Welcome to the AWS data engineer interview questions and answers blog! AWS data engineering is among the most in-demand roles in the significant data analytics segment. An organization's typical data engineer must work with various departments and ensure the sourced data is compelling and used for decision-making. In large-scale organizations, data engineers play a significant role in understanding customer behavior by building machine learning & predictive models.
The AWS data engineer combines multiple technologies, programming languages, tools, etc. You can expect the interview questions from cloud basics, Python, C++, Java, data structures, algorithms, ETL design, data modeling, SQL, etc. For the sake of simplifying this and to give you explicit knowledge of each segment of data engineering, we have divided this AWS data engineer interview questions into the areas:
Amazon Web Services (AWS) is one of the leading public cloud providers in the market and strongly competes with other cloud providers like Azure and Google Cloud. It has data centers worldwide, offering 200 fully featured services.
AWS is a cost-effective scalable cloud solution that offers on-demand computing, storage, content delivery, database storage, analytics, etc. It is simple to use and streamlines multiple areas of an organization.
Data has become one of the essential elements of day-to-day business operations because of the value it brings. Data should be collected, cleansed, and stored correctly to support decision-making.
Data Engineering is a combination of technologies and tools that helps organizations collect, store, and analyze massive volumes of data. This process also involves data validation, transformation, testing, etc.
Following are the general roles you can see when it comes to data engineering:
AWS data engineering uses Amazon cloud services to design, build, and manage end-to-end databases and analytical processes that support an organization's various applications and digital services.
AWS data engineering is the same as other data engineering practices, but we use Amazon web services as a cloud platform here.
The following are the widely used tools when opting for AWS data engineering:
1) Data Ingestion: Amazon Kinesis Firehose, AWS Snowball, AWS Storage Gateway
2) Storage: Amazon S3
3) Data Integration: AWS Glue
4) Data Warehouse: Amazon Redshift
5) Data Visualization: Amazon QuickSight
AWS Glue is a serverless integration platform that integrates data from diversified sources. It helps analytics users to discover, structure, integrate, and move data to targeted destinations.
Apart from data integration operations, it supports various tasks such as application development, AI & machine learning development, workflow implementation, running jobs, authoring, and more.
Amazon Simple Storage Service (S3) is an object storage service offered by AWS. It offers storage, scalability, security, availability, and performance that today's global organizations need. S3 is suitable for all business sizes and is used for different purposes such as websites, data lakes, mobile applications, IoT devices, Enterprise applications, big data analytics, etc.
AWS Redshift is a cloud-based enterprise-grade data warehouse platform. It is a fully managed platform and intelligently scales to offer optimal performance when required. Redshift is a cost-effective platform; you only have to pay for usage, not when idle.
Amazon EC2 is called Elastic Compute Cloud, a fast & scalable cloud computing service. It offers the required computing power to build and deploy applications faster than ever. EC2 offers 700 instances (hosted virtual machines) to run applications.
AWS Lambda is a serverless computing platform that helps developers to run code without managing servers. It is an event-driven service, and computing resources are automatically managed.
AWS Glue Data Catalog is a metadata repository for all data sources and contains reference data for all jobs in Glue. Data Catalog is one of the essential tasks to perform when you create a data lake or data design. It acts as an index to the schema, location, and many other data metrics. Moreover, it acts as a guide to create & monitor ETL jobs.
Amazon Elastic MapReduce (EMR) is a managed cluster platform specialized in processing large amounts of data. It facilitates the required environment to run big data frameworks like Hadoop and Spark on the AWS platform. EMR is majorly applied for analyzing data related to logs, data warehousing, web indexing, financial information, bioinformatics, etc.
Amazon Kinesis is a real-time data processing and analyzing platform. It simplifies the process of handling streaming data at a large scale. A Kinesis Data Streams application reads data from stream data records and integrates this information with dashboards to make data-driven decisions.
Amazon DynamoDB is a scalable database designed to support high-performance applications.DynamoDB works mainly with three essential components: tables, attributes, items, etc.
Amazon Aurora is a cloud-based, fully scalable relational database service that works with MySQL and PostgreSQL.
AWS Data Pipeline is an ETL workflow service that streamlines data transfer between different sources. It facilitates operations like data transformation, ETL scheduling, etc.
Amazon Athena is a cloud-based query service that helps users write simple SQL queries to analyze data in S3. You can point Athena to any data in s3 and run queries to get results.
ETL stands for Extract, Transform, and Load and facilitates the data integration. The extract stage is the very first stage; at this stage, the data will be extracted from different sources and stored in a place. The transform stage loads all the extracted data into a targeted system or warehouse. The final layer is the Transformation layer, where the business logic is applied to turn data into the required format.
ETL and SQL play a significant role in data engineering and warehousing. ETL stands for Extract, transform, and this approach is designed to extract data from multiple sources, apply some transformations, and load it into the targeted system. SQL stands for structured query language, and SQL commands interact with database tables and perform required operations.
OLAP and OLTP are data processing systems that store and analyze business data. OLAP stands for online analytical processing and specializes in grouping data so it is easy to explore different aspects. Meanwhile, OLTP stands for online transaction processing and is designed to efficiently store and update high volumes of transactional data.
The primary use of the OLAP system is to aggregate and analyze large volumes of data; OLTP systems are mainly used for database transactions.
The other significant difference is the OLAP system for analyzing complex data, generating reports, and spotting data trends. OLTP systems are used to process orders, manage customer accounts, update inventory, etc.
ETL & OLAP are integral to data warehousing and crucial to data movement. After finishing the ETL stages, that data will be available in Data warehouse systems, but it takes longer to generate cross-tab reports from these data. OLAP servers eliminate this obstacle and enhance the report generation process.
The ETL pipeline combines processes that extract data from single or multiple sources and move the collected data into a targeted database. These pipelines consist of three significant strategies, extract, transform, and load, which are interdependent to move data from source to destination.
ETL pipelines enhance data handling processes and bring advantages like easy access to data, simplifying developers' work, enhanced data migration, improved data analysis, etc.
An operational data store (ODS) is a data warehouse database complementary aspect. This data store offers a faster and more lightweight data processing environment for real-time data analytics and reporting. ODS integrates diversified data sources and supports real-time operational reporting, decision-making, controls, etc.
ETL & ELT (extract, Load, Transform) are data processing approaches to improve data integrity and quality. The significant difference between these 2 data processing mechanisms is that ETL transforms the data before loading it into a targeted database. In contrast, the transformation occurs in ELT after loading into a data warehouse.
The ETL approach is best for handling complex transformations and handles smaller relational databases. ELT is capable of handling large volumes of data faster than ETL.
ETL Partitioning or data partitioning is a querying technique to organize data hierarchically, improving query efficiency. Generally, partitioning can be structured by considering distinct values of single or multiple columns.
Partitioning enhances workflow performance, data integrity, and scalability by minimizing data complexity.
Incremental data load is a data management methodology in synchronizing data sets between OLTP and data mart systems; when any data modification occurs in the OLTP system, that gets reflected in the data mart so that updated information will be available in real-time to make business decisions.
Data mapping in ETL is a data migration technique that integrates matching fields of different sources into a centralized database. It is an approach to breaking down data and understanding its origin. There are three types of data mapping: manual, semi, and fully automated.
ETL data mapping process enhances organizational data structure, simplifies accessibility, improves data accessibility, etc.
Following are the five critical steps of the ETL workflow process:
Below outlined are the common ETL bugs:
ETL testing is a crucial stage that ensures the data is high in quality and serves the proper decision-making process. This process ensures that the data has been extracted, transformed, and loaded as per the transformation rules and completes validity checks.
ETL testing helps find duplicate data sets, data loss, corrupted data, etc.
Following are the typical stages involved in any ETL testing process:
Below are the common ETL testing types:
AWS Redshift is a cloud-based data warehouse management system capable of processing petabytes of data. It is equipped with massive parallel data processing (MPP) and analyzes data at high velocity. Like other AWS tools, it is easily set up with a few clicks and is infinitely scalable.
Following are the five major components of Amazon Redshift Architecture:
A cluster is a combination of one or multiple compute nodes. Apart from general nodes, every group has a leader node coordinating between nodes and managing external communication.
The Redshift Managed Storage (RMS) is a separate, highly scalable storage area where data warehouse data is stored.
The following are the six core features of the Redshift data warehouse:
By Tech Solidity
Last updated on February 2, 2024