Preparing for an interview with the kind of questions you might be asked is a great way to ace an interview. If you know the questions and are prepared to answer those questions in the best possible way, you have won the battle. No matter your experience, it is a good practice to go through the possible data engineer interview questions. Ideally, interviewers would want you to think on the fly and check your answers, but not many people are good at communicating well enough what they already know.
If you are prepared with the kind of questions you might be asked, you gain a certain level of confidence during the interview, which positively impacts your chances of getting through successfully. There are generic interview questions that try to know how you present yourself, what are your thought processes, your attitude towards co-workers and the working environment, and your work ethic. Then there are domain-specific questions targeted at the role in which the interview is conducted for. Today let’s go through such questions about the role of a Data Engineer.
Before we get on with the questions, one important point is the difference between Data Engineer and Data Scientist. There are many times when interview candidates confuse between the 2.
Data Engineers and Data Scientists work closely on Big Data projects; thus, there is an apparent overlap between the two roles. The two roles are distinct in their core responsibilities, although they work together to achieve a common main objective.
Data Scientists are consumers of the data infrastructure that the Data Engineers build and maintain. Data Engineers ensure that the system is robust and well equipped to handle and process huge amounts of data while being efficient.
This question is asked in almost every interview to understand how deep your passion runs for data engineering, which makes you get through your challenges every day. You may want to share your story about where you started, where you got hooked on the role, how you upskilled to gain knowledge about the field and what challenges you overcame to get knowledge or any experience that you might have in data engineering.
Another fundamental question you can answer is by pointing out some exciting features of the role, the job involved, and the kind of work the company is doing in that field that motivates you to join the company. Highlight your qualification, experience, skills, and personality to show how all the experience you have gained will help you be a better Data Engineer.
This question is to check if you have understood the role and whether you have a holistic view or a confined understanding. You could start by saying what is known about data engineering in textbooks and then add your own experience or views.
Data Engineers setup and maintain the infrastructure that supports the information infrastructure and related applications. The Data Engineer’s role has been carved out from a core IT role after the middle layer in information systems within businesses started growing manifold. To maintain a big data architecture, you need people who understand data, data ingestion, extraction, transformation, data loading, and more, which is more data specific and far removed from core IT practices and yet not sophisticated enough to handle data mining, identifying patterns in data, recommend data-backed changes to the business leadership, which is what data scientists do. So, data engineers are a crucial link between core IT and data scientists.
Data modeling is a scientific way of documenting complex data systems by way of a diagram to give a pictorial and conceptual representation of the system. You could also expand on any experience that you have had with data modeling.
There are mainly two types of schemas in data modeling: 1) Star schema and 2) Snowflake schema. Expand on each or any one of them that you are asked to explain.
Data Engineers constantly work with data that is coming into the systems in all sorts of formats. Broadly categorizing them as structured and unstructured. They differ in the way these are stored and accessed. For your convenience, some of the differences are listed.
|Criteria||Structured Data||Unstructured Data|
|Storage||DBMS||Unmanaged file structures|
|Standard||ADO.net, ODBC, and SQL||STMP, XML, CSV, and SMS|
|Integration Tool||ELT (Extract, Transform, Load)||Manual data entry or batch processing that includes codes|
|scaling||Schema scaling is difficult||Scaling is very easy.|
This is an important question, and you should be thorough with your answer as it assesses your understanding of the role and how much you have invested in learning them. You should include the below points in your response.
This question is asked to assess your knowledge of the systems from the ground up. There is no perfect answer to this question, and no answer is bad. Responses to the below questions might give you a good answer.
Once these questions are answered, you try to map the available technologies to address the challenges and characteristics of each. This is not an exhaustive list of questions but is an approach that you can take to respond to the original question from the interviewer.
The algorithm that you select to discuss must be one that you are good at and preferably used by the company. There will be follow-up questions to understand the depth of your answer, like,
Be sure to include the challenges that prop up when transforming from unstructured to structured in your response.
There is a good likely hood of this question being asked if you are an experienced candidate. Do mention the tools used for building the model and a small brief about how you’ve done it.
Talk about the tool you have used and highlight some of its features that helped you pick it for ETL.
Big Data is a phenomenon, a result of exponential growth in data availability, storage technology, and processing power, while Hadoop is a framework that helps to handle huge volumes of data that reside in the Big Data ecosystem. Describe the components of Hadoop as below.
NameNodes store metadata of all the files stored on the cluster. Basically, metadata about data nodes, bits of information like the location of blocks, size of files, and hierarchy. It is similar to a File Allocation Table (FAT), which stores information about blocks of data that make up files and where they are stored on a single computer. NameNodes keep the same kind of information for a distributed file system. Under normal circumstances, a NameNode crash will result in data non-availability, although all data blocks are intact. A high availability setup will ensure there is a passive NameNode that backs up the primary one and takes over in case the NameNode fails.
Blocks are the smallest unit of data allocated to a file, which the Hadoop system automatically creates for storage in different nodes in a distributed file system. Block Scanner verifies the integrity of a DataNode by checking the data blocks stored on it.
A few other questions that are asked in the interviews that you must be prepared for are listed below.
The XML configuration files available in Hadoop are as follows:
File System Check, commonly known as FSCK, is an essential HDFS command. It is mostly used when you need to check for errors and discrepancies in files.
When Hadoop comes across a large file, it automatically divides it into smaller chunks known as blocks. A block is considered to be the smallest data entity. A block scanner is installed to ensure that the loss-of-blocks created by Hadoop are successfully installed on the DataNode.
The acronym COSHH stands for Classification and Optimization-based Scheduling for Heterogeneous Hadoop Systems. It enables scheduling at both the cluster and application levels, as the name implies, to positively impact work completion time.
The star schema, often known as the star join schema, is one of the simplest schemas in the Data Warehousing concept. The table structure is shaped like a star and consists of fact tables accompanied by dimension tables. The star schema is commonly employed when dealing with enormous amounts of data.
Hive is used to give a user interface for managing all of Hadoop’s stored data. The data is mapped using HBase tables and worked on as needed. Hive queries (akin to SQL queries) are run to generate MapReduce jobs. This is done to keep the complexity under control when running numerous jobs at the same time.
Rack awareness is a notion in which the NameNode uses the DataNode to boost incoming network traffic while simultaneously executing reading or writing operations on the file closest to the rack from which the request was made.
While answering these types of Data Engineer interview questions, keep your explanation concise on how you would create a plan that works with the company setup and how you would implement the plan, ensuring that it works by first understanding the company’s data infrastructure setup, and you would also discuss how it can be improved or made better in the coming days with further iterations.
Hadoop includes a valuable utility feature known as Distributed Cache, which enhances job speed by caching files used by applications. Using JobConf settings, an application can specify a file for the Cache. The Hadoop framework replicates these files to the nodes where a task must be done. This is done before the task execution begins. Distributed Cache allows the dissemination of read-only, zip, and jar files.
Hive is an interface for managing data stored in the Hadoop ecosystem. Hive is used to map and manipulate HBase tables. Hive queries are translated into MapReduce jobs to hide the complexity of creating and running them.
The following are some examples of how data analytics and big data can boost company revenue:
Preparing for an upcoming interview can be intimidating, whether you’re new to the field of Data Science and want to break into a Data Engineering career or an experienced Data Engineer searching for a new opportunity. Considering how competitive the market is right now, you should be well-prepared for your interview. The above-mentioned are some of the most common Data Engineer interview questions and answers that will aid you in preparing for the interviews. Getting formal training and earning your certification is one of the greatest strategies to ace your next Data Engineer job interview. If you want to be a Data Engineer, check out our PG Certificate Program in Data Science & Machine Learning today and start building the skills that will help you land your dream job.