20+ Data Engineering Analyst Interview Questions and Answers

The problem requires returning an array where each element is the product of all elements in the input array except itself.

• Iterate through the array twice to calculate the product of all elements to the left and right of each element.

• Use two arrays to store the products of elements to the left and right of each element.

• Multiply the corresponding elements from the left and right arrays to get the final product array.

• Handle integer overflow by taking modulo MOD = 10^9 + 7.

• To so...read more

Find the maximum sum of a contiguous subarray within an array of integers.

• Use Kadane's algorithm to find the maximum subarray sum efficiently.

• Initialize two variables: max_sum and current_sum.

• Iterate through the array and update current_sum and max_sum accordingly.

• Return the max_sum as the result.

Find the 2nd highest salary employee in each department using PySpark.

• Read the CSV file into a DataFrame using spark.read.csv().

• Group the DataFrame by 'Department' and use the 'dense_rank()' function to rank salaries.

• Filter the DataFrame to get employees with a rank of 2.

• Select the 'Employee name' and 'Department' columns for the final output.

The number of executors required to load 200 Petabytes of data depends on the size of each executor and the available cache.

• Calculate the size of each executor based on available resources and data size

• Consider the amount of cache available for data processing

• Determine the optimal number of executors based on the above factors

Calculate the frequency of each unique string in an array and display the results.

• Use a dictionary to count occurrences: {'a': 3, 'b': 2, 'c': 1}.

• Iterate through the list and update counts for each character.

• Example: For input ['a', 'a', 'b'], output should be 'a,2' and 'b,1'.

• Utilize collections.Counter for a more concise solution.

Broadcasting is a technique used in Apache Spark to optimize data transfer by sending smaller data to all nodes in a cluster.

• Broadcasting is used to efficiently distribute read-only data to all nodes in a cluster to avoid unnecessary data shuffling.

• It is commonly used when joining large datasets with smaller lookup tables.

• Broadcast variables are cached in memory and reused across multiple stages of a Spark job.

• The limitation of broadcasting is that it can lead to out-of-memor...read more

To debug a slow block, start by identifying potential bottlenecks, analyzing logs, checking for errors, and profiling the code.

• Identify potential bottlenecks in the code or system that could be causing the slow performance.

• Analyze logs and error messages to pinpoint any issues or exceptions that may be occurring.

• Use profiling tools to analyze the performance of the code and identify areas that need optimization.

• Check for any inefficient algorithms or data structures that coul...read more

Accumulators are used for aggregating values across tasks, while Catalyst optimizer is a query optimizer for Apache Spark.

• Accumulators are variables that are only added to through an associative and commutative operation and can be used to implement counters or sums.

• Catalyst optimizer is a rule-based query optimizer that leverages advanced programming language features to build an extensible query optimizer.

• Catalyst optimizer in Apache Spark optimizes query plans by applying ...read more

Sorting arrays and lists of strings

• Use built-in sorting functions like sorted() or sort()

• Specify the key parameter to sort by a specific element in the strings

• Use reverse=True to sort in descending order

Lambda Architecture is a data processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods. Lambda function is a small anonymous function that can take any number of arguments, but can only have one expression.

• Lambda Architecture combines batch processing and stream processing to handle large amounts of data efficiently.

• Batch layer stores and processes large volumes of data, while speed layer processes r...read more

Airflow is a platform to programmatically author, schedule, and monitor workflows.

• Airflow is written in Python and uses Directed Acyclic Graphs (DAGs) to define workflows.

• It has a web-based UI for visualization and monitoring of workflows.

• Airflow consists of a scheduler, a metadata database, a web server, and an executor.

• Tasks in Airflow are defined as operators, which determine what actually gets executed.

• Example: A DAG can be created to schedule data processing tasks like E...read more

Case classes in Python are classes that are used to create immutable objects for pattern matching and data modeling.

• Case classes are typically used in functional programming to represent data structures.

• They are immutable, meaning their values cannot be changed once they are created.

• Case classes automatically define equality, hash code, and toString methods based on the class constructor arguments.

• They are commonly used in libraries like PySpark for representing structured da...read more

Broadcast Variables are read-only shared variables that are cached on each machine in a Spark cluster rather than being sent with tasks.

• Broadcast Variables are used to efficiently distribute large read-only datasets to all worker nodes in a Spark cluster.

• They are useful for tasks that require the same data to be shared across multiple stages of a job.

• Broadcast Variables are created using the broadcast() method in Spark.

• Example: broadcasting a lookup table to be used in a join...read more

RDD stands for Resilient Distributed Dataset in Spark, which is an immutable distributed collection of objects.

• RDD is the fundamental data structure in Spark, representing a collection of elements that can be operated on in parallel.

• RDDs are fault-tolerant, meaning they can automatically recover from failures.

• RDDs support two types of operations: transformations (creating a new RDD from an existing one) and actions (triggering computation and returning a result).

Join operations in SQL combine data from multiple tables based on related columns, enhancing data analysis capabilities.

• Inner Join: Returns records with matching values in both tables. Example: Joining 'Employees' and 'Departments' on 'DeptID'.

• Left Join: Returns all records from the left table and matched records from the right. Example: All 'Employees' with their 'Departments', even if some don't belong to any.

• Right Join: Returns all records from the right table and matched ...read more

RDD Lineage is the record of transformations applied to an RDD and the dependencies between RDDs.

• RDD Lineage tracks the sequence of transformations applied to an RDD from its source data.

• It helps in fault tolerance by allowing RDDs to be reconstructed in case of data loss.

• RDD Lineage is used in Spark to optimize the execution plan by eliminating unnecessary computations.

• Example: If an RDD is created from a text file and then filtered, the lineage would include the source file...read more

Prepartition is the process of dividing data into smaller partitions before performing any operations on it.

• Prepartitioning helps in improving query performance by reducing the amount of data that needs to be processed.

• It can also help in distributing data evenly across multiple nodes in a distributed system.

• Examples include partitioning a large dataset based on a specific column like date or region before running analytics queries.

Use SQL query with ORDER BY and LIMIT to find nth highest salary.

• Use ORDER BY clause to sort salaries in descending order

• Use LIMIT to specify the nth highest salary

• Example: SELECT salary FROM employees ORDER BY salary DESC LIMIT n-1, 1

Delta Live Tables are a framework for building reliable data pipelines in Databricks, enabling real-time data processing.

• Delta Live Tables simplify ETL processes by automating data pipeline management.

• They support incremental data processing, allowing for real-time updates.

• Users can define data transformations using SQL or Python, making it accessible.

• Example: A retail company can use Delta Live Tables to continuously update sales data from multiple sources.

• They provide built...read more

Parquet files are columnar storage files optimized for big data processing and analytics.

• Columnar storage format, allowing efficient data compression and encoding.

• Designed for use with big data processing frameworks like Apache Hadoop and Apache Spark.

• Supports complex nested data structures, making it suitable for various data types.

• Parquet files can significantly reduce storage costs and improve query performance.

• Example: A Parquet file can store a large dataset of user acti...read more

Basic SQL questions cover fundamental concepts like SELECT, JOIN, and WHERE clauses essential for data retrieval.

• SELECT statement: Used to select data from a database. Example: SELECT * FROM employees;

• JOIN operations: Combine rows from two or more tables based on a related column. Example: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id;

• WHERE clause: Filters records based on specified conditions. Example: SELECT * FROM products WHERE price > 100;

• GROUP...read more

Fibonacci series is a sequence of numbers where each number is the sum of the two preceding ones.

• Initialize variables for the first two numbers in the series (0 and 1)

• Use a loop to calculate the next number by adding the previous two numbers

• Continue this process until reaching the desired length of the series

Developed a data analysis tool to predict customer churn using machine learning algorithms.

• Used Python for data preprocessing and model building

• Implemented logistic regression and random forest algorithms

• Evaluated model performance using metrics like accuracy, precision, and recall