10+ Data Engineering Specialist Interview Questions and Answers

Creating data pipelines, processing requests, web crawling, scraping, and reading large CSV files in Python.

• Use tools like Apache Airflow or Luigi to create data pipelines

• Implement distributed computing frameworks like Apache Spark for processing millions of requests

• Utilize libraries like Scrapy or Beautiful Soup for web crawling and scraping

• Use pandas library in Python to efficiently read and process large CSV files

Query to identify and delete duplicate records in SQL

• Use a combination of SELECT and DELETE statements

• Identify duplicates using GROUP BY and HAVING clauses

• Delete duplicates based on a unique identifier or combination of columns

Handle incremental data by using tools like Apache Kafka for real-time data streaming and implementing CDC (Change Data Capture) for database updates.

• Utilize tools like Apache Kafka for real-time data streaming

• Implement CDC (Change Data Capture) for tracking database updates

• Use data pipelines to process and integrate incremental data

• Ensure data consistency and accuracy during incremental updates

AWS Lambda is a serverless computing service provided by Amazon Web Services.

• AWS Lambda allows you to run code without provisioning or managing servers.

• It automatically scales based on the incoming traffic.

• You only pay for the compute time you consume.

• Supports multiple programming languages like Node.js, Python, Java, etc.

• Can be triggered by various AWS services like S3, DynamoDB, API Gateway, etc.

Various optimisation techniques were used in my project to improve performance and efficiency.

• Implemented indexing to speed up database queries

• Utilized caching to reduce redundant data retrieval

• Applied parallel processing to distribute workloads efficiently

• Optimized algorithms to reduce time complexity

• Used query optimization techniques to improve database performance

Catalyst optimizer is a query optimization framework in Apache Spark that improves performance by applying various optimization techniques.

• It is a query optimization framework in Apache Spark.

• It improves performance by applying various optimization techniques.

• It leverages techniques like predicate pushdown, column pruning, and constant folding to optimize queries.

• Catalyst optimizer generates an optimized logical plan and physical plan for query execution.

map() and reduce() are higher-order functions used in functional programming to transform and aggregate data respectively.

• map() applies a given function to each element of an array and returns a new array with the transformed values.

• reduce() applies a given function to the elements of an array in a cumulative way, reducing them to a single value.

Transformations and actions are key concepts in Apache Spark for processing data.

• Transformations are operations that create a new RDD from an existing one, like map, filter, and reduceByKey.

• Actions are operations that trigger computation and return a result to the driver program, like count, collect, and saveAsTextFile.

Filters in DAX are used to manipulate data in Power BI reports. DAX calculations are used to create custom measures and columns.

• Filters in DAX include CALCULATE, FILTER, ALL, ALLEXCEPT, etc.

• DAX calculations are used to create custom measures like SUM, AVERAGE, etc.

• Examples: CALCULATE(SUM(Sales), FILTER(Products, Products[Category] = 'Electronics'))

Project architecture defines the structure and components of a data engineering project, ensuring scalability and efficiency.

• Define data sources: Identify where data will come from, e.g., databases, APIs, or IoT devices.

• Choose a data storage solution: Options include data lakes (e.g., AWS S3) or data warehouses (e.g., Snowflake).

• Implement data processing: Use ETL (Extract, Transform, Load) tools like Apache Spark or Apache Airflow.

• Design data pipelines: Create workflows to au...read more

Spark memory management optimizes resource allocation for efficient data processing in distributed computing environments.

• Spark uses a unified memory management model that divides memory into execution and storage regions.

• The default memory fraction for execution is 60%, while 40% is allocated for storage, but these can be configured.

• Spark employs a mechanism called 'Tungsten' for off-heap memory management, which reduces garbage collection overhead.

• Memory overhead can be mon...read more

Optimizing a report involves identifying inefficiencies and implementing improvements to enhance performance.

• Identify key performance indicators (KPIs) to focus on

• Streamline data collection and processing methods

• Utilize efficient algorithms and data structures

• Optimize database queries for faster retrieval

• Implement caching mechanisms to reduce processing time

Spark architecture enables distributed data processing using resilient distributed datasets (RDDs) and a master-slave model.

• Spark consists of a driver program that coordinates the execution of tasks across a cluster.

• The cluster manager (like YARN or Mesos) allocates resources for Spark applications.

• Data is processed in parallel using RDDs, which are immutable collections of objects.

• Spark supports various data sources, including HDFS, S3, and NoSQL databases.

• It provides high-l...read more