Top Data Engineer Interview Questions to Practice for FAANG+ Interviews
The demand for data engineering skills is increasing exponentially, and top companies set difficult data engineer interview questions to test your core competencies. To ace a technical interview for a position in data engineering, you must plan ahead of time.
Your answers to data engineer interview questions should demonstrate your extensive knowledge of data modeling, machine learning, building and maintaining databases, and locating warehousing solutions. During the data engineering interview, you should also be prepared to answer some behavioral interview questions that probe your soft skills. Read on to discover the most anticipated data engineer interview questions at top FAANG+ companies to uplevel your tech interview prep.
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To help you kickstart your data engineer technical interview prep, here is a compiled list of data engineer interview questions.
Here's what we'll cover in this article:
- Top Data Engineer Interview Questions and Answers
- Facebook Data Engineer Interview Questions
- Amazon Data Engineer Interview Questions
- Google Data Engineer Interview Questions
- Sample Data Engineer Interview Questions for Practice
- FAQs on Data Engineer Interview Questions
Top Data Engineer Interview Questions and Answers
Before we get into the most common data engineer interview questions, let's go over the top skills that a data engineer should have. Before you begin your data engineering technical interview prep for FAANG+ companies, you must have the following core skills:
- Programming languages including Python, Scala, Java, C, C++, C#, .Net, Ruby, SAS, MatLab, R
- Postgres, Relational Databases
- UNIX, Linux
- SQL, NoSQL, MySQL
- ETL skills; SSIS, SSRS, PowerCenter, Data Stage
- Big Data technologies such as Hadoop, Apache Kafka, Hive, Spark, Cassandra
- Google Cloud and AWS
- ELK Stack; APIs; Oracle; Git; Snowflake; Tableau
- Storm, MLib, Spark Streaming
- Agile, Scrum
- BI, Platform Engineering
- Luigi, Airflow, Azkaban
- Knowledge of Machine Learning
Read more about the role of a Data Engineer vs. Data Scientist, their career outlooks, salaries, and skill requirements.
Q1. What are the types of design schemas in data modeling?
There are mainly two types of design schemas in data modeling:
- Star schema - It is the simplest type of Data Warehouse schema. Its structure is like a star, where the star's center may have one fact table and multiple associated dimension tables. It is useful for querying large data sets.
- Snowflake schema - It is an extension of a Star Schema. It adds additional dimensions and looks like a snowflake in structure. The dimension tables are normalized, and data is split into additional tables.
Q2. What is Big Data? How is Hadoop related to Big Data?
Big Data is the collection of data from several sources. It is a result of exponential growth in data availability, processing power, and storage technology. It is often characterized by four Vs:
Hadoop is a framework technology that helps in handling huge volumes of data in the Big Data ecosystem. The components of the Hadoop application:
- Hadoop Common: A common set of utilities and libraries for Hadoop.
- HDFS: A distributed file system with high bandwidth in which the Hadoop data is stored.
- Hadoop MapReduce: A software framework for the provision of large-scale data processing.
- Hadoop YARN: YARN stands for Yet Another Resource Negotiator. It is used for resource management and task scheduling for users.
Q3. What is NameNode? What are the implications of the NameNode crash?
NameNodes in Hadoop store metadata of all the files on the Hadoop cluster. The metadata has data nodes, information of the location of blocks, size of files, hierarchy, and more. NameNode is the master node. It maintains and manages blocks present on DataNodes in the Apache Hadoop HDFS Architecture.
NameNode crash results in the non-availability of data, but all blocks of data remain intact. In a high availability setup, there is a passive NameNode that backs up the primary one. So, this takes over in case of a NameNode crash.
Q4. Explain Block and Block Scanner in HDFS.
Hadoop Distributed File System or HDFS automatically splits huge data files into smaller fragments. Blocks form the smallest unit of a data file.
Block Scanner identifies corrupt DataNode blocks. It verifies the available list of blocks presented on a DataNode.
Q5. What happens when Block Scanner identifies a corrupted data block?
The following steps occur when Block Scanner identifies a corrupted data block:
- Firstly, as the Block Scanner finds a corrupted data block, DataNode reports to NameNode.
- NameNode starts creating a new replica using a replica of the corrupted block.
- Next, the replication count of the correct replicas tries to match with the replication factor. If the match is found, the corrupted data block will not be deleted.
Q6. What is your understanding of Heartbeat in Hadoop?
Name nodes and data nodes communicate via Heartbeat in Hadoop. Heartbeat is the signal sent by the DataNode to the NameNode at regular intervals to indicate its presence. Therefore, as the name suggests, heartbeat indicates that DataNode is alive.
Learn more about the roles and responsibilities of a data engineer.
Q7. What is the significance of Distributed Cache in Apache Hadoop?
Distributed cache is a useful utility feature in Hadoop that improves the performance of jobs by caching the application files. It can cache read-only text files, jar files, archives, and more. An application specifies a file for the cache by employing JobConf configuration.
Q8. What is COSHH?
COSHH stands for Classification and Optimization-based Scheduling for Heterogeneous Hadoop systems. This multi-objective Hadoop job scheduler provides scheduling at the cluster and the application levels to impact the completion time for jobs positively.
Q9. What is Metastore in Hive?
Metastore is a central repository in Hive that stores partitions in a relational database, schema as well as the Hive table location. It is stored in RDBMS supported by JPOX. Hive provides clients access to this information by using Metastore service API.
Q10. What is SerDe in Hive? What are the different implementations of SerDe in Hive?
SerDe is the short form of Serializer or Deserializer. SerDe allows you to read data from table to and write it back out to HDFS in any format you want in Hive.
Q11. List out objects created by the 'create' statement in MySQL.
The 'create' statement in MySQL creates the following objects:
Facebook Data Engineer Interview Questions
Facebook deals with a significant amount of user data, making it the perfect place for a data engineer. If you are a data engineer aspiring to join the Facebook team, you must go through the following Facebook Data Engineer interview questions.
- What is meant by rack awareness?
- What is meant by skewed tables in Hive?
- What is the significance of the .hiverc file in Hive?
- What steps would you take to validate a data migration from one database to another?
- What are the advantages of the AWS network for data engineers?
- What are version control systems? State differences between Git and GitHub.
- What are the best features of Kafka for data engineering?
- Why do we use clusters in Kafka, and what are its benefits?
- State the differences between Rabbitmq and Kafka.
- What issues does airflow resolve?
Amazon Data Engineer Interview Questions
Amazon relies heavily on data collection and utilization, thereby making data engineering a lucrative position at the company. You can ace your Amazon data engineer interview with radical preparation. Here is a list of the most anticipated Amazon data engineer interview questions that you must practice before your final interview.
- How would you achieve security in Hadoop?
- What are the various modes in Hadoop?
- What is FSCK?
- What are *args and **kwargs used for?
- What are the functions of Secondary NameNode?
- What are the different phases of reducer in Hadoop?
- For a given order table, write the required SQL queries.
- Explain and write a code for the Traveling Salesman problem.
- How would you solve a data pipeline performance issue?
- Which Python libraries are best for efficient data processing?
Google Data Engineer Interview Questions
If you are preparing for Google data engineering positions, you should be well-versed in cleaning, organizing, and manipulating data using pipelines and other latest technologies. The following Google data engineer interview questions will help you ace your upcoming interview.
- How would you handle duplicate data points in an SQL query?
- For an expected increase in data volume, what steps would you take to add more capacity to the data processing architecture?
- For a given array of integers of length n spanning 0 to n with one missing, you have to write a function missing_number that returns the missing number in the array.
- For a given list of integers, write a program to find the index where the sum of the left half of the list equals the right half. Return -1 if there is no index satisfying the condition.
- When would you use the NumPy library vs. pandas?
- For a given string S, write a function recurring_char to find its first recurring character. If there is no recurring character, return None.
- Design a database to represent a Tinder-style dating app.
Recommended Reading: Google Data Engineer roles and responsibilities.
Sample Data Engineer Interview Questions for Practice
You can practice some more data engineering interview questions to get through your interview successfully.
- What are the core skills required in data engineering?
- What makes you interested in pursuing a career in data engineering?
- What is the difference between structured and unstructured data?
- Elaborate on an algorithm you used in a recent project.
- What is your experience of Big Data in a cloud environment?
- Which tools did you pick up for your projects and why?
- How would you search for a specific String in the MySQL table column?
- What is Hadoop streaming? Can we use Hadoop for real-time streaming?
- What is your favorite ETL tool, and how is this best compared to others?
- Describe a situation when you found a new use case for an existing database and how that positively impacted the business?
FAQs on Data Engineer Interview Questions
Q1. What should I study for data engineer interview questions?
You must brush up on fundamental and advanced topics of SQL and Python. As a data engineer, you should be well-versed in data modeling, data pipelines, distributed system fundamentals, event streaming, and some system design as well.
Q2. Is machine learning required for data engineering interview questions?
If you are preparing for data engineer interviews, you only need a basic knowledge of machine learning so that it enables you to understand a data scientist's needs better and build more accurate data pipelines.
Q3. Do I need to ace math for data engineering?
You only require basic math knowledge for data engineering. You need to focus primarily on statistics and probability in math, as your knowledge of statistics will help you have an idea of what the data scientists on your team will be doing.
Gear Up for Your Next Data Engineer Interview
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