100+ Data Engineer Interview Questions and Answers for 2024

Top Data Engineer Interview Questions and Answers- Ace your next big data/data engineer job interview | ProjectPro

100+ Data Engineer Interview Questions and Answers for 2024
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This blog is your one-stop solution for the top 100+ Data Engineer Interview Questions and Answers. In this blog, we have collated the frequently asked data engineer interview questions based on tools and technologies that are highly useful for a data engineer in the Big Data industry. Also, you will find some interesting data engineer interview questions that have been asked in different companies (like Facebook, Amazon, Walmart, etc.) that leverage big data analytics and tools.


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Preparing for data engineer interviews makes even the bravest of us anxious. One good way to stay calm and composed for an interview is to thoroughly answer questions frequently asked in interviews. If you have an interview for a data engineer role coming up, here are some data engineer interview questions and answers based on the skillset required that you can refer to help nail your future data engineer interviews.

 

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Top 100+ Data Engineer Interview Questions and Answers

The following sections consist of the top 100+ data engineer interview questions divided based on big data fundamentals, big data tools/technologies, and big data cloud computing platforms. Furthermore, you will find a few sections on data engineer interview questions commonly asked in various companies leveraging the power of big data and data engineering.

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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.

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Relational Database Management Systems (RDBMS)

Non-relational Database Management Systems 

Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema.

Non-relational databases support dynamic schema for unstructured data. Data can be graph-based, column-oriented, document-oriented, or even stored as a Key store.

RDBMS follow the ACID properties - atomicity, consistency, isolation, and durability.

Non-RDBMS follow the Brewers Cap theorem - consistency, availability, and partition tolerance.

RDBMS are usually vertically scalable. A single server can handle more load by increasing resources such as RAM, CPU, or SSD.

Non-RDBMS are horizontally scalable and can handle more traffic by adding more servers to handle the data.

Relational Databases are a better option if the data requires multi-row transactions to be performed on it since relational databases are table-oriented.

Non-relational databases are ideal if you need flexibility for storing the data since you cannot create documents without having a fixed schema. Since non-RDBMS are horizontally scalable, they can become more powerful and suitable for large or constantly changing datasets.

E.g. PostgreSQL, MySQL, Oracle, Microsoft SQL Server.

E.g. Redis, MongoDB, Cassandra, HBase, Neo4j, CouchDB

 

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Data modeling is a technique that defines and analyzes the data requirements needed to support business processes. It involves creating a visual representation of an entire system of data or a part of it. The process of data modeling begins with stakeholders providing business requirements to the data engineering team.

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Data warehouse

Operational database

Data warehouses generally support high-volume analytical data processing - OLAP.

Operational databases support high-volume transaction processing, typically - OLTP.

You may add new data regularly, but once you add the data, it does not change very frequently.

Data is regularly updated.

Data warehouses are optimized to handle complex queries, which can access multiple rows across many tables.

Operational databases are ideal for queries that return single rows at a time per table.

There is a large amount of data involved.

The amount of data is usually less.

A data warehouse is usually suitable for fast retrieval of data from relatively large volumes of data.

Operational databases are optimized to handle fast inserts and updates on a smaller scale of data.

 

  • Volume: refers to the size of the data sets to be analyzed or processed. The size is generally in terabytes and petabytes.

  • Velocity: the speed at which you generate data. The data generates faster than traditional data handling techniques can handle it.

  • Variety: the data can come from various sources and contain structured, semi-structured, or unstructured data.

  • Veracity: the quality of the data to be analyzed. The data has to be able to contribute in a meaningful way to generate results.

Star schema

Snowflake Schema

Star schema is a simple top-down data warehouse schema that contains the fact tables and the dimension tables.

The snowflake schema is a bottom-up data warehouse schema that contains fact tables, dimension tables, and sub-dimension tables.

Takes up more space.

Takes up less space.

Takes less time for query execution.

Takes more time for query execution than star schema.

Normalization is not useful in a star schema, and there is high data redundancy.

Normalization and denormalization are useful in this data warehouse schema, and there is less data redundancy.

The design and understanding are simpler than the Snowflake schema, and the Star schema has low query complexity.

The design and understanding are a little more complex. Snowflake schema has higher query complexity than Star schema.

There are fewer foreign keys.

There are many foreign keys.

OLTP (Online Transaction Processing) Systems

OLAP (Online Analytical Processing ) Systems

System for modification of online databases.

System for querying online databases.

Supports insert, update and delete transformations on the database.

Supports extraction of data from the database for further analysis.

OLTP systems generally have simpler queries that require less transactional time.

OLAP queries generally have more complex queries which require more transactional time.

Tables in OLTP are normalized.

Tables in OLAP are not normalized.

 

Data engineers and data scientists work very closely together, but there are some differences in their roles and responsibilities.

Data Engineer 

Data scientist 

The primary role is to design and implement highly maintainable database management systems.

The primary role of a data scientist is to take raw data presented on the data and apply analytic tools and modeling techniques to analyze the data and provide insights to the business.

Data engineers transform the big data into a structure that one can analyze.

Data scientists perform the actual analysis of Big Data.

They must ensure that the infrastructure of the databases meets industry requirements and caters to the business.

They must analyze the data and develop problem statements that can process the data to help the business.

Data engineers have to take care of the safety, security and backing up of the data, and they work as gatekeepers of the data.

Data scientists should have good data visualization and communication skills to convey the results of their data analysis to various stakeholders.

Proficiency in the field of big data, and strong database management skills.

Proficiency in machine learning is a requirement.

A data scientist and data engineer role require professionals with a computer science and engineering background, or a closely related field such as mathematics, statistics, or economics. A sound command over software and programming languages is important for a data scientist and a data engineer. Read more for a detailed comparison between data scientists and data engineers.

Data architect

Data engineers

Data architects visualize and conceptualize data frameworks.

Data engineers build and maintain data frameworks.

Data architects provide the organizational blueprint of data.

Data engineers use the organizational data blueprint to collect, maintain and prepare the required data.

Data architects require practical skills with data management tools including data modeling, ETL tools, and data warehousing.

Data engineers must possess skills in software engineering and be able to maintain and build database management systems.

Data architects help the organization understand how changes in data acquisitions will impact the data in use.

Data engineers take the vision of the data architects and use this to build, maintain and process the architecture for further use by other data professionals.

 

  1. Differentiate between structured and unstructured data.

Structured Data

Unstructured Data

Structured data usually fits into a predefined model.

Unstructured data does not fit into a predefined data model.

Structured data usually consists of only text.

Unstructured data can be text, images, sounds, videos, or other formats.

It is easy to query structured data and perform further analysis on it.

It is difficult to query the required unstructured data.

Relational databases and data warehouses contain structured data.

Data lakes and non-relational databases can contain unstructured data. A data warehouse can contain unstructured data too.

 

10. How does Network File System (NFS) differ from Hadoop Distributed File System (HDFS)? 

Network File System

Hadoop Distributed File System

NFS can store and process only small volumes of data.

Hadoop Distributed File System, or HDFS, primarily stores and processes large amounts of data or Big Data.

The data in an NFS exists in a single dedicated hardware.

The data blocks exist in a distributed format on local hardware drives.

NFS is not very fault tolerant. In case of a machine failure, you cannot recover the data.

HDFS is fault tolerant and you may recover the data if one of the nodes fails.

There is no data redundancy as NFS runs on a single machine.

Due to replication across machines on a cluster, there is data redundancy in HDFS.

 

Feature selection is identifying and selecting only the features relevant to the prediction variable or desired output for the model creation. A subset of the features that contribute the most to the desired output must be selected automatically or manually.

Some ways you can handle missing values in Big Data are as follows:

  • Deleting rows with missing values: You simply delete the rows or columns in a table with missing values from the dataset. You can drop the entire column from the analysis if a column has more than half of the rows with null values. You can use a similar method for rows with missing values in more than half of the columns. This method may not work very well in cases where a large number of values are missing.

  • Using Mean/Medians for missing values: In a dataset, the columns with missing values and the column's data type are numeric; you can fill in the missing values by using the median or mode of the remaining values in the column.

  • Imputation method for categorical data: If you can classify the data in a column, you can replace the missing values with the most frequently used category in that particular column. If more than half of the column values are empty, you can use a new categorical variable to place the missing values.

  • Predicting missing values: Regression or classification techniques can predict the values based on the nature of the missing values.

  • Last Observation carried Forward (LCOF) method: The last valid observation can fill in the missing value in data variables that display a longitudinal behavior.

  • Using Algorithms that support missing values: Some algorithms, such as the k-NN algorithm, can ignore a column if values are missing. Another such algorithm is Naive Bayes. The RandomForest algorithm can work with non-linear and categorical data.

In a dataset, an outlier is an observation that lies at an abnormal distance from the other values in a random sample from a particular data set. It is left up to the analyst to determine what can be considered abnormal. Before you classify data points as abnormal, you must first identify and categorize the normal observations. Outliers may occur due to variability in measurement or a particular experimental error. Outliers must be identified and removed before further analysis of the data not to cause any problems.

Logistic regression is a classification rather than a regression model, which involves modeling the probability of a discrete outcome given an input variable. It is a simple and efficient method that can approach binary and linear classification problems. Logistic regression is a statistical method that works well with binary classifications but can be generalized to multiclass classifications.

15. Briefly define the Star Schema.

The star join schema, one of the most basic design schemas in the Data Warehousing concept, is also known as the star schema. It looks like a star, with fact tables and related dimension tables. The star schema is useful when handling huge amounts of data.

16. Briefly define the Snowflake Schema.

The snowflake schema, one of the popular design schemas, is a basic extension of the star schema that includes additional dimensions. The term comes from the way it resembles the structure of a snowflake. In the snowflake schema, the data is organized and, after normalization, divided into additional tables.

  • The k-means method is an unsupervised learning algorithm used as a clustering technique, whereas the K-nearest-neighbor is a supervised learning algorithm for classification and regression problems.

  • KNN algorithm uses feature similarity, whereas the K-means algorithm refers to dividing data points into clusters so that each data point is placed precisely in one cluster and not across many.

A/B testing is a randomized experiment performed on two variants, ‘A’ and ‘B.’ It is a statistics-based process involving applying statistical hypothesis testing, also known as “two-sample hypothesis testing.” In this process, the goal is to evaluate a subject’s response to variant A against its response to variant B to determine which variants are more effective in achieving a particular outcome.

Collaborative filtering is a method used by recommendation engines. In the narrow sense, collaborative filtering is a technique used to automatically predict a user's tastes by collecting various information regarding the interests or preferences of many other users. This technique works on the logic that if person 1 and person 2 have the same opinion on one particular issue, then person 1 is likely to have the same opinion as person 2 on another issue than another random person. In general, collaborative filtering is the process that filters information using techniques involving collaboration among multiple data sources and viewpoints.

Some popular type of bias that occurs while sampling is

  • Undercoverage- The undercoverage bias occurs when there is an inadequate representation of some members of a particular population in the sample.

  • Observer Bias- Observer bias occurs when researchers unintentionally project their expectations on the research. There may be occurrences where the researcher unintentionally influences surveys or interviews.

  • Self-Selection Bias- Self-selection bias, also known as volunteer response bias, happens when the research study participants take control over the decision to participate in the survey. The individuals may be biased and are likely to share some opinions that are different from those who choose not to participate. In such cases, the survey will not represent the entire population.

  • Survivorship Bias- The survivorship bias occurs when a sample is more concentrated on subjects that passed the selection process or criterion and ignore the subjects who did not pass the selection criteria. Survivorship biases can lead to overly optimistic results.

  • Recall Bias- Recall bias occurs when a respondent fails to remember things correctly.

  • Exclusion Bias- The exclusion bias occurs due to the exclusion of certain groups while building the sample.

A distributed cache pools the RAM in multiple computers networked into a single in-memory data store to provide fast access to data. Most traditional caches tend to be in a single physical server or hardware component. Distributed caches, however, grow beyond the memory limits of a single computer as they link multiple computers, providing larger and more efficient processing power. Distributed caches are useful in environments that involve large data loads and volumes. They allow scaling by adding more computers to the cluster and allowing the cache to grow based on requirements.

Apache Hadoop is a collection of open-source libraries for processing large amounts of data. Hadoop supports distributed computing, where you process data across multiple computers in clusters. Previously, if an organization had to process large volumes of data, it had to buy expensive hardware. Hadoop has made it possible to shift the dependency from hardware to achieve high performance, reliability, and fault tolerance through the software itself. Hadoop can be useful when there is Big Data and insights generated from the Big Data. Hadoop also has robust community support and is evolving to process, manage, manipulate and visualize Big Data in new ways.

21. Briefly define COSHH.

COSHH is an acronym for Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. As the name implies, it offers scheduling at both the cluster and application levels to speed up job completion.

22. 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 (HDFS): 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.

23. List some of the essential features of Hadoop.

  • Hadoop is a user-friendly open source framework.

  • Hadoop is highly scalable. Hadoop can handle any sort of dataset effectively, including unstructured (MySQL Data), semi-structured (XML, JSON), and structured (MySQL Data) (Images and Videos). 

  • Parallel computing ensures efficient data processing in Hadoop.

  • Hadoop ensures data availability even if one of your systems crashes by copying data across several DataNodes in a Hadoop cluster.

24. What methods does Reducer use in Hadoop?

The three primary methods to use with reducer in Hadoop are as follows:

  • setup(): This function is mostly useful to set input data variables and cache protocols.

  • cleanup(): This procedure is useful for deleting temporary files saved.

  • reduce(): This method is used only once for each key and is the most crucial component of the entire reducer.

25. What are the various design schemas in data modeling?

There are two fundamental design schemas in data modeling: star schema and snowflake schema.

  • Star Schema- The star schema is the most basic type of data warehouse schema. Its structure is similar to that of a star, where the star's center may contain a single fact table and several associated dimension tables. The star schema is efficient for data modeling tasks such as analyzing large data sets.

  • Snowflake Schema- The snowflake schema is an extension of the star schema. In terms of structure, it adds more dimensions and has a snowflake-like appearance. Data is split into additional tables, and the dimension tables are normalized.

26. What are the components that the Hive data model has to offer?

Some major components in a Hive data model are

  • Buckets

  • Tables

  • Partitions.

You can go through many more detailed Hadoop Interview Questions here.

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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.

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  • *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.

Python's “is” operator checks whether two variables point to the same object. “==” is used to check whether the values of two variables are the same.

E.g. consider the following code:

a = [1,2,3]

b = [1,2,3]

c = b

a == b 

evaluates to true since the values contained in the list a and list b are the same but 

a is b 

evaluates to false since a and b refers to two different objects.

c is b

Evaluates to true since c and b point to the same object.

Memory in Python exists in the following way:

  • The objects and data structures initialized in a Python program are present in a private heap, and programmers do not have permission to access the private heap space.

  • You can allocate heap space for Python objects using the Python memory manager. The core API of the memory manager gives the programmer access to some of the tools for coding purposes.

  • Python has a built-in garbage collector that recycles unused memory and frees up memory for heap space.

A decorator is a tool in Python which allows programmers to wrap another function around a function or a class to extend the behavior of the wrapped function without making any permanent modifications to it. Functions in Python are first-class objects, meaning functions can be passed or used as arguments. A function works as the argument for another function in a decorator, which you can call inside the wrapper function.

The time complexity to look up a value in a list in Python is O(n) since the whole list iterates through to find the value. Since a dictionary is a hash table, the time complexity to find the value associated with a key is O(1). Hence, a lookup is generally faster with a dictionary, but a limitation is that dictionaries require unique keys to store the values.

The bin() function works on a variable to return its binary equivalent.

A list can be converted into a set and then back into a list to remove the duplicates. Sets do not contain duplicate data in Python.

E.g.

list1 = [5,9,4,8,5,3,7,3,9]

list2 = list(set(list1))

list2 will contain [5,9,4,8,3,7]

Set() may not maintain the order of items within the list.

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(1).

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(n), where n is the number of elements in the argument passed to extend.

Consider:

list1 = [“Python”, “data”, “engineering”]

list2 = [“projectpro”, “interview”, “questions”]

list1.append(list2)

List1 will now be : [“projectpro”, “interview”, “questions”, [“Python”, “data”, “engineering”]]

The length of list1 is 4.

Instead of append, use extend

list1.extend(list2)

List1 will now be : [“projectpro”, “interview”, “questions”, “Python”, “data”, “engineering”]

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

The break statement in Python terminates a loop or another statement containing the break statement. If a break statement is present in a nested loop, it will terminate only the loop in which it is present. Control will pass the statements after the break statement if they are present.

The continue statement forces control to stop the current iteration of the loop and execute the next iteration rather than terminating the loop completely. If a continue statement is present within a loop, it leads to skipping the code following it for that iteration, and the next iteration gets executed.

Pass statement in Python does nothing when it executes, and it is useful when a statement is syntactically required but has no command or code execution. The pass statement can write empty loops and empty control statements, functions, and classes.

 str.isalnum() can be used to check whether a string ‘str’ contains only letters and numbers.

  • NumPy arrays take up less space in memory than lists.

  • NumPy arrays are faster than lists.

  • NumPy arrays have built-in functions optimized for various techniques such as linear algebra, vector, and matrix operations.

  • Lists in Python do not allow element-wise operations, but NumPy arrays can perform element-wise operations.

import pandas as pd

days = [‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’]

# Calling DataFrame constructor on list

df = pd.DataFrame(days)

df is the data frame created from the list ‘days’.

df = pd.DataFrame(days, index =[‘1’,’2’,’3’,’4’], columns=[‘Days’])

Can be used to create the data frame and the values for the index and columns.

The median() function can be used to find the median value in a column. E.g.- employees[“age”].median()

The rename() function can be used to rename columns of a data frame.

To rename address_line_1 to ‘region’ and address_line_2 to ‘city’

employees.rename(columns=dict(address_line_1=’region’, address_line_2=’city’))

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.

SciPy is an open-source Python library that is useful for scientific computations. SciPy is short for Scientific Python and is used to solve complex mathematical and scientific problems. SciPy is built on top of NumPy and provides effective, user-friendly functions for numerical optimization. The SciPy library comes equipped with functions to support integration, ordinary differential equation solvers, special functions, and support for several other technical computing functions.

The function numpy.linalg.inv() can help you inverse a matrix. It takes a matrix as the input and returns its inverse. You can calculate the inverse of a matrix M as:

if det(M) != 0

    M-1 = adjoint(M)/determinant(M)

else

    "Inverse does not exist

In NumPy, an array is a table of elements, and the elements are all of the same types and you can index them by a tuple of positive integers. To create an array in NumPy, you must create an n-dimensional array object. An ndarray is the n-dimensional array object defined in NumPy to store a collection of elements of the same data type.

from numpy import random

x = random.randint(500, size=(4, 7))

import NumPy as np

array = np.array([5,9,6,3,2,1,9])

To find the indices of values greater than 5

print(np.where(array>5))

Gives the output (array([0,1,2,6])

Data Engineer Interview Questions on Excel

Microsoft Excel is one of the most popular data engineering tools in the big data industry. In contrast to BI tools, which ingest processed data supplied by the data engineering pipeline, Excel gives data engineers flexibility and control over data entry. Here are some data engineer interview questions on Microsoft Excel and its features.

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Freeze panes are used in MS Excel to lock a particular row or column. The rows or columns you lock will be visible on the screen even when scrolling the sheet horizontally or vertically.

To freeze panes on Excel:

  1. First, select the cell to the right of the columns and below the rows to be kept visible.

  2. Select View > Freeze Panes > Freeze Panes.

In Excel, the ribbon exists in the topmost area of the window. They contain the toolbars and menu items available in Excel. Ribbons contain multiple tabs, each with its own command set. You can switch the ribbon between shown and hidden using CTRL+F1. 

In Excel, you can protect a worksheet, meaning that you can paste no copied data from the cells in the protected worksheet. To be able to copy and paste data from a protected worksheet, you must remove the sheet protection and unlock all cells, and once more lock only those cells that are not to be changed or removed. To protect a worksheet, go to Menu -> Review -> Protect Sheet -> Password. Using a unique password, you can protect the sheet from getting copied by others.

The SUM function may be useful for finding the sum of columns in an Excel spreadsheet.

=SUM(A5:F5) can be useful to find the sum of values in the columns A-F of the 5th row.

Macros in Excel refers to an action or a set of actions that can be saved and recorded to run as often as required. Macros may be given names and can be used to save time to perform any frequently run tasks. Excel stores macros as VBA code, and you can view the code using a VBA editor. You can assign macros to objects, including shapes, graphics, or control.

Excel follows the same order of operations as in standard mathematics, which is indicated by “PEMDAS” where:

P - Parentheses

E - Exponent

M - Multiplication

D - Division

A - Addition

S - Subtraction

A pivot table is a tool consisting of a table of grouped values where individual items of a larger, more extensive table aggregate within one or more discrete categories. It is useful for quick summarization of large unstructured data. It can automatically perform sort, total, count, or average of the data in the spreadsheet and display the results in another spreadsheet. Pivot tables save time and allow linking external data sources to Excel.

The SUBSTITUTE function in Excel is useful to find a match for a particular text and replace it. The REPLACE function replaces the text, which you can identify using its position.

SUBSTITUTE syntax

=SUBSTITUTE (text, text_to_be_replaced, text_to_replace_old_text_with, [instance_number])

Where 

text refers to the text in which you can perform the replacements

instance_number refers to the number of times you need to replace a match.

E.g. consider a cell A5 which contains “Bond007”

=SUBSTITUTE(A5, “0”, “1”, 1) gives the result “Bond107”

=SUBSTITUTE(A5, “0”, “1”, 2) gives the result “Bond117”

=SUBSTITUTE(A5, “0”, “1”) gives the result “Bond117”

REPLACE syntax

=REPLACE (old_text, start_num, num_chars, text_to_be_replaced)

Where start_num - starting position of old_text to be replaced

num_chars - number of characters to be replaced

E.g. consider a cell A5 which contains “Bond007”

=REPLACE(A5, 5, 1, “99”) gives the result “Bond9907”

The IF function in Excel performs the logic test and is used to check whether a given condition is true or false, then perform further operations based on the result.

The syntax is:

=IF (test condition, value if true, value if false)

You can use the Advanced Criteria Filter to analyze a list or in cases where you need to test more than two conditions.

A red triangle at the top right-hand corner of a cell indicates a comment associated with that particular cell. You can view the comment by hovering the cursor over it.

Data Engineer Interview Questions on SQL

You will spend most of your career using SQL if you are a Data Engineer working in an organization. Building a strong foundation in SQL is crucial since you may easily save time and effort if you can leverage its various features effectively. Also, acquire a solid knowledge of databases such as the NoSQL or Oracle database. Questions addressing data modeling and database architecture test your understanding of entity-relationship modeling, normalization and denormalization, dimensional modeling, and relevant ideas. Below are a few data engineer interview questions on SQL concepts, queries on data storage, data retrieval, and a lot more.

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In SQL, aggregate functions are functions where the values from multiple rows are grouped to form a single value with its significant meaning. Aggregate functions in SQL include count(), min(), max(), sum(), avg().

To find duplicates in a single column:

SELECT column_name, COUNT(column_name)

FROM table_name

GROUP BY column_name

HAVING COUNT(column_name)>1

Will display all the records in a column which have the same value.

To find duplicates in multiple columns of a table:

SELECT column1_name, column2_name, COUNT(*)

FROM table_name

GROUP BY column1_name, column2_name

HAVING COUNT(*)>1

Will display all the records with the same values in column1 and column2.

In SQL, a primary key refers to a field in a table that can uniquely identify rows in that table. Primary keys must have unique values, and a primary key value cannot be NULL. A table can have only one primary key and can be a single field or multiple fields. When you use multiple fields as the primary key, they are collectively known as the composite key.

The UNIQUE constraint is used for columns in SQL to ensure that all the values in a particular column are different. The UNIQUE constraint and the PRIMARY KEY both ensure that a column contains a value with unique values. However, there can be only one PRIMARY KEY per table, but you can specify the UNIQUE constraint for multiple columns. After creating the table, you can add or drop the UNIQUE constraints from columns.

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

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

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

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

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

In SQL, an index is a special lookup table used by the database search engine to perform data retrieval from any data structure more speedily.  Indexes speed up SELECT queries and WHERE clauses, but slow down UPDATE and INSERT statements, which require input data. Indexes can be created or dropped and will not affect the data. Indexing is a method for optimizing database efficiency by reducing the number of disc accesses required during query execution. This data structure technique may quickly search for and access a database.

Clustered indexes in SQL modify how you store records in the database based on the indexed column. They are useful for the speedy retrieval of data from the database. Non-clustered indexes create a different entity within the table that references the original table. They are relatively slower than clustered indexes, and SQL allows only a single clustered index but multiple non-clustered indexes.

The BETWEEN operator in SQL tests if a particular expression lies between a range of values. The values can be in the form of text, dates, or numbers. You can use the BETWEEN operator with SELECT, INSERT, UPDATE, and DELETE statements. In a query, the BETWEEN condition helps to return all values that lie within the range. The range is inclusive. The syntax is of BETWEEN is as follows:

SELECT column_name(s)

FROM table_name

WHERE column_name BETWEEN value1 AND value2;

The IN operator tests whether an expression matches the values specified in a list of values. It helps to eliminate the need of using multiple OR conditions. NOT IN operator may exclude certain rows from the query return. IN operator may also be used with SELECT, INSERT, UPDATE, and DELETE statements. The syntax is:

SELECT column_name(s)

FROM table_name

WHERE column_name IN (list_of_values);

A foreign key is a field or a collection of fields in one table that can refer to the primary key in another table. The table which contains the foreign key is the child table, and the table containing the primary key is the parent table or the referenced table. The purpose of the foreign key constraint is to prevent actions that would destroy links between tables.

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.

An alias enables you to give a table or a particular column in a table a temporary name to make the table or column name more readable for that specific query. Aliases only exist for the duration of the query.

The syntax for creating a column alias

SELECT column_name AS alias_name

FROM table_name;

The syntax for creating a table alias

SELECT column_name(s)

FROM table_name AS alias_name;

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

2NF - second normal form

3NF - third normal form

BCF - Boyce-Codd normal form

Stored procedures are used in SQL to run a particular task several times. You can save or reuse stored procedures when required.

 The syntax for creating a stored procedure:

CREATE PROCEDURE procedure_name *params*

AS

sql_statement

GO;

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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SELECT *

FROM places

WHERE name LIKE '%ind%'

The TRUNCATE command helps delete all the rows from a table but keeps its structure intact. The column, indexes, and constraints remain intact when using the TRUNCATE statement.

select min(sales) from   

(select distinct sales from Apparels by sales desc)   

where rownum < 3; 

NULL in SQL is not the same as zero or a blank space. NULL is used in the absence of any value and is said to be unavailable, unknown, unassigned, or inappropriate. Zero is a number, and a blank space gets treated as a character. You can compare a blank space or zero to another black space or zero, but cannot compare one NULL with another NULL.

  1. What is the default ordering of the ORDER BY clause and how can this be changed?

The ORDER BY clause is useful for sorting the query result in ascending or descending order. By default, the query sorts in ascending order. The following statement can change the order:

SELECT expressions FROM table_name   

WHERE conditions    

ORDER BY expression DESC; 

SELECT employee_id, AVG (sales)  

FROM Employees 

WHERE AVG(sales) > 70000  

GROUP BY month; 

No, the above query will not return an output since you cannot use the WHERE clause to restrict the groups. To generate output in this query, you should use the HAVING clause.

SQL injection is a type of vulnerability in SQL codes that allows attackers to control back-end database operations and access, retrieve and/or destroy sensitive data present in databases. SQL injection involves inserting malicious SQL code into a database entry field. When the code gets executed, the database becomes vulnerable to attack, and SQL injection is also known as SQLi attack.

Whenever a database is modified, the system executes a trigger command.

SELECT * FROM student WHERE stud_name like '%T';  

DELETE command

TRUNCATE command

The DELETE command helps to delete one specific row or more than one row corresponding to a certain condition.

The TRUNCATE command helps to delete all rows of a table.

It is a Data Manipulation Language (DML) command.

It is a Data Definition Language (DDL) command.

In the case of the DELETE statement, rows are removed one at a time. The DELETE statement records an entry for each deleted row in the transaction log.

Truncating a table removes the data associated with a table by deallocating the data pages that store the table data. Only the page deallocations get stored in the transaction log.

The DELETE command is slower than the TRUNCATE command.

The TRUNCATE command is faster than the DELETE command.

You can only use the DELETE statement with DELETE permission for the table.

Using the TRUNCATE command requires ALTER permission for the table.

 

In SQL, a trigger refers to a set of statements in a system catalog that runs whenever DML (Data Manipulation Language) commands run on a system. It is a special stored procedure that gets called automatically in response to an event. Triggers allow the execution of a batch of code whenever an insert, update or delete command is executed for a specific table. You can create a trigger by using the CREATE TRIGGER statement. The syntax is:

CREATE TRIGGER trigger_name

(AFTER|BEFORE) (INSERT|UPDATE|DELETE)

ON table_name FOR EACH ROW

BEGIN

Variable declarations

Trigger code

END;

Data Engineer Interview Questions on Azure

Most businesses are switching to cloud infrastructure these days. Organizations employ a variety of providers including AWS, Google Cloud, and Azure for their BI and Machine Learning applications. Microsoft Azure allows data engineers to build and deploy applications using various solutions. Check out these common data engineer interview questions on various Microsft Azure concepts, tools, and frameworks.

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76. Explain the features of Azure Storage Explorer.

  • It's a robust stand-alone application that lets you manage Azure Storage from any platform, including Windows, Mac OS, and Linux.

  • An easy-to-use interface gives you access to many Azure data stores, including ADLS Gen2, Cosmos DB, Blobs, Queues, Tables, etc.

  • One of the most significant aspects of Azure Storage Explorer is that it enables users to work despite being disconnected from the Azure cloud service using local emulators.

77. What are the various types of storage available in Azure?

In Microsoft Azure, there are five storage types classified into two categories.

  • The first group comprises Queue Storage, Table Storage, and Blob Storage. It is built with data storage, scalability, and connectivity and is accessible through a REST API.

  • The second group comprises File Storage and Disk Storage, which boosts the functionalities of the Microsoft Azure Virtual Machine environment and is only accessible through Virtual Machines.

  • Queue Storage enables you to create versatile applications that comprise independent components depending on asynchronous message queuing. Azure Queue storage stores massive volumes of messages accessible by authenticated HTTP or HTTPS queries anywhere.

  • Table Storage in Microsoft Azure holds structured NoSQL data. The storage is highly extensible while also being efficient in storing data. However, if you access temporary files frequently, it becomes more expensive. This storage can be helpful to those who find Microsoft Azure SQL too costly and don't require the SQL structure and architecture.

  • Blob Storage supports unstructured data/huge data files such as text documents, images, audio, video files, etc. In Microsoft Azure, you can store blobs in three ways: Block Blobs, Append Blobs, and Page Blobs.

  • File Storage serves the needs of the Azure VM environment. You can use it to store huge data files accessible from multiple Virtual Machines. File Storage allows users to share any data file via the SMB (Server Message Block) protocol.

  • Disk Storage serves as a storage option for Azure virtual machines. It enables you to construct virtual machine disks. Only one virtual machine can access a disk in Disk Storage.

78. What data security solutions does Azure SQL DB provide?

In Azure SQL DB, there are several data security options:

  • Azure SQL Firewall Rules: There are two levels of security available in Azure.

  • The first are server-level firewall rules, which are present in the SQL Master database and specify which Azure database servers are accessible.

  • The second type of firewall rule is database-level firewall rules, which monitor database access.

  • Azure SQL Database Auditing: The SQL Database service in Azure offers auditing features. It allows you to define the audit policy at the database server or database level.

  • Azure SQL Transparent Data Encryption: TDE encrypts and decrypts databases and performs backups and transactions on log files in real-time.

  • Azure SQL Always Encrypted: This feature safeguards sensitive data in the Azure SQL database, such as credit card details.

79. What do you understand by PolyBase?

Polybase is a system that uses the Transact-SQL language to access external data stored in Azure Blob storage, Hadoop, or the Azure Data Lake repository. This is the most efficient way to load data into an Azure Synapse SQL Pool. Polybase facilitates bidirectional data movement between Synapse SQL Pool and external resources, resulting in faster load performance.

  • PolyBase allows you to access data in Hadoop, Azure Blob Storage, or Azure Data Lake Store from Azure SQL Database or Azure Synapse Analytics.

  • PolyBase uses relatively easy T-SQL queries to import data from Hadoop, Azure Blob Storage, or Azure Data Lake Store without any third-party ETL tool.

  • PolyBase allows you to export and retain data to external data repositories.

80. 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.

81.  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 (such as SessionWindow()).

82. Discuss the different consistency models in Cosmos DB.

There are five distinct consistency models/levels in Azure Cosmos DB, starting from strongest to weakest-

  • Strong- It ensures linearizability, i.e., serving multiple requests simultaneously. The reads will always return the item's most recent committed version. Uncommitted or incomplete writes are never visible to the client, and users will always be able to read the most recent commit.

  • Bounded staleness- It guarantees the reads to follow the consistent prefix guarantee. Reads may lag writes by "K" versions (that is, "updates") of an item or "T" time interval, whichever comes first.

  • Session- It guarantees reads to honor the consistent prefix, monotonic reads and writes, read-your-writes, and write-follows-reads guarantees in a single client session. This implies that only one "writer" session or several authors share the same session token.

  • Consistent prefix- It returns updates with a consistent prefix throughout all updates and has no gaps. Reads will never detect out-of-order writes if the prefix consistency level is constant.

  • Eventual- There is no guarantee for ordering of reads in eventual consistency.  The replicas gradually converge in the lack of further writes. 

83. What are the various types of Queues that Azure offers?

Storage queues and Service Bus queues are the two queue techniques that Azure offers.

  • Storage queues- Azure Storage system includes storage queues. You can save a vast quantity of messages on them. Authorized HTTP or HTTPS calls allow you to access messages from anywhere. A queue can hold millions of messages up to the storage account's overall capacity limit. Queues can build a backlog of work for asynchronous processing.

  • Service Bus queues are present in the Azure messaging infrastructure, including queuing, publish/subscribe, and more advanced integration patterns. They mainly connect applications or parts of applications that encompass different communication protocols, data contracts, trust domains, or network settings.

84. What are the different data redundancy options in Azure Storage?

When it comes to data replication in the primary region, Azure Storage provides two choices:

  • Locally redundant storage (LRS) replicates your data three times synchronously in a single physical location in the primary area. Although LRS is the cheapest replication method, it is unsuitable for high availability or durability applications.

  • Zone-redundant storage (ZRS) synchronizes data across three Azure availability zones in the primary region. Microsoft advises adopting ZRS in the primary region and replicating it in a secondary region for high-availability applications.

Azure Storage provides two options for moving your data to a secondary area:

  • Geo-redundant storage (GRS) synchronizes three copies of your data within a single physical location using LRS in the primary area. It moves your data to a single physical place in the secondary region asynchronously.

  • Geo-zone-redundant storage (GZRS) uses ZRS to synchronize data across three Azure availability zones in the primary region. It then asynchronously moves your data to a single physical place in the secondary region. 

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Data Engineer Interview Questions on AWS

Data engineers may leverage cloud-based services like AWS to help enterprises overcome some of the issues they face as they deal with large data volumes. Practice these data engineering interview questions below to impress your hiring manager with your data engineering skills in cloud computing.

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85. What logging capabilities does AWS Security offer?

  • AWS CloudTrail allows security analysis, resource change tracking, and compliance auditing of an AWS environment by providing a history of AWS API calls for an account. CloudTrail sends log files to a chosen Amazon Simple Storage Service (Amazon S3) bucket, with optional log file integrity validation.

  • Amazon S3 Access Logs record individual requests to Amazon S3 buckets and can be capable of monitoring traffic patterns, troubleshooting, and security and access audits. It can also assist a business in gaining a better understanding of its client base, establishing lifecycle policies, defining access policies, and determining Amazon S3 prices.

  • Amazon VPC Flow Logs record IP traffic between Amazon Virtual Private Cloud (Amazon VPC) network interfaces at the VPC, subnet, or individual Elastic Network Interface level. You can store Flow log data in Amazon CloudWatch Logs and export it to Amazon CloudWatch Streams for enhanced network traffic analytics and visualization.

86. How can Amazon Route 53 ensure high availability while maintaining low latency?

AWS's highly available and stable infrastructure builds Route 53. The DNS servers' widely distributed design helps maintain a constant ability to direct end-users to your application by avoiding internet or network-related issues. Route 53 delivers the level of dependability that specific systems demand. Route 53 uses a worldwide anycast network of DNS servers to automatically respond to inquiries from the best location available based on network circumstances. As a result, your end consumers will experience low query latency.

87. What is Amazon Elastic Transcoder, and how does it work?

  • Amazon Elastic Transcoder is a cloud-based media transcoding service.

  • It's intended to be a highly flexible, simple-to-use, and cost-effective solution for developers and organizations to transform (or "transcode") media files from their original format into versions suitable for smartphones, tablets, and computers.

  • Amazon Elastic Transcoder also includes transcoding presets for standard output formats, so you don't have to assume which parameters will work best on specific devices.

88. Discuss the different types of EC2 instances available.

  • On-Demand Instances- You pay for computing capacity by the hour or second with On-Demand instances, depending on the instances you run. There are no long-term obligations or upfront payments required. You can scale up or down your compute capacity based on your application's needs, and you only pay the per-hour prices for the instance you utilize.

  • Reserved Instances- When deployed in a specific Availability Zone, Amazon EC2 Reserved Instances (RI) offer a significant reduction (up to 72%) over On-Demand pricing and a capacity reservation.

  • Spot Instances- You can request additional Amazon EC2 computing resources for up to 90% off the On-Demand price using Amazon EC2 Spot instances.

89. Mention the AWS consistency models for modern DBs.

A database consistency model specifies how and when a successful write or change reflects in a future read of the same data.

  • The eventual consistency model is ideal for systems where data update doesn’t occur in real-time. It's Amazon DynamoDB's default consistency model, boosting read throughput. However, the outcomes of a recently completed write may not necessarily reflect in an eventually consistent read.

  • In Amazon DynamoDB, a strongly consistent read yields a result that includes all writes that have a successful response before the read. You can provide additional variables in a request to get a strongly consistent read result. Processing a highly consistent read takes more resources than an eventually consistent read.

90. What do you understand about Amazon Virtual Private Cloud (VPC)?

  • The Amazon Virtual Private Cloud (Amazon VPC) 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.

91. Outline some security products and features available in a virtual private cloud (VPC).

  • Flow Logs- Analyze your VPC flow logs in Amazon S3 or Amazon CloudWatch to obtain operational visibility into your network dependencies and traffic patterns, discover abnormalities, prevent data leakage, etc.

  • Network Access Analyzer- The Network Access Analyzer tool assists you in ensuring that your AWS network meets your network security and compliance standards. Network Access Analyzer allows you to establish your network security and compliance standards.

  • Traffic Mirroring- You can directly access the network packets running through your VPC via Traffic Mirroring. This functionality enables you to route network traffic from Amazon EC2 instances' elastic network interface to security and monitoring equipment for packet inspection.

92. What do you mean by RTO and RPO in AWS?

  • Recovery time objective (RTO): The highest allowed time between a service outage and restoration. This specifies the maximum amount of service downtime that you may tolerate.

  • Recovery point objective (RPO): The maximum allowed time since the previous data recovery point. This establishes the level of data loss that is acceptable.

93. What are the benefits of using AWS Identity and Access Management (IAM)?

  • AWS Identity and Access Management (IAM) supports fine-grained access management throughout the AWS infrastructure.

  • IAM  Access Analyzer allows you to control who has access to which services and resources and under what circumstances. IAM policies let you control rights for your employees and systems, ensuring they have the least amount of access.

  • It also provides Federated Access, enabling you to grant resource access to systems and users without establishing IAM Roles.

94. What are the various types of load balancers available in AWS?

  1. An Application Load Balancer routes requests to one or more ports on each container instance in your cluster, making routing decisions at the application layer (HTTP/HTTPS). It also enables path-based routing and may route requests to one or more ports on each container instance in your cluster. Dynamic host port mapping is available with Application Load Balancers.

  2. The transport layer (TCP/SSL) is where a Network Load Balancer decides the routing path. It processes millions of requests per second, and dynamic host port mapping is available with Network Load Balancers.

  3. Gateway Load Balancer distributes traffic while scaling your virtual appliances to match demands by combining a transparent network gateway.

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.

95. What do you understand by Azure Data Lake Analytics?

  • Azure Data Lake Analytics is a real-time analytics job application that makes big data easier to understand.

  • You create queries to change your data and get essential insights instead of deploying, configuring, and optimizing hardware.

  • The analytics service can instantaneously manage jobs of any complexity by pitching in the amount of power you require.

  • Also, it's cost-effective because you only pay for your task when it's operating.

96. Compare Azure Data Lake Gen1 vs. Azure Data Lake Gen2.

Azure Data Lake Gen1

Azure Data Lake Gen2

  • Azure Data Lake Gen 1 is file system storage In a hierarchical file system that distributes data in blocks.

  • Azure Data Lake Gen 2 includes a file system for efficiency and reliability and flexible object storage.

  • The hot/cold storage tier isn't available.

  • The hot/cold storage tier is available.

  • It doesn’t approve storage redundancy.

  • It supports Storage Redundancy.

97. What do you mean by U-SQL?

  • Azure Data Lake Analytics uses U-SQL as a big data query language and execution infrastructure.

  • U-SQL scales out custom code (.NET/C#/Python) from a Gigabyte to a Petabyte scale using typical SQL techniques and language.

  • Big data processing techniques like "schema on reads," custom processors, and reducers are available in U-SQL.

  • The language allows you to query and integrate structured and unstructured data from various data sources, including Azure Data Lake Storage, Azure Blob Storage, Azure SQL DB, Azure SQL Data Warehouse, and SQL Server instances on Azure VMs.

98. Outline some of the features of Azure Data Lake Analytics.

  • Azure Data Lake offers high throughput for raw or other data types for analytics, real-time reporting, and monitoring.

  • It's highly flexible and auto-scalable, with payment handling flexibility.

  • U-SQL can process any structured and unstructured data using SQL syntax and Azure custom functions to set up new ADFS driver functions.

  • It offers a highly accessible on-premise data warehouse service for exploring data for analytics, reporting, monitoring, and Business Intelligence using various tools.

99. What are the different blob storage access tiers in Azure?

  • Hot tier - An online tier that stores regularly viewed or updated data. The Hot tier has the most expensive storage but the cheapest access.

  • Cool tier - An online layer designed for rarely storing data that is accessed or modified. The Cool tier offers reduced storage costs but higher access charges than the Hot tier.

  • Archive tier - An offline tier designed for storing data accessed rarely and with variable latency requirements. You should keep the Archive tier's data for at least 180 days.

Data Engineer Technical Interview Questions | Data Engineering Technical Interview Questions

Here are some data engineering interview questions for you that will help hiring managers to test your technical skills and knowledge.

100. What do you mean by Blocks and Block Scanner?

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.

101. How does a block scanner deal with a corrupted data block?

The DataNode notifies the NameNode about a particular file when the block scanner detects a corrupted data block. After that, NameNode processes the data file by replicating it using the original, corrupted file. The corrupted data block is not deleted if there is a match between the replicas made and the replication block.

102. List some of the XML configuration files present in Hadoop.

Some of the XML configuration files present in Hadoop are

  • HDFS-site (one of the most important XML configuration files)

  • Core-site

  • YARN-site

  • Mapred-site

103. How would you check the validity of data migration between databases?

A data engineer's primary concerns should be maintaining the accuracy of the data and preventing data loss. The purpose of this question is to help the hiring managers understand how you would validate data.

You must be able to explain the suitable validation types in various instances. For instance, you might suggest that validation can be done through a basic comparison or after the complete data migration.

104. How does a SQL query handle duplicate data points?

In SQL, there are mainly two ways to handle or reduce duplicate data points- you can use the SQL keywords DISTINCT & UNIQUE to reduce duplicate data points. Additionally, you have other options, like using GROUP BY to handle duplicate data points.

Databricks Data Engineer Interview Questions

Below are the Data Engineer interview questions asked in Databricks-

100. What is Databricks Runtime?

101. What Spark components are included in Azure Databricks?

102. What are the types of runtimes Azure Databricks offers?

103. What is Databricks File System?

104. How do you access Azure Data Lake Storage from a Notebook?

Walmart Data Engineer Interview Questions

Some of the Data Engineer interview questions asked in Walmart are

105. What is a case class in Scala?

106. Elaborate on the Hive architecture.

106. What are the various types of data models?

107. Can we use Hadoop commands to load data in the backend to a particular partition table?

108. How can we truncate a table in Hive?

109. What is Spark? How is it different from Hive?

EY Data Engineer Interview Questions

Here are the most commonly asked Data Engineer interview questions at EY -

110. When should you not use a pie chart?

111. What is database dynamic?

112. Explain the spark architecture.

113. Explain joins in SQL.

114. Difference between map and flat map.

115. Difference between RDD and dataframe.

116. Given this role sits within EY Data Analytics Team, please let us know about your recent experience and exposure to Data and Analytics. What data-related projects, tools, platforms, and technologies have you worked on?

Behavioral Data Engineering Questions

These are some of the behavioral Data Engineer interview questions asked in almost every data engineering interview.

117. Why are you opting for a career in data engineering, and why should we hire you?

118. What are the daily responsibilities of a data engineer?

119. What problems did you face while trying to aggregate data from multiple sources? How did you go about resolving this?

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

121. Do you have any experience working in a cloud computing environment? What are some challenges that you faced?

122. What are the fundamental characteristics that make a good data engineer?

123. How would you approach a new project as a data engineer?

124. Do you have any experience working with data modeling techniques?

Facebook Data Engineer Interview Questions

As per Glassdoor, here are some Data Engineer interview questions asked in Facebook:

124. Given a list containing a None value, replace the None value with the previous value in the list.

125. Print the key in a dictionary corresponding to the nth highest value in the dictionary. Print just the first one if there is more than one record associated with the nth highest value.

126. Given two sentences, print the words that are present in only one of the two sentences.

127. Create a histogram using values from a given list.

128. Write a program to flatten the given list : [1,2,3,[4,5,[6,7[8,9]]]]

129. Write a program to remove duplicates from any given list.

130. Write a program to count the number of words in a given sentence.

131. Find the number of occurrences of a letter in a string.

Amazon Data Engineer Interview Questions

Data Engineer interview questions that are most commonly asked at Amazon 

132. How can you tune a query? If a query takes longer than it initially did, what may be the reason, and how will you find the cause?

133. In Python, how can you find non-duplicate numbers in the first list and create a new list preserving the order of the non-duplicates?

134. Consider a large table containing three columns corresponding to DateTime, Employee, and customer_response. The customer_response column is a free text column. Assuming a phone number is embedded in the customer_response column, how can you find the top 10 employees with the most phone numbers in the customer_response column?

135. Sort an array in Python so that it produces only odd numbers.

136. How can you achieve performance tuning in SQL? Find the numbers which have the maximum count in a list?

137. Generate a new list containing the numbers repeated in two existing lists.

138. How would you tackle a data pipeline performance problem as a data engineer?

How Data Engineering helps Businesses? | Why is Data Engineering In Demand?

Data engineering is more significant than data science. Data engineering maintains the framework that enables data scientists to analyze data and create models. Without data engineering, data science is not possible. A successful data-driven company relies on data engineering. Data engineering makes it easier to build a data processing stack for data collection, storage, cleaning, and analysis in batches or in real time, making it ready for further data analysis.

Furthermore, as businesses learn more about the significance of big data engineering, they turn towards AI-driven methodologies for end-to-end Data Engineering rather than employing the older techniques. Data engineering aids in finding useful data residing in any data warehouse with the help of advanced analytic methods. Data Engineering also allows businesses to collaborate with data and leads to efficient data processing.

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Data Engineer Job Growth and Demand in 2024

When compared to data science, data engineering does not receive as much media coverage. However, data engineering is a career field that is rapidly expanding and in great demand. It can be a highly exciting career for people who enjoy assembling the "pieces of a puzzle" that build complex data pipelines to ingest raw data, convert it, and then optimize it for various data users. According to a LinkedIn Search as of June 2022, there are over 229,000 jobs for data engineering in the United States, and over 41,000 jobs for the same in India

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Based on Glassdoor, the average salary of a data engineer in the United States is $112,493 per annum. In India, the average data engineer salary is ₹925,000. According to Indeed, Data Engineer is the 5th highest paying job in the United States across all the sectors. These stats clearly state that the demand for the role of a Data Engineer will only increase with lucrative paychecks. 

Image showing Data Engineer annual average salary in the US

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What Skills Does a Data Engineer Need?

Below are some essential skills that a data engineer or any individual working in the data engineering field requires-

  1. SQL: Data engineers are responsible for handling large amounts of data. Structured Query Language (SQL) is required to work on structured data in relational database management systems (RDBMS). As a data engineer, it is essential to be thorough with using SQL for simple and complex queries and optimize queries as per requirements.

  2. Data Architecture and Data Modeling: Data engineers are responsible for building complex database management systems. They are considered the gatekeepers of business-relevant data and must design and develop safe, secure, and efficient systems for data collection and processing.

  3.  Data Warehousing: It is important for data engineers to grasp building data warehouses and to work with them. Data warehouses allow the aggregation of unstructured data from different sources, which can be used for further efficient processing and analysis.

  4. Programming Skills: The most popular programming languages used in Big Data Engineering are Python and R, which is why it is essential to be well versed in at least one of these languages.

  5. Microsoft Excel: Excel allows developers to arrange their data into tables. It is a commonly used tool to organize and update data regularly if required. Excel provides many tools that can be used for data analysis, manipulation, and visualization.

  6. Apache Hadoop-Based Analytics: Apache Hadoop is a prevalent open-source tool used extensively in Big Data Engineering. The Hadoop ecosystem provides support for distributed computing, allows storage, manipulation, security, and processing of large amounts of data, and is a necessity for anyone applying for the role of a data engineer.

  7. Operating Systems: Data engineers are often required to be familiar with working with operating systems like LINUX, Solaris, UNIX, and Microsoft.

  8. Machine Learning: Machine learning techniques are primarily required for data scientists. However, since data scientists and data engineers work closely together, knowledge of machine learning tools and techniques will help a data engineer.

Get Set Go For Your Interview with ProjectPro’s Top Data Engineer Interview Questions

We hope these questions will help you ace your interview and land a data engineer role in your dream organization. Apart from the data engineer interview questions, here are some essential tips to keep you prepared for your next data engineering interview:

  • Brush up your skills: Here are some skills that are expected in a data engineer role:

    • Technical skills: Data Engineers have to be familiar with database management systems, SQL, Microsoft Excel, programming languages especially R and Python, working with Big Data tools including Apache Hadoop and Apache Spark.

    • Analytical Skills: Data Engineering requires individuals with strong mathematical and statistical skills who can make sense of the large amounts of data that they constantly have to deal with.

    • Understanding business requirements: To design optimum databases, it is important that data engineers understand what is expected of them, and design databases as per requirements.

  • Be familiar with the specific company with which you are interviewing. Understand the goals and objectives of the company, some of their recent accomplishments, and any ongoing projects you can find out about. The more specific your answers to questions like “Why have you chosen Company X?”, the more you will be able to convince your interviewers that you have truly come prepared for the interview.

  • Have a thorough understanding of the projects you have worked on. Be prepared to answer questions based on these projects, primarily if the projects are related to Big Data and data engineering. You may be asked questions about the technology used in the data engineering projects, the datasets you used, how you obtained the required data samples, and the algorithms you used to approach the end goal. Try to recall any difficulties that you encountered during the execution of the project and how you went about solving them.

  • Spend time working on building up your project profile and in the process, your confidence. By working on projects, you can expand your knowledge by gaining hands-on experience. Projects can be showcased to your interviewer but will also help build up your skillset and give you a deeper understanding of the tools and techniques used in the market in the field of Big Data and data engineering.

Make sure to get some hands-on practice with ProjectPro’s solved big data projects with reusable source code that can be used for further practice with complete datasets. At any time, if you feel that you require some assistance, we provide one-to-one industry expert guidance to help you understand the code and ace your data engineering skills.

FAQs on Data Engineer Interview Questions

You can pass a data engineer interview if you have the right skill set and experience necessary for the job role. If you want to crack the data engineer interview, acquire the essential skills like data modeling, data pipelines, data analytics, etc., explore resources for data engineer interview questions, and build a solid portfolio of big data projects. Practice real-world data engineering projects on ProjectPro, Github, etc. to gain hands-on experience.

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 (data scientists, etc.) 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.

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

  • What is data modeling?

  • What are the four V’s 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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