Data engineers do similar work as data analysts but on a larger scale. While they transform data into business insights, they also build and maintain database pipeline architectures. It’s high-demand work and well-paid — the average salary is around $88,000.
Planning to become a data engineer? If so, keep reading. We’ve curated a list of the best data engineering interview questions to help you ace your upcoming interview.
We’ve divided our list of data engineering questions into three categories: basic, intermediate, and advanced.
Basic Data Engineer Interview Questions
Let’s start with some basic interview questions for data engineer entry-level roles.
1. How does data modeling work?
Data modeling is a technique for making complex software architecture accessible. Conceptual illustrations show the connections between distinct data objects and the rules.
2. Explain data engineering.
Data engineering is the process of building, testing, and maintaining database structures to analyze data on a large scale. Data engineering helps to turn this unstructured data into usable business insights.
3. Describe NameNode.
NameNode serves as the HDFS (Hadoop Data File System) main hub. It keeps track of different files across clusters and maintains HDFS data. However, NameNode does not store the actual data — DataNodes store it.
4. Describe streaming in Hadoop.
Streaming enables the construction of maps and reduces jobs and the submission of those jobs to a particular cluster.
5. Expand on HDFS.
HDFS stands for Hadoop Distributed File System. This file system handles extensive data collections and runs on commodity hardware, i.e., inexpensive computer systems.
6. Explain HDFS's Block and Block Scanner.
A block is the smallest data file component. Hadoop automatically divides large files into small workable segments. On the flip side, the Block Scanner verifies a DataNode's list of blocks.
7. What happens when Block Scanner finds a faulty data block?
First, DataNode alerts NameNode. Then, NameNode creates a new replica using the corrupted block as a starting point.
The goal is to align the replication factor with the replication count of the proper replicas. If a match is discovered, the corrupted data block won't be removed.
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8. Describe the attributes of Hadoop
The following are key attributes of Hadoop:
- Open-source, freeware framework
- Compatible with a wide range of hardware to simplify access to new hardware inside a given node
- Enables faster-distributed data processing
- Stores data in the cluster, separate from the other operations.
- Allows the creation of three replicas for each block using various nodes.
9. What does COSHH stand for?
COSHH stands for Classification and Optimization based Schedule for Heterogeneous Hadoop systems. It lets you schedule tasks at both application and cluster levels to save on task completion time.
10. Describe the Star Schema.
A star schema, often known as a star join schema, is the most fundamental type of data warehouse model. It is called a star schema due to its structure. The Star Schema allows for numerous related dimension tables and one fact table in the star's center. This model is ideal for querying large data collections.
11. How is a big data solution deployed?
This is one of a few big data engineer interview questions you might encounter.
Here’s how you can deploy a big data solution:
- Combine data from many sources, including RDBMS, SAP, MySQL, and Salesforce.
- Save the extracted data in a NoSQL database or an HDFS file system.
- Utilize processing frameworks like Pig, Spark, and MapReduce to deploy a big data solution.
12. What do you know about FSCK?
File System Check or FSCK is a command that HDFS leverages. This command checks inconsistencies and problems in files.
13. Describe the Snowflake Schema.
A Snowflake Schema is an extended model of the Star Schema, which adds new dimensions and resembles a snowflake. It divides data into extra tables by the normalization of the dimension tables.
14. Describe the distributed Hadoop file system.
Scalable distributed file systems like S3, HFTP FS, FS, and HDFS are compatible with Hadoop. The Google File System is the foundation for the Hadoop Distributed File System. This file system is made to be easily operable on a sizable cluster of the computer system.
15. What does YARN's complete name entail?
YARN stands for Yet Another Resource Negotiator. It’s responsible for allocating system resources to applications running in a Hadoop cluster. In addition, it schedules applications to run on different clusters.
16. List the Hadoop modes.
Hadoop has three modes:
1) Standalone
2) Pseudo-distributed
3) Completely distributed
Intermediate Data Engineer Technical Interview Questions
You’ll likely see these data engineer questions for associate-level data engineer roles.
17. How can you achieve security in Hadoop?
For Hadoop security, take the following actions:
1) Secure the client's authentication channel with the server, and give the client time-stamped documents.
2) The client asks TGS for a service ticket using the time-stamped information.
3) The client uses a service ticket to self-authenticate to a particular server in the last phase.
18. What does Hadoop's Heartbeat mean?
NameNode and DataNode converse with one another in Hadoop. The heartbeat is the regular signal DataNode sends to NameNode to confirm its presence.
19. What is big data?
Big data is data of immense volume, variety, and velocity. It entails larger data sets from various data sources.
20. What does FIFO entail?
FIFO is a scheduling algorithm for Hadoop jobs.
21. List the standard port numbers on which Hadoop's task tracker, NameNode, and job tracker operate.
Hadoop’s task and job trackers all run on the following default port numbers:
- Task tracker runs on 50060 port.
- NameNode runs on 50070 port.
- Job Tracker runs on 50030 port.
22. How do you turn off the HDFS Data Node's Block Scanner?
Set dfs.datanode.scan.period.hours to 0 to disable Block Scanner on HDFS Data Node.
23. How can the distance between two Hadoop nodes be defined?
The getDistance() function determines the distance between two Hadoop nodes.
24. Why is Hadoop based on commodity hardware?
Commodity hardware is accessible and reasonably priced. It is a platform that works with Linux, Windows, or MS-DOS.
25. Describe the HDFS replication factor.
The replication factor is the total number of file copies in the system.
26. What information does NameNode keep?
NadeNodes keep HDFS metadata, including namespace and block information.
27. Explain “rack awareness."
When reading or writing any file located closer to the neighboring rack to the Read or Write request in the Hadoop cluster, Namenode leverages the Datanode to reduce network traffic.
Namenode keeps track of each DataNode's rack id, which is known as rack awareness.
28. What are the Secondary NameNode's functions?
Secondary NameNode's functions are as follows:
- FsImage, which keeps a copy of both the FsImage and EditLog files.
- NameNode failure: The Secondary NameNode's FsImage can be used to reconstruct the NameNode if it crashes.
- Checkpoint: Secondary NameNode uses this checkpoint to ensure that HDFS data is not damaged.
- Update: The EditLog and FsImage files are both automatically updated. Updating the FsImage file on the Secondary NameNode is beneficial.
29. What occurs if a user submits a new job when NameNode is down?
Hadoop's NameNode is a single point of failure, making it impossible for users to submit or run new jobs. The user must wait for NameNode to restart before performing any jobs since if NameNode is down, the job may fail.
30. Why does Hadoop employ the context object?
The Hadoop framework uses context objects with the Mapper class to communicate with the rest of the system. The system configuration information and job are passed to the context object in its function Object() { [native code] }.
To pass information in the setup(), cleanup(), and map() functions, we employ context objects. With the help of this object, crucial data is made available for map operations.
31. Define the Hadoop Combiner.
The Hadoop Combiner comes after Map but before Reduce. Combiner builds key-value pairs from the result of the Map function and sends them to the Hadoop Reducer. The Combiner must convert the final output from Map into summary records with the same key.
32. Which replication factor does HDFS, by default, offer? What does it mean?
In HDFS, the default replication factor is three, meaning there will be three replicas of each piece of data.
33. What is Hadoop's "Data Locality?"
Data movement over the network is unnecessary in a Big Data system due to the amount of data. Hadoop is now attempting to bring processing closer to the data. The information is kept local to the storage place in this manner.
Advanced or Senior Data Engineer Interview Questions
These data engineering questions are for senior engineering roles, likely with leadership duties and management responsibilities.
34. Define Balancer in HDFS.
The balancer in HDFS is a tool the admin staff leverages to shift blocks from overused to underused nodes and redistribute data across DataNodes.
35. Describe the HDFS Safe mode.
In a cluster, NameNode operates in read-only mode, while NameNode starts out in Safe Mode. Safe Mode inhibits writing to the file system. At this point, it gathers information and statistics from each DataNode.
36. What role does Apache Hadoop's distributed cache play?
Distributed cache, a key utility feature of Hadoop, enhances job performance by caching the files used by applications. Using JobConf settings, an application can specify a file for the cache.
The Hadoop framework copies these files to each node where a task must be run. This is carried out prior to the task's execution. In addition to zip and jar files, Distributed Cache offers the dissemination of read-only files.
37. What does SerDe in the Hive mean?
Serializer or Deserializer is the full form of SerDe. Hive's SerDe feature lets you read data from a table and write data in any format you like to a particular field.
38. List the elements of the Hive data model.
The Hive data model consists of these elements:
- Tables
- Partitions
- Buckets
39. Describe how Hive is used in the Hadoop ecosystem.
Hive offers a management interface for data stored within the Hadoop environment and allows you to work with and map HBase tables.
The complexity involved in setting up and running MapReduce jobs is concealed by converting Hive searches into MapReduce jobs.
40. Describe the purpose of the .hiverc file in Hive.
The .hiverc file is Hive’s initialization file. When we launch Hive's Command Line Interface (CLI), this file is initially loaded. In the .hiverc file, we can set the parameter's starting values.
41. Can you create more than one table in Hive for the same data file?
Yes, you can generate many table schemas for a single data file. Hive stores its schema in the Hive Metastore. We can retrieve several results from the same data using this model.
42. What does a skewed table mean in Hive?
Skewed refers to a table's tendency to contain column values more frequently. Skewed values are saved in separate files, and the remaining data is written to a different file when a table is formed in Hive with the SKEWED flag.
43. What are the differences between an operational database and a data warehouse?
Databases that use Delete SQL commands, Insert, and Update are operational standards with a focus on quickness and effectiveness. As a result, data analysis may be a little more challenging.
On the other hand, a data warehouse places more emphasis on aggregations, calculations, and select statements. Because of these, data warehouses are a great option for data analysis.
44. Differentiate between a data engineer and data scientist.
This is one of the most common data engineering questions candidates hear in interviews.
Data scientists study and understand complicated data, whereas data engineers create, test, and manage the entire architecture for data generation. They concentrate on organizing and translating big data. Data engineers also build the infrastructure data scientists need to function.
45. As a data engineer, how would you go about creating a new analytical product?
Understanding the overall product outline will help you fully grasp a project’s requirements and scope. The second stage would be to research each measure’s specifics and causes.
Consider as many potential problems as you can to build a more resilient system with an appropriate level of granularity.
46. Which two messages does NameNode get from DataNode?
DataNodes provide NameNodes with information about the data in the form of messages or signals.
The two indicators are:
- Block report signals, which is a list of the data blocks stored on the DataNode and an explanation of how they operate.
- DataNode's heartbeat, which indicates it’s active and working. A recurring report helps decide whether to employ NameNode. If this signal is not sent, DataNode's operation has apparently ceased.
47. How does schema evolution work?
Schemas have advanced to the point where the same set of data can be stored in numerous files with different but compatible schemas. You can automatically identify and combine those files’ schema by using Spark's Parquet data source.
A typical approach to dealing with schema evolution without automatic schema merging is to reload historical data, which is time-consuming.
48. How does orchestration work?
IT firms must manage a lot of servers and apps, but doing it manually isn’t scalable. The more complicated an IT system is, the harder it is to keep track of all the moving elements. The demand to integrate several automated jobs and their configurations across groups of systems or machines is growing, coupled with the necessity to combine such automated operations and settings. This circumstance benefits from the usage of orchestration.
A computer system, application, and service orchestration is the automated configuration, administration, and coordination of these components. Orchestration makes it easier for IT to manage challenging operations and processes. Numerous technologies for container orchestration, including Kubernetes and OpenShift, are available.
49. Describe the fundamental idea underlying the Apache Hadoop Framework.
It is based on the MapReduce algorithm, to be precise. The Map and Reduce procedures of this technique are used to process a large data set. Reduce summaries of the data while Map filters and sorts the data. The main ideas behind this paradigm are scalability and fault tolerance. By effectively utilizing MapReduce and Multi-threading, we may successfully implement these functionalities in Apache Hadoop.
50. Mention some of Hadoop's key attributes.
- Hadoop is a free, open-source framework whose code can be modified to suit different needs.
- It supports faster-distributed data processing with MapReduce.
- Hadoop is quite forgiving and, by default, permits the user to build three clones of each block at several nodes. Therefore, even if one of the nodes fails, we may still recover the data from another node.
- Scalable and hardware-neutral.
- Due to Hadoop's cluster-based data storage, all other operations were unaffected. Thus, it is trustworthy. The failure of the machines has no impact on the data that has been stored.
Bonus Tips
This list of data engineer interview questions is a go-to way to prepare for your next data engineer interview.
Here are some additional tips that may help you ace your interview:
- Test mock interviews with friends to catch difficult questions before the big day.
- Consult with a senior data engineer to find out their experience in the interview process.
- Keep reading big data and data engineering articles and blogs to maintain industry knowledge.
- Prepare for scenario-based interview questions.
Conclusion
This set of data engineer interview questions and answers covers extensive technology topics about data engineering and big data. Prepare for questions about Hadoop, as well as scenario-based questions where you’ll be tasked with recalling previous experiences.
Reviewing data engineering questions is a fabulous starting point, but there’s more to do on your journey to a data engineering career.
Explore How to Become a Data Engineer
Frequently Asked Questions
1. What Questions Are Asked in a Data Engineer interview?
We have prepared these top 50+ questions, which are helpful for both beginners and professionals. You’ll be asked questions about Hadoop, NameNodes, data modeling, analysis, and more.
2. What Skills Does a Data Engineer Need?
Here are some common skills and expertise you’ll need in a data engineering role:
- Knowledge of database tools
- Coding
- Critical thinking
- Experience with data analysis
- Knowledge of data transformation, buffering, ingestion, and mining tools
- AI and machine learning experience
- Data warehousing and ETL tools
- Real-time processing frameworks
3. How Can I Prepare for Data Engineering?
Focus on core data and mathematical skills, and build some projects to prepare for a data engineering career. Also, go over our extensive list of data engineering questions.
