ProjectPro

JUNE 6, 2025

So, let’s dive into the list of the interview questions below - List of the Top Amazon Data Engineer Interview Questions Explore the following key questions to gauge your knowledge and proficiency in AWS Data Engineering. Become a Job-Ready Data Engineer with Complete Project-Based Data Engineering Course !

Data Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

Apache Kafka facilitated seamless communication between microservices, and Prometheus/Grafana provided robust monitoring. This ensures that data engineers and analysts have access to comprehensive information about the datasets they work with, promoting better understanding and utilization of the available data.

Data Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

ProjectPro

JUNE 6, 2025

ETL is a process that involves data extraction, transformation, and loading from multiple sources to a data warehouse, data lake, or another centralized data repository. An ETL developer designs, builds and manages data storage systems while ensuring they have important data for the business.

Data Engineering 59

Data Engineering Data Engineer Engineering ETL Tools 59

ProjectPro

JUNE 6, 2025

Table of Contents What is Real-Time Data Ingestion? Data Collection The first step is to collect real-time data (purchase_data) from various sources, such as sensors, IoT devices, and web applications, using data collectors or agents.

Data Ingestion 40

Data Ingestion Kafka Google Cloud AWS 40

ProjectPro

JUNE 6, 2025

It was built from the ground up for interactive analytics and can scale to the size of Facebook while approaching the speed of commercial data warehouses. Presto allows you to query data stored in Hive, Cassandra, relational databases, and even bespoke data storage. CMAK is developed to help the Kafka community.

Big Data 49

Big Data Project Metadata Programming Language 49

ProjectPro

JUNE 6, 2025

They include relational databases like Amazon RDS for MySQL, PostgreSQL, and Oracle and NoSQL databases like Amazon DynamoDB. Database Variety: AWS provides multiple database options such as Aurora (relational), DynamoDB (NoSQL), and ElastiCache (in-memory), letting startups choose the best-fit tech for their needs.

AWS 40

AWS Database Amazon Web Services MySQL 40

ProjectPro

JUNE 6, 2025

FAQs on Data Engineering Skills Mastering Data Engineering Skills: An Introduction to What is Data Engineering Data engineering is the process of designing, developing, and managing the infrastructure needed to collect, store, process, and analyze large volumes of data. 2) Does data engineering require coding?

Data Engineering 49

Data Engineering Data Engineer Engineering Hadoop 49

ProjectPro

SEPTEMBER 16, 2021

NoSQL databases are the new-age solutions to distributed unstructured data storage and processing. The speed, scalability, and fail-over safety offered by NoSQL databases are needed in the current times in the wake of Big Data Analytics and Data Science technologies.

NoSQL 52

NoSQL Database Hadoop Big Data 52

Knowledge Hut

APRIL 25, 2024

Each of these technologies has its own strengths and weaknesses, but all of them can be used to gain insights from large data sets. As organizations continue to generate more and more data, big data technologies will become increasingly essential. Let's explore the technologies available for big data.

Big Data 98

Big Data Technology NoSQL Hadoop 98

ProjectPro

JUNE 6, 2025

are all present in logical data models. The process of creating logical data models is known as logical data modeling. Prepare for Your Next Big Data Job Interview with Kafka Interview Questions and Answers 2. How would you create a Data Model using SQL commands? List some of the benefits of data modeling.

Data Warehouse 53

Data Warehouse NoSQL PostgreSQL Relational Database 53

Confluent

MARCH 4, 2019

A trend often seen in organizations around the world is the adoption of Apache Kafka ® as the backbone for data storage and delivery. As mentioned earlier, companies today need to be able to process not only transactional data but also unstructured data coming from sources like logs.

Cloud 89

Cloud Banking Kafka NoSQL 89

ProjectPro

JUNE 6, 2025

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis.

Hadoop 40

Hadoop Project Big Data Scala 40

ProjectPro

JUNE 6, 2025

There are many cloud computing job roles like Cloud Consultant, Cloud reliability engineer, cloud security engineer, cloud infrastructure engineer, cloud architect, data science engineer that one can make a career transition to. PaaS packages the platform for development and testing along with data, storage, and computing capability.

Cloud Computing 53

Cloud Computing Cloud Amazon Web Services AWS 53

ProjectPro

JUNE 6, 2025

Build an Awesome Job Winning Data Engineering Projects Portfoli o Technical Skills Required to Become a Big Data Engineer Database Systems: Data is the primary asset handled, processed, and managed by a Big Data Engineer. You must have good knowledge of the SQL and NoSQL database systems.

Big Data 40

Big Data Data Engineering Data Engineer Engineering 40

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. Data storage and processing. NoSQL databases.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

JUNE 7, 2021

Master Nodes control and coordinate two key functions of Hadoop: data storage and parallel processing of data. Worker or Slave Nodes are the majority of nodes used to store data and run computations according to instructions from a master node. Data storage options. Hadoop nodes: masters and slaves.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

ProjectPro

JUNE 6, 2025

Spark saves data in memory (RAM), making data retrieval quicker and faster when needed. Spark is a low-latency computation platform because it offers in-memory data storage and caching. The cache() function or the persist() method with proper persistence settings can be used to cache data.

Hadoop 68

Hadoop Metadata Java Python 68

ProjectPro

JUNE 6, 2025

It has to be built to support queries that can work with real-time, interactive and batch-formatted data. Insights from the system may be used to process the data in different ways. This layer should support both SQL and NoSQL queries. Even Excel sheets may be used for data analysis.

Data Lake 40

Data Lake Data Warehouse Cloud Hadoop 40

Knowledge Hut

DECEMBER 26, 2023

Kafka Kafka is an open-source processing software platform. It is used to handle real-time data feeds and build real-time streaming apps. The applications developed by Kafka can help a data engineer discover and apply trends and react to user needs.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

U-Next

AUGUST 17, 2022

Because of this, all businesses—from global leaders like Apple to sole proprietorships—need Data Engineers proficient in SQL. NoSQL – This alternative kind of data storage and processing is gaining popularity. The term “NoSQL” refers to technology that is not dependent on SQL, to put it simply.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Knowledge Hut

DECEMBER 26, 2023

According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 10 9 gigabytes) globally by the year 2025. In other words, they develop, maintain, and test Big Data solutions. To become a Big Data Engineer, knowledge of Algorithms and Distributed Computing is also desirable.

Data Science 97

Data Science BI Data Mining Business Intelligence 97

Knowledge Hut

NOVEMBER 28, 2023

Concepts of IaaS, PaaS, and SaaS are the trend, and big companies expect data engineers to have the relevant knowledge. Kafka Kafka is one of the most desired open-source messaging and streaming systems that allows you to publish, distribute, and consume data streams. ETL is central to getting your data where you need it.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

AltexSoft

OCTOBER 30, 2021

Data engineer’s integral task is building and maintaining data infrastructure — the system managing the flow of data from its source to destination. This typically includes setting up two processes: an ETL pipeline , which moves data, and a data storage (typically, a data warehouse ), where it’s kept.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Increasingly, data warehouses and data lakes are moving toward each other in a general shift toward data lakehouse architecture.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Increasingly, data warehouses and data lakes are moving toward each other in a general shift toward data lakehouse architecture.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Knowledge Hut

APRIL 25, 2023

Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Knowledge Hut

OCTOBER 27, 2023

cvtColor(image, cv2.COLOR_BGR2GRAY) COLOR_BGR2GRAY) _, thresh = cv2.threshold(gray_image, threshold(gray_image, 127, 255, cv2.THRESH_BINARY) THRESH_BINARY) contours, _ = cv2.findContours(thresh, findContours(thresh, cv2.RETR_TREE, RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) boundingRect(max_cnt) else: return None image = cv2.imread("fingerprint.jpg")

Software Engineer 98

Software Engineer Software Engineering Coding Project 98

Cloud Academy

JANUARY 27, 2022

Some basic real-world examples are: Relational, SQL database: e.g. Microsoft SQL Server Document-oriented database: MongoDB (classified as NoSQL) The Basics of Data Management, Data Manipulation and Data Modeling This learning path focuses on common data formats and interfaces.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

JUNE 23, 2023

Other Competencies You should have proficiency in coding languages like SQL, NoSQL, Python, Java, R, and Scala. You should be thorough with technicalities related to relational and non-relational databases, Data security, ETL (extract, transform, and load) systems, Data storage, automation and scripting, big data tools, and machine learning.

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

ProjectPro

JUNE 6, 2025

Here are some role-specific skills you should consider to become an Azure data engineer- Most data storage and processing systems use programming languages. Data engineers must thoroughly understand programming languages such as Python, Java, or Scala. Who should take the certification exam?

Data Engineering 49

Data Engineering Data Engineer Engineering Certification 49

Knowledge Hut

NOVEMBER 19, 2023

As a result, data engineers working with big data today require a basic grasp of cloud computing platforms and tools. Businesses can employ internal, public, or hybrid clouds depending on their data storage needs, including AWS, Azure, GCP, and other well-known cloud computing platforms.

Data Engineering 52

Data Engineering Data Engineer Engineering PostgreSQL 52

ProjectPro

JUNE 4, 2015

Defining Architecture Components of the Big Data Ecosystem Core Hadoop Components 3) MapReduce- Distributed Data Processing Framework of Apache Hadoop MapReduce Use Case: >4)YARN Key Benefits of Hadoop 2.0 2) Hadoop Distributed File System (HDFS) - The default big data storage layer for Apache Hadoop is HDFS.

Hadoop 52

Hadoop Architecture IT Java 52

Rockset

JANUARY 23, 2020

While traditional RDBMS databases served well the data storage and data processing needs of the enterprise world from their commercial inception in the late 1970s until the dotcom era, the large amounts of data processed by the new applications—and the speed at which this data needs to be processed—required a new approach.

Database 40

Database MySQL Kafka NoSQL 40

Silectis

JANUARY 3, 2021

Hadoop / HDFS Apache’s open-source software framework for processing big data. JSON JavaScript Object Notation – a data-interchange format for storing and transporting data. Kafka Apache Kafka is the Apache Foundation’s open-source software platform for streaming.

Data Engineering 40

Data Engineering Data Engineer Engineering Non-relational Database 40

Knowledge Hut

JULY 24, 2023

As big data and machine learning have become more prevalent, SQL is increasingly being used to train and query predictive models, which may help businesses make better decisions. However, SQL is still widely used and will continue to play a vital role in data management.

Database 52

Database SQL Process NoSQL 52

AltexSoft

MARCH 14, 2023

Additionally, this modularity can help prevent vendor lock-in, giving organizations more flexibility and control over their data stack. Many components of a modern data stack (such as Apache Airflow, Kafka, Spark, and others) are open-source and free. But this distinction has been blurred with the era of cloud data warehouses.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

JUNE 2, 2024

These languages are used to write efficient, maintainable code and create scripts for automation and data processing. Databases and Data Warehousing: Engineers need in-depth knowledge of SQL (88%) and NoSQL databases (71%), as well as data warehousing solutions like Hadoop (61%).

Engineering 52

Engineering Amazon Web Services AWS Data Science 52