Today, data engineering has become one of the fastest-growing jobs in the world after software development. Interviewers want their team to see the best data engineers, so they tend to interview the applicants in detail. They seek specific skills and expertise. You must also be prepared to fulfill the standards accordingly.
Before jumping to the topic, let’s know what precisely a Data engineer means?
Who is a data engineer?
Anybody who acts as a gatekeeper and facilitator to transfer and store data is a data engineer. Also, they are often required to convert big data into a helpful analysis type.
If you are looking to get an edge, data engineering certification is a great choice. Certifications test your experience and expertise against industry and vendor-specific standards to demonstrate the right qualifications for employers.
Data engineers usually have a degree in mathematics, science, or business. This kind of experience helps them use languages to process data and queries and, in many situations, use SQL engines for big data. Most data engineers get their first job after receiving their graduate degree, depending on their career or business.
Usually, data engineers can use Hadoop, Spark, and other open-source Big Data ecosystems and Java, Scala, or Python programming.
Receiving an interview invitation with a data engineer is an essential step towards the profession you want. A job interview offers you the opportunity to impress and inspire your future employer to see you as an outstanding candidate. Consider your general questions and your in-depth analysis of your experience and history to get ready for your job interview.
Technical interview questions
1st question: What is data engineering?
Answer: Data engineering enables information collection and processing by combining desktop software, mobile applications, cloud-based servers, and physical infrastructure. Careful construction, a solid pipeline, and competent staff are essential for practical data engineering. Data engineers are key data scientists collaborators who interpret and use the knowledge that we obtain.
2nd question: What are Big Data’s four V’s?
Answer: Let start explaining the four V’s of Big Data:
- The first V is a velocity that refers to the time at which big data is produced. Thus, data analysis can be considered.
- The second V is the range of different Big Data forms, whether in images, log files, media, and voice recording.
- The third V is the volume. The number of users, tables, data size, or records may indicate this.
- Veracity is the 4th V, which is linked to data uncertainty or security. In other words, it determines how certain it is that the data is accurate.
3rd question: What is Data Modelling?
Answer: The process of documenting complex software designs as a scheme for all to understand is data modeling.
4th question: Explain the design schemas of data modeling?
Answer: Two schemas, star, and snowflake are part of data modeling. Star schema consists of dimensional tables linked to a table of facts. The Snowflake schema includes a related table of points and dimension tables with snowflake layers.
5th question: What is the core concept behind Apache Hadoop?
Answer: The MapReduce algorithm is the basis for this. Map and Reduce operations are used in this algorithm to process a large data set. The key points in this concept are scalability and fault tolerance. These features are possible with the efficient use of MapReduce and Multi-Threading.
6th question: What steps do you take when implementing a big data solution?
Answer: In deploying a big data solution, there are three steps to be taken:
- Data ingestion – It is the first step in the implementation of a big data solution. Data from different sources such as SAP, MYSQL, Salesforce, logs, and internal databases are extracted. You may use real-time streaming or batch jobs for data ingestion.
- Data Storage- The extracted data is to be stored somewhere after the data is ingested. It is stored in either HDFS databases or NoSQL databases. For sequential HBase access for random reading or writing, HDFS works well.
- Data Processing- This is the third and last phase in a Big data solution deployment. The data is handled by one of the key frameworks such as MapReduce and Pig following storage.
7th question: What are some common data engineering problems?
- Continuous integration/Real-time Integration
- One problem is the storage of a great deal of data, and the information from that data is another problem.
- What methods can be used to achieve optimal outcomes, storage, productivity, and performance?
- Taking into account RAM configuration and processors
- Is fault tolerance there or not, and how to deal with failures?
8th question: How can a Big Data solution be deployed?
Answer: To deploy a big data solution, perform the following steps.
1) Incorporate data by RDBMS, SAP, MySQL, and Salesforce data sources
2) Store data derived from the NoSQL or HDFS databases
3) Deploy big data solutions using Pig, Spark, and MapReduce processing frameworks.
9th question: How important is Apache Hadoop’s Distributed Cache?
Answer: Hadoop has a helpful utility called Distributed Cache that boosts job efficiency by storing applications’ used files. You can specify a cache file with the JobConf configuration in an application.
Hadoop Application replicates these files to the nodes that a task needs to perform. It is done before the job is executed. Distributed Cache supports read-only and zip and jars file distribution.
10th question: Explain how Big Data and data analysis could boost company sales?
Answer: The following are ways to raise company revenues through data analysis and Big Data:
- Use data to ensure company growth effectively.
- Increase consumer satisfaction.
- Empirical turning to boost estimates of staffing levels.
- Reduction of organizations’ manufacturing costs.
Due to data engineering and Big Data’s importance, people with computer and IT skills are highly sought after in all sectors. Opting for data engineering bootcamps, degrees, and certifications helps Data engineers grow to be leaders in the field. Data engineering might sound like regular tedious work, but it has a lot of exciting aspects. Be prepared to address technical and situation questions that help deserve your job well.