10+ Data Engineering Analyst Interview Questions and Answers

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

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

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

Window analytical functions are used to perform calculations across a set of table rows related to the current row.

• Window functions operate on a set of rows related to the current row

• They allow calculations to be performed across a group of rows

• Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE()

• They are used with the OVER() clause in SQL queries

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

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).

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

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