions.
      2. Logging best practices.
   5. Python for Data Engineering:
      1. Working with large files (csv, json, parquet).
      2. Serialization/deserialization (pickle, JSON, etc.).
      3. Performance optimization (multiprocessing, concurrent.futures)
2. Actionable Exercises:
   1. Implement a custom decorator for memoization.
   2. Write a context manager to handle file operations with error handling.
   3. Develop an async web scraper using aiohttp or httpx.
   4. Solve algorithm problems on LeetCode (Medium/Hard) related to:
      1. Arrays (e.g., sliding window problems).
      2. Hashmaps and strings (e.g., anagrams, longest substrings).
      3. Trees and graphs (e.g., BFS, DFS)

Day 2: SQL and AWS

Part 1: SQL

Focus: Writing efficient, optimized queries, working with complex datasets, and core AWS services for data pipelines.

1. Topics to Cover:
   1. SQL Essentials:
      1. Joins (INNER, OUTER, CROSS).
      2. Aggregations (GROUP BY, HAVING).
      3. Subqueries and Common Table Expressions (CTEs).
   2. Performance Optimization:
      1. Indexing strategies.
      2. Query execution plans (EXPLAIN).
      3. Window functions (ROW_NUMBER, RANK, LAG/LEAD).
   3. Advanced Topics:
      1. Recursive queries.
      2. Partitioning tables.
2. Actionable Exercises:
   1. Given two large datasets (e.g., users and transactions), write queries to:
      1. Find users with the highest transaction volume in the last 3 months.
      2. Calculate moving averages of transaction amounts.
      3. Generate a report showing top-performing users grouped by region.
   2. Use online SQL platforms like HackerRank or LeetCode SQL for challenges.

Part 2: AWS/Cloud Platforms

Focus: Core AWS services and their integration for data pipelines.

1. Topics to Cover:
   1. Compute:
      1. AWS Lambda, EC2.
   2. Storage:
      1. S3. Glacier. DynamoDB.
   3. Database:
      1. RDS. Redshift.
   4. Workflow Orchestration:
      1. AWS Step Functions.
      2. Managed Airflow.
   5. Infrastructure as Code:
      1. Basics of CloudFormation or Terraform.
2. Actionable Exercises:
   1. Create an AWS Lambda function to process a dataset from S3 and store results in DynamoDB.
   2. Design an S3-to-Redshift ETL pipeline and write a Python script to load data.
   3. Explore AWS Free Tier services and practice basic setups for S3, Lambda, and RDS.

Day 3: Data Engineering Tools

Focus: Using Pandas, Databricks, and Spark for ETL and data manipulation.

1. Topics to Cover:
   1. Pandas:
      1. Efficient DataFrame manipulations (apply, merge, groupby).
      2. Handling missing data (fillna, interpolate).
      3. Working with large datasets (chunksize, dask for scaling Pandas).
   2. Databricks and Apache Spark:
      1. Introduction to Spark's architecture (RDD, DataFrame, Dataset API).
      2. Writing PySpark jobs for ETL pipelines.
      3. Optimizing queries with caching and partitioning.
   3. ETL Patterns:
      1. Data cleaning, transformation, and validation.
      2. Designing idempotent ETL pipelines.
2. Actionable Exercises:
   1. Pandas:
      1. Load a large dataset (e.g., 10 million rows) and perform:
         1. Cleaning (e.g., removing duplicates, imputing missing values).
         2. Aggregation (e.g., sales by month).
         3. Visualization (e.g., plotting trends using matplotlib or seaborn).
   2. Databricks:
      1. Set up a free Databricks workspace and:
         1. Process a CSV file into Parquet format.
         2. Write and execute a Spark SQL query.
   3. ETL Pipeline:
      1. Build a pipeline that:
         1. Reads data from an S3 bucket.
         2. Cleanses and transforms the data with Pandas or Spark.
         3. Writes the results to a Redshift table.

Day x: Summary

Mock Interviews and Review

1. Python Live Coding Practice:
   1. Solve live problems from platforms like CoderPad or Pramp.
   2. Write an end-to-end Python script for an ETL pipeline.
2. SQL:
   1. Conduct mock interviews focused on SQL query design and optimization.
3. AWS/Cloud:
   1. Be ready to explain cloud service designs and trade-offs (e.g., why use S3 vs. RDS?).
4. Data Engineering Tools:
   1. Prepare to write Pandas or PySpark transformations live.

20 Real-Life Challenges for Mock Interviews (Divided by Topics)

1. Python Mock Interview Questions

1. Design Patterns:
   1. How would you implement a Singleton pattern in Python? When would you use it in real projects?
2. Memory Management:
   1. Explain how Python handles memory allocation. What are garbage collection and reference counting?
3. Concurrency:
   1. Write a program to scrape multiple URLs concurrently using asyncio.
4. Error Handling:
   1. How do you design a robust error-handling mechanism for a file-processing pipeline?
5. Code Optimization:
   1. Refactor a function with nested loops processing large datasets to improve performance.
6. Testing:
   1. How do you write unit tests for a function that interacts with a third-party API?
7. OOP:
   1. Implement a class-based solution for a parking lot management system.
8. Generators:
   1. How would you use a generator to handle streaming data efficiently in Python?
9. Large Files:
   1. How would you read and process a 10GB CSV file in Python without loading it all into memory?
10. Database Integration:
    1. Write a Python script that connects to a PostgreSQL database, fetches data, and performs transformations.

2. SQL Mock Interview Questions

1. Query Optimization:
   1. Analyze a poorly written query and optimize it to reduce execution time.
2. Joins:
   1. Write a query to find customers who made purchases in two consecutive months.
3. Window Functions:
   1. Calculate a 3-month rolling average for sales data using a window function.
4. CTEs and Recursive Queries:
   1. Use a recursive CTE to find all employees under a manager in an organization hierarchy.
5. Indexes:
   1. Explain how indexes work and why they can sometimes slow down performance.
6. Data Aggregation:
   1. Write a query to group sales data by region and month, including the total and average sales.
7. Schema Design:
   1. Design a schema for a library system. Explain your normalization decisions.
8. Data Cleaning:
   1. How would you identify and remove duplicate entries in a database table?
9. Transaction Management:
   1. Explain how you would ensure data consistency in a banking system with concurrent transactions.
10. Error Debugging:
    1. You receive an error in a SQL query that uses a GROUP BY clause. How do you troubleshoot it?

3. AWS/Cloud Platforms Mock Interview Questions

1. S3 Bucket Policies:
   1. How do you restrict public access to an S3 bucket while allowing specific users or services?
2. Lambda Functions:
   1. Design an AWS Lambda function to process a stream of events from Kinesis and store results in DynamoDB.
3. EC2 Scaling:
   1. How do you set up auto-scaling for an EC2 instance in response to traffic spikes?
4. Serverless Architecture:
   1. When would you choose serverless over traditional EC2-based deployments? Provide real-world examples.
5. CloudFormation:
   1. Write a basic CloudFormation template to provision an S3 bucket and a DynamoDB table.
6. Networking:
   1. How do you set up a VPC with a public and private subnet? Why is this architecture useful?
7. Data Transfer:
   1. What are the options to transfer 1TB of data from an on-premise server to S3?
8. AWS IAM:
   1. How would you manage permissions for multiple users in an organization?
9. Monitoring:
   1. Explain how you would use CloudWatch to monitor the performance of an application hosted on AWS.
10. AWS Glue:
    1. How would you use AWS Glue to create an ETL pipeline for transforming data from S3 to Redshift?

4. Data Engineering Tools Mock Interview Questions

1. Pandas Performance:
   1. How would you optimize a Pandas DataFrame operation to process a dataset with 10 million rows?
2. Data Validation:
   1. Write a Pandas pipeline to clean, validate, and enrich raw customer data.
3. PySpark Basics:
   1. Explain the differences between Spark RDDs and DataFrames. When would you use one over the other?
4. Databricks:
   1. How do you manage a large ETL job in Databricks, ensuring scalability and fault tolerance?
5. Partitioning in Spark:
   1. How does partitioning improve Spark job performance? Write an example.
6. Distributed Computing:
   1. Explain how Spark handles fault tolerance in a distributed system.
7. Data Lake:
   1. What are the challenges of designing a data lake? How do tools like Databricks address them?
8. ETL Pipelines:
   1. Design an ETL pipeline to ingest JSON data from an API, transform it, and store it in a SQL database.
9. Data Serialization:
   1. Discuss the pros and cons of using Avro, Parquet, and JSON for storing large datasets.
10. Real-Time Data:
    1. Write a PySpark job to process streaming data from Kafka and store aggregated results in HDFS.