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Questions For Data Engineer Interview

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Data Engineer Interview Questions And Answers:

Data Engineer Interview Questions | Data Engineer Interview Preparation | Intellipaat

Q. What is data engineering?

Data engineering focuses on implementing data analysis and data collection. Data collected from multiple resources is just unprocessed information. Data engineers transform this bare information into usable information. In other words, data engineering transforms, cleanses, profiles and aggregates large data sets for data scientists and analysts to use.

Q. Why have you chosen Data Engineering as a career?

This question aims to understand the drives and beliefs of an individual who is moving forward in the data engineering domain. This is a subjective and personal answer. Make sure you share your motivations, the insights that your learning has given you until this point, what you like about the domain and what your long-term objectives are.

Q. What are the differences between a data warehouse and an operational database?

This is a common question at the intermediate level. An operational database uses Delete SQL statements, Insert and Update as its standard functionalities, focusing on efficiency and speed. Consequently, data analysis is slightly complex. Meanwhile, data warehouses focus primarily on select payments, aggregations and calculations, making them better suited for data analyses.

Q. What is data modelling?

Q. What are the design schemas available in data modelling?

There are two data model design schemas available for data engineers:

  • Snowflake schema

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  • Block report

Q4 Explain Data Engineering

This question is to check if you have understood the role and whether you have a holistic view or a confined understanding. You could start by saying what is known about data engineering in textbooks and then add your own experience or views.

Data engineers setup and maintain the infrastructure that supports the information infrastructure and related applications. Data engineers role has been carved out from a core IT role after the middle layer in information systems within businesses started growing manifold. To maintain a big data architecture, you need people who understand data, data ingestion, extraction, transformation, data loading and more, which is more data specific and far removed from core IT practices and yet not sophisticated enough to handle data mining, identifying patterns in data, recommend data-backed changes to the business leadership, which is what data scientists do. So, data engineers are a crucial link between core IT and data scientists.

Data Engineer Interview Questions With Sample Answers

Below are seven of the most common job interview questions for data engineers. Review the explanation and sample responses as you think of your own answers to prepare for your interview.

  • What is data engineering?

  • What are the essential qualities of a data engineer?

  • Which frameworks and applications are critical for data engineers?

  • Can you explain the design schemas relevant to data modeling?

  • Do you consider yourself database- or pipeline-centric?

  • What is the biggest professional challenge you have overcome as a data engineer?

  • As a data engineer, how would you prepare to develop a new product?

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    Differentiate Between *args And **kwargs

    • *args in function definitions are used to pass a variable number of arguments to a function when calling the function. By using the *, a variable associated with it becomes iterable.

    • **kwargs in function definitions are used to pass a variable number of keyworded arguments to a function while calling the function. The double star allows passing any number of keyworded arguments.

    How Is Data Security Ensured In Hadoop

    Data Engineer Interview Questions And Answers 2020

    Following are some of the steps involved in securing data in Hadoop:

    • You need to begin by securing the authentic channel that connects clients to the server.
    • Second, the clients make use of the stamp that is received to request a service ticket.
    • Lastly, the clients use the service ticket as a tool for authentically connecting to the corresponding server.

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    Data Structures And Algorithms Questions

    Data engineers focus mostly on data modeling and data architecture, but a basic knowledge of algorithms and data structure is also needed. Of particular importance is the data engineers ability to develop inexpensive methods for the transfer of large amounts of data. If youre responsible for a database with potentially millions of records, its important to find the most efficient solution. Common algorithm questions include:

    What Are The Different Kinds Of Joins In Sql

    A JOIN clause combines rows across two or more tables with a related column. The different kinds of joins supported in SQL are:

    • JOIN: returns the records that have matching values in both tables.

    • LEFT JOIN: returns all records from the left table with their corresponding matching records from the right table.

    • RIGHT JOIN: returns all records from the right table and their corresponding matching records from the left table.

    • FULL JOIN: returns all records with a matching record in either the left or right table.

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    What Is The Difference Between Append And Extend In Python

    The argument passed to append is added as a single element to a list in Python. The list length increases by one, and the time complexity for append is O.

    The argument passed to extend is iterated over, and each element of the argument adds to the list. The length of the list increases by the number of elements in the argument passed to extend. The time complexity for extend is O, where n is the number of elements in the argument passed to extend.

    Consider:

    List1 will now be : ]

    The length of list1 is 4.

    Instead of append, use extend

    list1.extend

    List1 will now be :

    The length of list1, in this case, becomes 6.

    Q17 What Is A Block And What Roles Does Block Scanner Play

    Azure Data Engineer Interview Questions and Answers | K21Academy

    Blocks are the smallest unit of data allocated to a file, which the Hadoop system automatically creates for storage in different nodes in a distributed file system. Block Scanner verifies the integrity of a DataNode by checking the data blocks stored on it.A few other questions that are asked in the interviews, that you must be prepared for are listed below.

    Q18) Which tools did you pick up for your projects and why?

    Q19) What is MapReduce in Hadoop, and what role does Reducer play?

    Q20) Talk us through how a Big Data solution is deployed.

    Q21) What is the approach you will take to deal with duplicate data points?

    Q22) What is your experience with Big Data in a cloud environment?

    Q23) How can Data Analytics and Big Data help to positively impact the bottom line of the company?

    Q24) What is the replication factor in HDFS?

    Q25) Explain Block and Block Scanner in HDFS.

    Q26) What sequence of events takes place when Block Scanner detects a problem with a data block?

    Q27) What messages are transacted between NameNode and DataNode?

    Q28) What are the security features in Hadoop?

    Q29) Explain Heartbeat in Hadoop.

    Q30) What is the difference between NAS and DAS?

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    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.

    What Is The Use Of Hive In The Hadoop Ecosystem

    Hive is used to provide the user interface to manage all the stored data in Hadoop. The data is mapped with HBase tables and worked upon, as and when needed. Hive queries are executed to be converted into MapReduce jobs. This is done to keep the complexity under check when executing multiple jobs at once.

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    Three Questions To Help You Prepare For A Data Engineering Interview

    Data science is just one of the modern data-driven fields in our new data world. Another job that is even more prevalent than data scientist is data engineer. Now, being a data engineer does not have all the hype behind it of being a data scientist. However, companies like Google, Facebook, Amazon, and Netflix all need great data engineers!

    Data engineering requires a combination of knowledge, from data warehousing to programming, in order to ensure the data systems are designed well and are as automated as possible.

    The question is: How do you prepare for an interview for a data engineering position?

    Many of the questions will require you to understand data warehouses, scripting, ETL development, and possibly some NO-SQL if the company uses a different form of data storage system like CouchDB.

    In case you are preparing for a data engineering interview, here are some questions that might help you. We are focusing on conceptual questions. However, you should also work on some technical skills like SQL, Python, and etc.

    Which Modes Are You Aware Of In Hadoop

    Data Engineer Interview Questions

    Sample answer:

    I have working knowledge of the three main Hadoop modes:

    • Fully distributed mode
    • Standalone mode
    • Pseudo-distributed mode

    While Id use standalone mode for debugging, the pseudo-distributed mode is used for testing purposes, specifically when resources are not a problem, and the fully-distributed mode is used in production.

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    Data Engineering Etl Questions

    Data engineers and data scientists work hand in hand. Data engineers are responsible for developing ETL processes, analytical tools, and storage tools and software. Thus, expertise with existing ETL and BI solutions is a much-needed requirement.

    ETL refers to how the data is taken from a data source, converted into a format that can be easily analyzed, and stored into a data warehouse. The ETL process then loads the converted data into a database or BI platform in order to be used and viewed by anyone in the organization.

    What Are The Repercussions Of The Namenode Crash

    In an HDFS cluster, there is only one NameNode. This node keeps track of DataNode metadata. Because there is only one NameNode in an HDFS cluster, it is the single point of failure. The system may become inaccessible if NameNode crashes. In a high-availability system, a passive NameNode backs up the primary one and takes over if the primary one fails.

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    What Is Data Engineering

    Interviewers frequently bring this question up to assess whether you can discuss your field in an understandable and competent way. When you answer, try to include a general summary as well as a brief discussion of how data engineers collaborate with colleagues.

    Example:âData engineering powers the collection and processing of information through a combination of desktop software, mobile applications, cloud-based servers and physical infrastructure. Effective data engineering requires careful construction, strong pipelines and smart collaborators. Data engineers are essential partners of data scientists, who analyze and use the information we collect.â

    Can You Explain What Data Locality Means In Hadoop

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    Sample answer:

    Since data contained in an extensive data system is so large, shifting it across the network can cause network congestion.

    This is where data locality can help. It involves moving the computation towards the location of the actual data, which reduces the congestion. In short, it means the data is local.

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    What Do You Understand By Namenode In Hdfs

    NameNode is one of the most important parts of HDFS. It is the master node in the Apache Hadoop HDFS Architecture and is used to maintain and manage the blocks present on the DataNodes .

    NameNode is used to store all the HDFS data, and at the same time, it keeps track of the files in all clusters as well. It is a highly available server that manages the File System Namespace and also controls access to files by clients. Here, we must know that the data is stored in the DataNodes and not in the NameNodes.

    Top 29 Data Engineer Interview Questions And Answers

    List of Most Frequently Asked Data Engineer Interview Questions And Answers to Help You Prepare For The Upcoming Interview:

    Today, data engineering is the most sought after field after software development and it has become one of the fastest-growing job options in the world. Interviewers want the best data engineers for their team and thats why they tend to interview the candidates thoroughly. They look for certain skills and knowledge. So, you have to be prepared accordingly to meet their expectations.

    What You Will Learn:

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    What Is The Difference Between A Where Clause And A Having Clause In Sql

    Answer all of the given differences when this data analyst interview question is asked, and also give out the syntax for each to prove your thorough knowledge to the interviewer.

    WHERE
    WHERE clause operates on row data. The HAVING clause operates on aggregated data.
    In the WHERE clause, the filter occurs before any groupings are made.

    HAVING is used to filter values from a group.

    Aggregate functions cannot be used. Aggregate functions can be used.

    Syntax of WHERE clause:

    ORDER BY column_name

    What Are The 4 Most Key Questions A Data Engineer Is Likely To Hear During An Interview

    Data Engineer Interview Questions

    The four most key questions a data engineer is likely to hear during an interview are

    • What is data modeling?

    • What are the four Vs of Big Data?

    • Do you have any experience working on Hadoop, and how did you enjoy it?

    • Do you have any experience working in a cloud computing environment, what are some challenges that you faced?

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    What Is The Best Way To Capture Streaming Data In Azure

    • Azure has a separate analytics service called Azure Stream Analytics, which supports the Stream Analytics Query Language, a primary SQL-based language.

    • It enables you to extend the query language’s capabilities by introducing new Machine Learning functions.

    • Azure Stream Analytics can analyze a massive volume of structured and unstructured data at around a million events per second and provide relatively low latency outputs.

    What Is Meant By Normalization In Sql

    Normalization is a method used to minimize redundancy, inconsistency, and dependency in a database by organizing the fields and tables. It involves adding, deleting, or modifying fields that can go into a single table. Normalization allows you to break the tables into smaller partitions and link these partitions through different relationships to avoid redundancy.

    Some rules followed in database normalization, which is also known as Normal forms are

    1NF – first normal form

    Syntax for executing a stored procedure

    EXEC procedure_name *params*

    A stored procedure can take parameters at the time of execution so that the stored procedure can execute based on the values passed as parameters.

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    What Is A Cursor

    A cursor is a temporary memory or workstation. It is allocated by the server when DML operations are performed on the table by the user. Cursors store Database tables. SQL provides two types of cursors which are:

    • Implicit Cursors: they are allocated by the SQL server when users perform DML operations.

    • Explicit Cursors: Users create explicit cursors based on requirements. Explicit cursors allow you to fetch table data in a row-by-row method.

    Tell Us About A Time You Had Performance Issues With An Etl And How Did You Fix It

    Data Engineering Interview Tips By Airbnb Data Engineer | Part 2

    As a data engineer, you will run into performance issues. Either you developed an ETL when the data was smaller and it didnt scale, or youre maintaining older architecture that is not scaling. ETLs feature multiple components, multiple table inserts, merges, and updates. This makes it difficult to tell exactly where the ETL issue is occurring. The first step is identifying the problem, so you need to figure out where the bottleneck is occurring.

    Hopefully, whoever set up your ETL has an ETL log table somewhere that tracks when components finish. This makes it easy to spot bottlenecks and the biggest time sucks. If not, it will not be easy to find the issue. Depending on the urgency of the issue, we would recommend setting up an ETL log table and then rerunning to identify the issue. If the fix is needed right away, then you will probably just have to go piece-by-piece through the ETL to try to track down the long-running component. This also depends on how long the ETL takes to run. There are ways you can approach that as well depending on what the component relies on.

    When you look at the activity monitor, you can see if there is any data being processed at all. Is there too much data being processed, none, or table locks? Any of these issues can choke an ETL and would need to be addressed.

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    What Are The Key Differences Between Namenode And Datanode In Hadoop

    Following is the list of key differences between NameNode and DataNode in Hadoop:

    NameNode DataNode
    NameNodes are the centerpiece of HDFS. They are used to control and manage the HDFC. They are known as the Master in the Hadoop cluster. DataNodes are used to store the actual business data in HDFS. They are also known as the Slave in the Hadoop cluster.
    NameNode only stores the metadata of actual data. It acts as the directory tree of all files in the file system and tracks them across the cluster. For example, filename, path, no. of data blocks, block IDs, block location, no. of replicas, slave-related configuration, etc. DataNode acts as the actual worker node where Read/Write/Data processing is handled.
    NameNode is responsible for constructing the file from blocks as it knows the list of the Blocks and their location for any given file in HDFS. DataNode makes a constant communication with NameNode to do the job.
    NameNode plays a critical role in HDFS when the NameNode is down, the HDFS/Hadoop cluster cannot be accessed and is considered down. DataNode is not so important as when it is down. It does not affect the availability of the data or the cluster. NameNode will arrange replication for the blocks managed by the DataNode that is not available.
    NameNode is generally configured with a lot of memory because the block locations are held in the main memory. DataNode is generally configured with a lot of hard disk space because the actual data is stored in the DataNode.

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