30+ AWS Data Engineer Interview Questions and Answers

I have used various libraries in my project such as Pandas, NumPy, Matplotlib, etc. and I import them using the import statement.

• I have used Pandas for data manipulation and analysis

• I have used NumPy for numerical operations

• I have used Matplotlib for data visualization

• I import them using the import statement followed by the library name

The * symbol is used to pass variable number of arguments and keyword arguments in Python functions.

• The * symbol before a parameter name in a function definition allows it to accept variable number of arguments as a tuple.

• The ** symbol before a parameter name in a function definition allows it to accept variable number of keyword arguments as a dictionary.

• The * symbol before an iterable object in a function call unpacks it into separate arguments.

• The ** symbol before a dictio...read more

Use Glue job to modify null salaries with average salary and find count of employees by joining date.

• Create a Glue job to read data, modify null salaries with average salary, and count employees by joining date

• Use Glue connectors to connect to data sources like S3, RDS, or Redshift

• Data connections in Glue service are used to define the connection information to data sources

• Example: Use Glue job to read employee data from S3, calculate average salary, replace null values, and ...read more

Program to find greatest element in a window of size 3

• Iterate through the list and maintain a window of size 3

• Find the maximum element in each window and store it in a separate list

• Return the list of maximum elements

Explaining joins and total count for two tables

• Joins combine data from two or more tables based on a common column

• Inner join returns only the matching rows from both tables

• Left join returns all rows from the left table and matching rows from the right table

• Right join returns all rows from the right table and matching rows from the left table

• Full outer join returns all rows from both tables

• Total count is the number of rows in the resulting joined table

The function to extract the first letter of the firstname in a column varies based on the data manipulation package used.

• Use SUBSTR function in SQL

• Use str_extract function in R

• Use substring function in Python

Python provides built-in libraries to parse JSON messages easily.

• Use the json module to load and parse JSON data

• Access the data using keys and indexes

• Use loops to iterate through JSON arrays

• Use dumps() method to convert Python objects to JSON format

Data governance is implemented through policies, processes, and tools to ensure data quality, security, and compliance.

• Establish data governance policies and procedures to define roles, responsibilities, and processes for managing data

• Implement data quality controls to ensure accuracy, completeness, and consistency of data

• Utilize data security measures such as encryption, access controls, and monitoring to protect sensitive data

• Enforce compliance with regulations and standard...read more

To print current time in Python, use the datetime module.

• Import the datetime module

• Use the now() method to get the current date and time

• Use the strftime() method to format the time as a string

SQL dense rank and Python removing duplicates

• For SQL dense rank, use the DENSE_RANK() function to assign a unique rank to each row within a partition

• For Python removing duplicates, use the set() function to remove duplicates from a list or use pandas library to drop duplicates from a DataFrame

AWS Glue is a fully managed ETL service that makes it easy to move data between data stores.

• AWS Glue crawls your data sources and constructs a data catalog using metadata.

• It then generates ETL code to transform and move data between data stores.

• AWS Glue supports various data sources like Amazon S3, JDBC, Amazon RDS, etc.

• It also provides a serverless environment to run your ETL jobs.

• AWS Glue integrates with other AWS services like Amazon Athena, Amazon EMR, etc.

AWS is a cloud computing platform provided by Amazon with a scalable and flexible architecture.

• AWS stands for Amazon Web Services

• It offers a wide range of cloud services including computing power, storage, and databases

• AWS architecture is designed to be scalable, secure, and cost-effective

• Examples of AWS services include EC2 for virtual servers, S3 for storage, and RDS for databases

Python code to find repeated numbers and their concurrency

• Iterate through the array and use a dictionary to store the count of each number

• Iterate through the dictionary to find numbers with count greater than 1 and their concurrency

AWS Glue is the equivalent of Data Factory in AWS, providing ETL capabilities for data integration and transformation.

• AWS Glue is a fully managed ETL (Extract, Transform, Load) service.

• It allows users to prepare and transform data for analytics.

• Glue can automatically discover and categorize data using its Data Catalog.

• It supports various data sources like S3, RDS, and Redshift.

• Glue jobs can be triggered on-demand or on a schedule.

Lambda Asynchronous invocation allows decoupling of services by triggering functions without waiting for a response.

• Lambda function is invoked by an event source like S3, SNS, or API Gateway.

• The event source sends the event to Lambda, which queues the event for processing.

• Lambda scales automatically to handle the incoming events concurrently.

• The function processes the event and can send the result to another service or store it in a database.

• Example: An S3 bucket triggers a L...read more

AWS Kinesis is a managed service that enables real-time processing of streaming data at scale.

• Kinesis can handle large amounts of data in real-time from various sources such as IoT devices, social media, and logs.

• It allows data to be processed in real-time using AWS Lambda, Kinesis Analytics, or Kinesis Data Firehose.

• Kinesis can be used for various use cases such as real-time analytics, machine learning, and fraud detection.

• Kinesis provides various features such as data encry...read more

Creating a dataframe from multiple lists using PySpark code.

• Import necessary libraries like pyspark.sql.

• Create lists of data.

• Create a SparkSession.

• Convert lists to RDDs and then to a DataFrame.

• Display the DataFrame.

Data Lake is a storage repository that holds a vast amount of raw data in its native format, while Data Warehouse is a structured repository for processed and analyzed data.

• Data Lake stores raw, unstructured data in its original form for future processing and analysis.

• Data Warehouse stores structured, processed data for querying and reporting.

• Data Lake is ideal for big data analytics and machine learning applications.

• Data Warehouse is optimized for complex queries and busines...read more

Create a Directed Acyclic Graph (DAG) to execute Python scripts in parallel.

• Define tasks for each Python script to be executed in parallel

• Use a parallelism parameter to specify the number of tasks to run concurrently

• Set up dependencies between tasks to ensure proper execution order

• Use a DAG scheduler like Apache Airflow to manage and execute the DAG

JSON is a text-based format for data interchange, while Parquet is a columnar storage format optimized for performance and efficiency.

• JSON is human-readable, while Parquet is binary and more efficient for storage.

• Parquet supports complex data types and nested structures, making it suitable for big data processing.

• JSON files can be larger in size compared to Parquet files due to their text-based nature.

• Parquet is optimized for read-heavy operations, making it ideal for analyti...read more

Lambda for event-driven processing, Glue for ETL jobs

• Use Lambda for event-driven processing, such as real-time data processing or triggering actions based on events

• Use Glue for ETL (Extract, Transform, Load) jobs, especially when dealing with large volumes of data or complex transformations

• Lambda is serverless and scales automatically, while Glue provides managed ETL capabilities with built-in connectors to various data sources

• Consider using Lambda for small, quick tasks and ...read more

Understanding SQL window functions for advanced data analysis and reporting.

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

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

• Example: SELECT employee_id, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank FROM employees;

• Window functions can be used with PARTITION BY to segment data, e.g., SELECT department, employee_id, SUM(salary) OVER (PARTITION BY...read more

AWS Lambda is a serverless compute service that runs code in response to events, automatically managing the compute resources.

• Event-Driven: AWS Lambda functions are triggered by events such as changes in data, HTTP requests via API Gateway, or messages from SQS.

• Scalability: Lambda automatically scales your application by running code in response to each trigger, handling thousands of requests simultaneously.

• Pay-as-You-Go: You only pay for the compute time you consume, with no...read more

Types of compression include lossless and lossy compression.

• Lossless compression reduces file size without losing any data.

• Lossy compression reduces file size by removing some data, resulting in lower quality.

• Examples of lossless compression include ZIP, GZIP, and PNG.

• Examples of lossy compression include JPEG and MP3.

Partitioning and coalesce are techniques used in data processing to optimize performance and manage data distribution.

• Partitioning involves dividing data into smaller chunks based on a specified column or criteria, which helps in parallel processing and query optimization.

• Coalesce is used to reduce the number of partitions by combining smaller partitions into larger ones, which can improve query performance and reduce overhead.

• Partitioning is commonly used in distributed syst...read more

Two data lineage tools are Apache Atlas and Informatica Enterprise Data Catalog.

• Apache Atlas is an open source tool for metadata management and governance in Hadoop ecosystems.

• Informatica Enterprise Data Catalog provides a comprehensive data discovery and metadata management solution.

Flattening JSON involves transforming nested JSON structures into a simpler, one-dimensional format.

• Use libraries like `json_normalize` in Python to flatten JSON. Example: `pd.json_normalize(data)`.

• Flattening can be done by concatenating keys. For example, `{ 'a': { 'b': 1 } }` becomes `{ 'a_b': 1 }`.

• Consider using AWS Glue for ETL processes to flatten JSON data in data lakes.

• In JavaScript, you can use recursion to flatten JSON objects. Example: `function flatten(obj, prefix ...read more