Posted on : 19 Apr, 2021, 05:36:29 PM
Data Engineering is one of the best and most sought fields, which is the fastest-growing job option globally. Companies always want professional data engineers for their team, so they interview every candidate thoroughly. They look for specific knowledge and skills; candidates have to be prepared accordingly to meet interviewer expectations.
Being prepared for a Data Engineer interview with a question type that the interviewee might ask is an excellent way to acing the interview. No matter how experienced the candidate is because the interviewer’s questions usually target multiple areas such as compatibility of the interviewee, leadership skills, project management skills, knowledge of technical tools, and so on.
Here Wissenhive has collected the top 50 data engineer interview questions to help you in preparing for the data engineer field role.
Data Engineering refers to a term that is used while working on Big Data. Data Engineer focuses on performing data collections and in-depth research on the application by generating raw data from various sources. Through data engineering, raw entity data can be converted into useful and beneficial information.
The roles and responsibility of Data Engineer covers a wide array of things, but some of the important are
|Scope||Data Scientist||Data Analytics|
|Background||It deals with various data operations.||It is related to data cleansing, transforming, and generating inferences from data.|
|Scope||Involves several underlying data procedures||Involves limited small data and static inferences|
|Data Type||Manages structured and unstructured data||Manages structured data only|
|Skills||Processes knowledge of statistics, mathematics, and learning algorithms||Has problems solving skills and knowledge of basic statistics.|
|Tools||Proficient in Python, SAS, TensorFlow, R, Spark, and Hadoop.||Knows SQL, Excel, and R, and Tableau|
Data modeling refers to the simplification method for documenting complex designs of software in the form of diagrams for easy understanding without any prerequisites. It offers numerous benefits, such as simple conceptual and visualized representation of data objects, associated between data objects and their rules.
There are two different types of design schemas performed in Data Modelling, and those are
|Parameters||Structure Data||Unstructured Data|
|Storage Strategies||DBMS||Unmanageable structure of the file|
|Protocol Standards||SQL, ADO.net, and ODBC||CSV, SMSM, XML, and SMTP|
|Integration Tool||ELT||Batch processing or Manual data entry that includes codes|
|Example||Ordered dataset text file||Photos, videos, etc.|
Hadoop refers to a set of open-source software framework utilities that facilities by using many computers’ networks to solve a massive amount of computation and data-related problems. It provides the software framework for Big data processing and distributing storage by using the MapReduce programming model.
There are several components required while working on the Hadoop application, but some of the popular components are
In the Hadoop application, DataNode, and NameNode establish communication together. It refers to a signal which DataNode usually sends to NameNode to show its presence regularly.
Hadoop streaming refers to widely used Hadoop utilities for creating maps, performing various reduction operations, and submitting specific clusters.
There are three different modes used in Hadoop, and those are
There are some steps that help in securing data in Hadoop Application.
NameNode is one of the centerpieces or vital parts of HDFS. It helps in storing HDFS data and tracking multiple files from the clusters. However, the actual data does not store the information in NameNodes but is stored in DataNodes.
There are two different methods that help DataNode and NameNode in communicating via messages, and those are
HDFS Block refers to a single data entity, which is considered the smallest factor. Blocks automatically divide files into a smaller section when Hadoop encounters a large file.
HDFS Block Scanner verifies whether Hadoop created loss-of-blocks is updated on the DataNode successfully or not.
The full form of COSHH is Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. The COSHH provides detailed scheduling at both the application and the cluster levels to have a direct positive impact on the job completion times.
|Parameters||Snowflake Schema||Star Schema|
|Data stored in||Individual tables||Dimension tables|
|Redundancy||Low data||High data|
|Processor||Slower cube processor||Fast cube processor|
|Data presentation||Complex data-handling storage||Simple database designs|
There are four different types of Hadoop XML configuration files available or presented in Hadoop, and those are:
Combiner refers to an optional step between Reduce and Map. It takes all the necessary output from the Map function, building the key-value pairs, and submitting to the Hadoop reducer. Combiners’ main task is to summarize the final result into summary records from Map with an identical key.
The three main methods of Reducer are
Star Join Schema or Star Schema is one of the simplest type schemas for the Data Warehousing concept. This schema’s structure is like a star, consisting of multiple associated dimension tables and fact tables. It is widely used while working with large data sets.
The snowflake schema refers to a star schema’s primary extension with the presence of more dimensions. As the name suggests, the structure of this schema looks like a snowflake. It structures the data and, after normalization, split it into multiple tables.
FSCK or file system check refers to one of the most important commands that HDFS uses. It is mostly used to check for file discrepancies, inconsistencies, and problems.
Data engineers use few languages or fields, and those are
To check the structure of the database in MySQL, Data Engineers can use the DESCRIBE command.
Rack Awareness refers to a process where NameNode takes access from DataNode to enhance the network traffic while writing or reading any document or file in the Hadoop cluster, which is a nearby rack to Write or Read request. NameNode manages the id of the rack of every DataNode to achieve information from the rack.
Hadoop application works with a distributed scalable file system such as
The distributed file system of the Hadoop application is made on Google File System, which is designed to run with a large cluster on the computer system.
Big Data refers to a large amount of structured data and unstructured data, which is difficult to process by using traditional data storage methods. Data engineers prefer using the Hadoop application for managing large amounts of data.
Big Data Analytics helps many organizations in multiple ways, and those fundamental strategies are
|Storage||10(9) to 101(2) in byte||10(9) in byte|
|Management cost||GB is moderate||GB is high|
|Transmit data||Ethernet or IP/TCP||IDE/ SCSI|
FIFO (First In First Out) scheduling is one of the Job scheduling algorithms, which is also known as FCFS (first come, first served). In this scheduling, the reporter chooses the job from the queue of work, the oldest job first.
A context object refers to a means of communication that is used in the Hadoop application with mapper class. It presents system configuration jobs and details in the constructor obtained easily by using context objects. Context objects are used in three methods to send information, and those methods are map(), setup(), and cleanup().
In Big Data, the data size is huge, which makes it difficult to move huge data across the different networks. Here, Hadoop helps in moving computation closer to data, and in that way, the day remains stored to the location.
Hive in the Hadoop ecosystem is used to build the user interface to manage all the Hadoop stored data. HBase's data mapped tables work when needed. Hive queries are very similar to SQL queries that are executed and converted into MapReduce jobs, which can be done to keep to montage complexity when multiple executing jobs at once.
There are three different types of components available in Hive data mode, and those are
Metastore in Hive is used to store locations for the Hive tables and Schema. Data such as mappings, definitions, and other metadata are stored in the Hive Metastore. Later, it started storing data in an RDMS. It uses metadata records to enable the MapR FS and Hadoop FS destinations to write parquet data or drifting Avro.
The important functions available in the Hive to creation table are
The role and responsibility of the .hiverc file is initialization. When individuals want to create or write code for the hive, they can open up the command-line interface. .hiverc is the first to load when CLI opened while containing the parameters that the user initially set.
The **kwargs function is used for donating argument sets that are in line to be input and unordered to function. The *args function allows users to define an ordered function for the usage in the command line.
Hive’s skewed table refers to a special type of table that contains values column more often. The skewed table is a specific table in a hive, usually split into various separate files, and the remaining values go to the other files.
In Hive, SerDe’s full form is Serialization and Deserialization. It refers to an operation that involves passing records through the tables of the Hive. The Deserializer takes a record and changes it into an object of Java that the Hive understands. And then the serializer takes that object of Java and changes it into a processable format by HDFS. Later, HDFs take over the functions of storage.
There are several SerDe implementations available in Hive; you even can create your own custom. Some of the popular SerDe implementations are
|Suited solution||OLTP Solutions||OLAP solutions|
|No. of users||It can handle thousands of users||It can handle a smaller number|
|Use||Recording data||Analysis data|
|Downtime||Always available||Scheduled downtime|
|Optimization||CRUD operations||Complex analysis|
|Data Type||Real-time detailed data||Summarized historical data|
Here, this blog brings us to the end of the top 50 most asked questions by interviewers in a Data Engineering interview. Wissenhive covered almost every question that can help you in gaining and updating your knowledge to the next level and help you in clearing your interview.
Are you stuck with any questions? Feel free ''learners'' to mention it down in the comment section, and we will get back to you as soon as possible.
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