Interviews are often a drain on time and energy, and technical interviews can be especially challenging. This guide will help you practice how to answer typical data engineer interview questions. You will learn how to answer questions about SQL, Python, and databases.
Data engineering is one of the fastest-growing industries in the world today, and it is the second most in-demand profession after software development. The interviewers are looking for the best data engineers to join their team, so they ask lots of questions. They need people with specific abilities and knowledge. As a result, you need to do your best to ensure you live up to their expectations.
To make it easy for you, we have broken it into three parts:
- Data Engineer Interview Questions Sql
- Data Engineer Interview Questions Amazon
- Data Engineer Interview Questions Python
Data Engineer Interview Questions Sql
Data engineers need to be proficient in SQL. They use the querying language to model data, get performance metrics, and make data structures that can be used again and again.
The SQL-related needs of a data engineer often reflect the tasks of a typical engineer.
Questions on SQL for an Interview
- When comparing SQL and MySQL, what are the key differences?
- How many distinct varieties of SQL are there?
- What does “DBMS” mean? When you say “table” and “field” in SQL, what do you mean?
- Why do we use joins in SQL?
- When working with SQL, what are the distinctions between the CHAR and VARCHAR2 datatypes?
Data Engineer Interview Questions Python
Python is widely used for data science, machine learning, and artificial intelligence. Therefore, if you want to land a job as a data engineer, you should study up on Python’s definitions, theory, and function writing in order to ace the interview.
Here are the most frequently asked questions:
- Which Python libraries for working with data are the best?
- Why do we need to smooth the data? How do you accomplish this feat?
- What are some instances of the most fundamental data structures in Python? Which data structures are subject to user modification?
- Which of Python’s dictionaries and lists is more effective for lookups?
- How can duplicates be removed from a Python list?
Data Engineer Interview Questions Amazon
Amazon’s interviews for the position of data engineer are notoriously difficult. Even if you make it to the final onsite interview, you have only a 20% chance of being hired.
To help you prepare for your data engineering interview at Amazon, we’ve included answers to some of the most frequently asked questions in this section.
- How would one go about designing a database schema to accommodate a customer’s frequently changing address?
- How would you describe SQL to someone who knows nothing about computers?
- How does the Amazon database work?
- Explain auto-scaling in DynamoDB.
- Which of the four AWS services will you use to gather and process data about online purchases in real time?
- When do you use an inner join?
- What is a SQL full outer join?
- At the column level, which constraint is the only one that can be applied?
- Is it possible to undo the changes made by an ALTER command?
- How might one use pseudocolumns in a SQL database?
We have organized the interview questions into distinct groups to help you prepare for them.
Data Engineer Interview Questions For Freshers
Q. What does “Data Engineering” mean?
“Data engineering” is often used as a synonym for “big data.” In this part, we focus primarily on gathering information and conducting research. The information gathered from all these places is raw and unprocessed. Data engineering aids in the process of transforming this raw data into useful information.
Q. What does “data modeling” mean?
Data modeling is a way to make software design that is hard to understand for everyone easier to understand for everyone. It’s a picture of the relationships between data objects and the rules that apply to them. There are a number of abstract data objects in it, and rules determine their relationships.
Q. What can one expect to do in a data engineer position?
One of a data engineer’s main jobs is to collect, manage, and turn unstructured data into knowledge that data scientists and business analysts can use. The end goal is to make data easy to find so that businesses can use it to measure their performance and improve it.
Q. What kind of background knowledge is needed for a data engineer?
Data engineers need to be good at databases, building data infrastructure, containerization, and big data frameworks. The ideal candidate also has a lot of real-world experience with a wide range of technologies, such as Hadoop, Scala, Storm, HPCC, MapReduce, Rapidminer, Cloudera, SAS, SPSS, R, Python, Kubernetes, Docker, and Pig, among others.
Q. What are the techniques for optimal big data deployment?
Here’s what you need to do to set up a solution for big data.
Several tools, such as Salesforce, RDBMS, SAP, and MySQL, can be used to integrate data.
You should put the data you’ve collected in an HDFS or a NoSQL database.
Use processing frameworks like Spark, Pig, and MapReduce to put your big data solution into place.
Q. Do data engineers collect data?
Data engineers collect and clean the information that data scientists and analysts need. Data scientists and engineers often work together in close-knit teams to collect, import, and analyze data from start to finish.
Now that we’ve covered the basics, let’s move on to the more advanced set of data engineer interview questions that we’ve compiled.
Data Engineer Interview Questions For Experienced
Q. What kinds of collections does Hive have?
The following types of data can be stored in Hive:
Q. What sets a data architect apart from a data engineer?
When information enters an organization from various channels, it is the architect’s job to make sense of it all. A data architect must be proficient in data management tools like databases. The DATDC is also worried about how pivotal model conflicts will arise as a result of data changes.
Now, the data warehousing pipeline and the enterprise data hub architecture have been established with the help of a data engineer, who is primarily responsible for assisting the data architect.
Q. In Hive, what is SerDe?
Hive’s Serialization and Deserialization (SerDe) is a handy feature. It’s what happens when data is processed using Hive tables.
Deserialization is the process by which a record is transformed into a Hive-friendly Java object.
The serializer now transforms this Java object into an HDFS-friendly format. Eventually, HDFS takes over the storage role.
Q.Definition of “Rack Awareness”
By sending network traffic to the DataNode, which is physically located in the rack where the request came from, the NameNode can increase throughput and decrease latency for incoming requests.
Q. Can you tell me about the Hive table creation features?
Among Hive’s many table-making options are the ones below:
JSON tuple() \sStack()
Q. To what end does Hive employ the .hiverc file extension?
The.hiverc file is used for starting things up. Hive’s command-line interface (CLI) is where code is written, and this file is read as soon as the CLI is opened. It stores the default values you chose during setup.
Q. What is the purpose of the metastore in Hive?
The schema and the Hive tables are saved in the metastore. The metastore is a repository for metadata such as definitions and mappings. This is then filed away in an RDMS for future reference.
Q. What are the functions of *args and **kwargs?
The **kwargs function is used to indicate a set of arguments that are unordered and in line to be input to a function, while the *args function allows users to define an ordered function for use in the command line.
Q. How does one examine the database layout in MySQL?
The describe command can be used to examine a database’s structure. The grammar is elementary.
tablename: a description;
Q. Can you perform a string search in a MySQL table’s column?
MySQL does allow for string-specific and substring-specific operations. For this purpose, we employ the regex operator.
Q. What, exactly, is the difference between a database and a data warehouse?
Aggregation functions, calculations, and selecting subsets of data for processing are where most of your time will be spent when working with data warehouses. The main use of databases is to change, delete, and do other similar things with data. Working quickly and effectively is essential when dealing with either of these.
While the term “data engineer” may conjure up images of a dull, repetitive job, the field actually has many fascinating facets. That much is obvious from the kinds of hypothetical questions that could come up in an interview. The questions above are examples of the types of situational questions you should be prepared to answer. That’s the only way to prove that you’re qualified for the position.