Data Engineer Behavioral Interview Questions

Data Engineer Interview Questions On Data Lake

Data lakes are the ideal way to store the company’s historical data because they can store a lot of data at a low cost. Data lake enables users to switch back and forth between data engineering and use cases like interactive analytics and machine learning. Azure Data Lake, a cloud platform, supports big data analytics by providing unlimited storage for structured, semi-structured, or unstructured data. Take a look at some important data engineering interview questions on Azure Data Lake.

Discuss The Different Windowing Options Available In Azure Stream Analytics

Stream Analytics has built-in support for windowing functions, allowing developers to quickly create complicated stream processing jobs. Five types of temporal windows are available: Tumbling, Hopping, Sliding, Session, and Snapshot.

Tumbling window functions take a data stream and divide it into discrete temporal segments, then apply a function to each. Tumbling windows often recur, do not overlap, and one event cannot correspond to more than one tumbling window.
Hopping window functions progress in time by a set period. Think of them as Tumbling windows that can overlap and emit more frequently than the window size allows. Events can appear in multiple Hopping window result sets. Set the hop size to the same as the window size to make a Hopping window look like a Tumbling window.
Unlike Tumbling or Hopping windows, Sliding windows only emit events when the window’s content changes. As a result, each window contains at least one event, and events, like hopping windows, can belong to many sliding windows.
Session window functions combine events that coincide and filter out periods when no data is available. The three primary variables in Session windows are timeout, maximum duration, and partitioning key.
Snapshot windows bring together events having the same timestamp. You can implement a snapshot window by adding System.Timestamp to the GROUP BY clause, unlike most windowing function types that involve a specialized window function ).

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What Are The Roles And Responsibilities Of Data Engineer

Some of the roles and responsibilities of a data engineer are

Create and implement ETL data pipeline for a variety of clients in various sectors.
Generate accurate and useful data-driven solutions using data modeling and data warehousing techniques.
Interact with other teams and help them by delivering relevant datasets for analysis.
Build data pipelines for extraction and storage tasks by employing a range of big data engineering tools and various cloud service platforms.

How Does Big Data Analytics Help Increase The Revenue Of A Company

1. Best questions to ask interviewer at the end of an interview? 2. Top ...

Data Analytics helps the companies of todays world in numerous ways. Following are the foundational concepts in which it helps:

Effective use of data to relate to structured growth
Effective customer value increase and retention analysis
Manpower forecasting and improved staffing methods
Bringing down the production cost majorly

Learn more about the salary structure of this professional in our blog on the Big Data Engineer Salary in India!

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Is Coding Required To Learn Data Science

Sometimes. While it is not necessary to have advanced coding skills, it is good if learners are comfortable with data analytics, data management, and data visualization.

Fundamental knowledge in C/C++, Python, R, Java, or SQL can boost your learning process. Learning any of these programming languages would serve you in grouping the unstructured datasets.

How Can You Identify Missing Values In A Data Frame

The isnull function help to identify missing values in a given data frame.

The syntax is DataFrame.isnull

It returns a dataframe of boolean values of the same size as the data frame in which missing values are present. The missing values in the original data frame are mapped to true, and non-missing values are mapped to False.

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Data Engineer Interview Questions On Python

Python is crucial in implementing data engineering techniques. Pandas, NumPy, NLTK, SciPy, and other Python libraries are ideal for various data engineering tasks such as faster data processing and other machine learning activities. Data engineers primarily focus on data modeling and data processing architecture but also need a fundamental understanding of algorithms and data structures. Take a look at some of the data engineer interview questions based on various Python concepts, including Python libraries, algorithms, data structures, etc. These data engineer interview questions cover Python libraries like Pandas, NumPy, and SciPy.

How Comfortable Are You Presenting Your Insights

McKinsey & Company – Data Engineer Interview Experience | Experienced | Must Watch Important Topics

Interviewers want to know youre confident in your communication skills and can effectively communicate complex ideas. With a question like this, walk the interviewer through your process:

How you prepare data presentations
Strategies you use to make data accessible
What tools you use in presentations

Also, the ability to present virtually is vitally important in todays market. Have several recent experiences to talk about, both in-person and virtual. This is a common question in data visualization interviews.

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What Happens When The Block Scanner Detects A Corrupt Data Block

The following steps occur when the block scanner detects a corrupt data block:

First and foremost, when the Block Scanner detects a corrupted data block, DataNode notifies NameNode.
NameNode begins the process of constructing a new replica from a corrupted block replica.
The replication factor is compared to the replication count of the right replicas. The faulty data block will not be removed if a match is detected.

Data Engineer Interview Questions On Big Data

Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis. Complex algorithms, specialized professionals, and high-end technologies are required to leverage big data in businesses, and big Data Engineering ensures that organizations can utilize the power of data.

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis, data migration, data processing architecture, data storage, big data analytics, etc.

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How Would You Convey Insights And The Methods You Use To A Non

Youll find a lot of variations to this question, but the objective is always the same: to assess your ability to communicate complex subject matter and make it accessible. Data analysts often work cross-functionally, and this is a key skill they must possess.

Have a few examples ready and use a framework to describe them. You might say:

The marketing team wanted to better segment customers, so, after gaining an understanding of their motivations and goals for the project, I presented several segmenting options and talked them through trade-offs.

I felt that K-means clustering would be the best method for their objective, so I made a presentation about how the method worked, potential strategies for visualizing the new segments, described key benefits, and ultimately, talked about potential trade-offs.

What Do You Mean By Blocks And Block Scanner

Programming interview questions and practice resources

Block is the smallest unit of a data file and is regarded as a single entity. When Hadoop comes across a large data file, it automatically breaks it up into smaller pieces called blocks.

A block scanner is implemented to check whether the loss-of-blocks generated by Hadoop are successfully installed on the DataNode.

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Wrapping Up And Next Steps

Data engineering is a fantastic career choice for anyone with an analytic mind and a curiosity about the kind of information they can find in massive datasets.Learning the right skills to break into this career can be relatively straightforward. Once youre comfortable with SQL and Python, youll have the knowledge you need to start learning how to design data models and build data warehouses. If you find that data engineering isnt right for you, but you still want to work with data, many of these skills are transferable to careers in data science, machine learning, and data analytics.

We encourage you to check out some of the great resources we have here at Educative and wish you success in your interviews!

To get started learning these concepts and more, check out Educatives learning path Python for Programmers

Happy learning!

What Are The Features Of Hadoop

Hadoop has the following features:

It is open-source and easy to use.
Hadoop is extremely scalable. A significant volume of data is split across several devices in a cluster and processed in parallel. According to the needs of the hour, the number of these devices or nodes can be increased or decreased.
Data in Hadoop is copied across multiple DataNodes in a Hadoop cluster, ensuring data availability even if one of your systems fails.
Hadoop is built in such a way that it can efficiently handle any type of dataset, including structured , semi-structured , and unstructured . This means it can analyze any type of data regardless of its form, making it extremely flexible.
Hadoop provides faster data processing. More Features.

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Give A Brief Overview Of The Major Hadoop Components

Working with Hadoop involves many different components, some of which are listed below:

Hadoop Common: This comprises all the tools and libraries typically used by the Hadoop application.
Hadoop Distributed File System : When using Hadoop, all data is present in the HDFS, or Hadoop Distributed File System. It offers an extremely high bandwidth distributed file system.
Hadoop YARN: The Hadoop system uses YARN, or Yet Another Resource Negotiator, to manage resources. YARN can also be useful for task scheduling.
Hadoop MapReduce: Hadoop MapReduce is a framework for large-scale data processing that gives users access.

What Is The Biggest Professional Challenge You Have Overcome As A Data Engineer

SQL Interview Questions For Data Scientists And Data Engineers – Tips For Practicing SQL Interviews

Hiring managers often ask this question to learn how you address difficulties at work. Rather than learning about the details of these difficulties, they typically want to determine how resilient you are and how you process what you learn from challenging situations. When you answer, try using the STAR method, which involves stating the situation, task, action and result of the circumstances.

Example:âLast year, I served as the lead data engineer for a project that had insufficient internal support. As a result, my portion of the project fell behind schedule and I risked disciplinary measures. After my team missed the first deadline, I took the initiative to meet with the project manager and proposed possible solutions. Based on my suggestions, the company assigned additional personnel to my team and we were able to complete the project successfully within the original timeline.â

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Amazon Data Engineer Interview Questions

In this article, Ill teach you everything you need to know about the Amazon Data Engineer interview. Ill cover what questions youre likely to be asked, how to prepare for the interview, and what Amazon is looking for in an ideal candidate. Amazon is one of the largest tech companies in the world, and they are always hiring top talent. If youre looking for a job as a data engineer, youll need to ace the interview process. This Amazon Data Engineer Interview Guide will give you all the information you need to do just that.

Overview of the Data Engineer Interview Process
Amazon Data Engineer Interview Question Examples
Tips for Amazon Data Engineer Interview Preparation
Frequently Asked Questions

What Is Hadoop How Is It Related To Big Data Can You Describe Its Different Components

This question is most commonly asked by hiring managers to verify your knowledge and experience in data engineering. You should tell them that Big data and Hadoop are related to each other as Hadoop is the most common tool for processing Big data, and you should be familiar with the framework.

With the escalation of big data, Hadoop has also become popular. It is an open-source software framework that utilizes various components to process big data. The developer of Hadoop is the Apache foundation, and its utilities increase the efficiency of many data applications.

Hadoop comprises of mainly four components:

HDFS stands for Hadoop Distributed File System and stores all of the data of Hadoop. Being a distributed file system, it has a high bandwidth and preserves the quality of data.

MapReduce processes large volumes of data.

Hadoop Common is a group of libraries and functions you can utilize in Hadoop.

YARN deals with the allocation and management of resources in Hadoop.

Free Course: Getting Started with Hadoop

What Do You Understand About Amazon Virtual Private Cloud

The Amazon Virtual Private Cloud enables you to deploy AWS resources into a custom virtual network.
This virtual network is like a typical network run in your private data center, but with the added benefit of AWS’s scalable infrastructure.
Amazon VPC allows you to create a virtual network in the cloud without VPNs, hardware, or real data centers.
You can also use Amazon VPC’s advanced security features to give more selective access to and from your virtual network’s Amazon EC2 instances.

The Rise In Popularity And Demand For Data Science Courses

Top 10 Behavioral Interview Questions and Sample Answers

analysis has predicted that the market size of the data science platform will reach USD 140.9 billion by 2024, at a Compound Annual Growth Rate of 30% during the forecast period.

The demand for Data Science is primarily a result of businesses worldwide trying to remain competitive by increasingly using digital technologies. On the other hand, there is a huge demand for Data Scientists because there are not enough skilled professionals to fill the vacancies created by these businesses. This short supply, in turn, has made Data Science one of the highest-paying jobs in the world. Hence, Data Science aspirants are looking for the best Data Scientist courses.

The role of a Data Science professional is to essentially help businesses make informed decisions and solve critical problems through insights generated by interpreting and managing large, complex sets of data.

Intellipaat has collaborated with several top-rated institutions to bring you Data Science courses tailored for anyone who wishes to pursue this career. We have certification programs like Advanced Certification in Data Science and AI by CCE, IIT Madras, PG certification in Data Science and Machine Learning by MNIT, Jaipur, Masters in Data Science online program, Data Science online course, Big Data and Data Science Masters course, and the MCA degree program with a specialization in Data Science by Jain University.

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As A Data Engineer How Have You Handled A Job

Data engineers have a lot of responsibilities, and its a genuine possibility that youll face challenges while on the job, or even emergencies. Just be honest and let them know what you did to solve the problem. If you have yet to encounter an urgent issue while on the job or this is your first data engineering role, tell your interviewer what you would do in a hypothetical situation. For example, you can say that if data were to get lost or corrupted, you would work with IT to make sure data backups were ready to be loaded, and that other team members have access to what they need.

What Are Some Important Features Of Hadoop

Following is a list of some Hadoop features that make Hadoop popular in the industry, more reliable to use, and the most powerful Big Data tool:

Hadoop is an open-source and free-to-use framework. It is an open-source project, so the source code is available online for anyone. Anyone can understand and use it and make some modifications according to their industry requirement.
Hadoop is fault-tolerant. If somehow any of your systems got crashed, it provides data availability. In Hadoop, data is replicated on various DataNodes in a Hadoop cluster that ensures data availability every time. By default, Hadoop makes 3 copies of each file block and stores it in different nodes.
Hadoop provides parallel computing, which ensures faster data processing.
In Hadoop, data is stored in separate clusters away from the operations.
Hadoop provides high availability of data. Its fault tolerance feature provides high availability in the Hadoop cluster. If any DataNode goes down, you can retrieve the same data from any other node where the data is replicated.
Hadoop provides the data redundancy feature. It is used to ensure no data loss.
Hadoop is cost-effective compared to other traditional relational databases that require expensive hardware and high-end processors to work with Big Data.
Hadoop provides flexibility as it is designed in such a way that it can deal with any kind of dataset like structured , Semi-Structured , Unstructured efficiently.

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In Your Opinion What Are Some Principles Every Software Engineer Should Follow

The interviewer is likely to ask this question to evaluate your diligence in your job and how you’d meet or exceed the expectations of the company. Consider mentioning the value of adaptability or resourcefulness, as these qualities are useful in many professional settings. You can also discuss your personal coding philosophy and overall thoughts about software engineering.

Example:”One principle that I try to follow as a software engineer is to keep things simple and straightforward. The work itself can be technical and complicated, so I find that a simple and effective system for coding and task execution allows me to stay focused on complex tasks without becoming overwhelmed.”