How I Cracked Interviews At Apple, Uber, Atlassian & Databricks

Summary

In this video, the speaker shares insights on how to crack data engineering interviews at top companies like Uber, Apple, and Atlassian. The guide covers essential topics including SQL, DSA, data modeling, Spark, system design, and product sense. The speaker emphasizes the importance of mastering SQL and DSA, along with practical tips on data modeling and distributed computing concepts in Spark. They also explain how to approach system design and product sense rounds. The video aims to help viewers prepare effectively for data engineering roles and succeed in interviews by mastering key concepts and avoiding common mistakes.

Takeaways

• 😀 SQL is crucial for data engineering interviews. Focus on mastering medium to hard SQL problems, especially those involving window functions, rolling sums, and complex aggregation.
• 😀 DSA is essential for data engineering roles. Focus on easy to medium-level problems covering arrays, strings, linked lists, stacks, queues, recursion, and binary search.
• 😀 Data modeling skills are important, particularly concepts like star and snowflake schemas, fact and dimension tables, normalization, and slowly changing dimensions.
• 😀 For data modeling, practice by creating models for apps you use daily (e.g., Uber, Instagram) and validate them through SQL queries to solve real-world analytical problems.
• 😀 Spark knowledge is vital, including its architecture, memory management, partitioning vs. bucketing, and tuning executors for optimal performance in distributed computing tasks.
• 😀 Focus on mastering the 8 core DSA topics (arrays, strings, linked lists, stacks, queues, recursion, binary search, DP) before moving on to rarer topics like bit manipulation and binary trees.
• 😀 When preparing for system design interviews, always clarify functional and non-functional requirements before designing solutions like a real-time data dashboard.
• 😀 System design involves choosing the right technologies for ingestion, storage, and processing. Think about scalability, security, and best practices like backup and logging.
• 😀 Product sense can be tested in interviews. Understand how data engineering can improve product features by tracking metrics such as user engagement or feature adoption.
• 😀 Mastering the six areas—SQL, DSA, data modeling, Spark, system design, and product sense—will significantly enhance your chances of succeeding in top data engineering interviews.

Q & A

Why is SQL considered one of the most important skills for data engineering interviews?

SQL is crucial because it's the primary language for querying and manipulating data in most data engineering roles. Being comfortable with SQL allows you to efficiently handle complex data retrieval and manipulation tasks, which are core to the job. Mastering concepts like window functions and complex aggregations will help you tackle medium to hard-level problems typically asked in interviews.

What specific SQL topics should one focus on during preparation for data engineering interviews?

Key SQL topics to focus on include window functions (e.g., rolling sums and averages), complex aggregations (like using CASE WHEN inside SUM), and solving problems like 'Island and Water'. These areas help you handle real-world data processing scenarios effectively.

How should one approach DSA (Data Structures and Algorithms) for data engineering interviews?

For data engineering interviews, focusing on easy to medium difficulty problems is enough. Prioritize topics like arrays, strings, linked lists, stacks, queues, recursion, binary search, and 1D dynamic programming. While more advanced topics like bit manipulation and binary trees can be useful, they should be given lower priority compared to the basics.

Why is data modeling an important topic in data engineering interviews, and how can one practice it?

Data modeling is important because it forms the foundation of designing efficient data storage systems. Understanding concepts like star and snowflake schemas, normalization, and denormalization helps in structuring data for optimal analysis. A practical approach is to model data for real-world applications, such as Uber or Instagram, and test the model's performance by writing SQL queries to answer business questions.

What are some key concepts one should understand about Spark for data engineering interviews?

Spark knowledge is essential for distributed computing. Key concepts to focus on include understanding Spark architecture (executors, drivers), memory management, partitioning vs. bucketing, and performance tuning. Knowing how to optimize the number of executors and understanding common memory-related issues can set you apart in interviews.

What is the importance of system design knowledge for data engineering interviews?

System design is vital for understanding how to build scalable and efficient data pipelines. It helps you design the architecture for handling large volumes of data, including choosing the right technologies for storage, processing, and serving. Asking the right clarifying questions to determine functional and non-functional requirements is essential for building robust systems.

What are some example questions to ask when designing a system for a data engineering interview?

When designing a system, key questions to ask include: 'Does the system need to support real-time or batch processing?', 'What data sources are involved?', 'What is the expected data volume?', and 'What file formats need to be supported?'. These questions help you clarify the system requirements and determine the appropriate technologies and architectures.

What is product sense in the context of data engineering, and how can it be demonstrated in interviews?

Product sense refers to the ability to assess how data engineering work contributes to the success of a product or feature. In interviews, you can demonstrate product sense by defining metrics that evaluate the success of a feature, such as tracking user engagement or calculating stickiness ratios. This shows you can think beyond the technical and consider the impact on users and the business.

How can you track the success of a new feature like Facebook's reactions feature?

To track the success of a new feature like reactions, you could measure metrics such as daily and monthly active users, the stickiness ratio, and the percentage of users who interact with the feature compared to commenting. These metrics provide insights into user engagement and whether the feature is fulfilling its purpose.

How can practicing with real-world applications like Uber or Instagram help in preparing for data engineering interviews?

Practicing with real-world applications helps you understand business processes and design data models that reflect real-world scenarios. For example, designing a data model for Uber helps you think about entities like riders and drivers, events like rides and payments, and how to structure data for analytical queries. This hands-on approach strengthens both your technical and problem-solving skills.