Data engineering courses are available online and offline all over, and acquiring a data engineering certification is certainly a big achievement for any professional. This is a top-level profession that puts one in charge of an organization’s data processing infrastructure and many times in charge of others.
However, the greatest challenge lies with winning the confidence of your interviewing panel to land your first or dream job depending. If you never built some practical experience during your coursework, it is time to seriously think about it before approaching an organization to hire you.
A candidate that is fit for a data engineering position is an innovative person with excellent interpersonal skills, analytical skills, and a working knowledge of big data and machine learning concepts.
20 frequently asked data engineer interview questions
Ultimately, you will have to face an interviewing panel to land the job you desire. Which questions can you be asked, and how should you answer them? Here are some frequently asked interview questions that you should take note of.
- Explain the main responsibilities of a data engineer
The main responsibility of a data engineer is to manage the entire data ecosystem of an organization. Some of the roles of a data engineer include:
- Developing database solutions for the organization
- Database and schema design
- Act as liaison between the database and data science departments
- Managing data pipelines as well as the flow of data
- Data cleaning and data preparation
- Supports the development of ETL systems and manages ETL data processes
- Develop ad-hoc data query building operations for purposes of reporting and analysis
- What core languages and technologies that a data engineer will use in the course of his duties?
Some common languages and technology used by data engineers include:
- Machine learning
- Mathematics: probability and linear algebra
- Statistics: trend analysis and regression
- Python, R, and S
- SQL and Hive QL domain-specific languages
- What is data modeling?
Data modeling is the process by which data models are created that will be used to store and manage data in databases. It is the process of visually representing software or system design specifications for data objects, the association between them, and their rules and requirements.
- Name the various types of design schemas in data modeling.
There are two main types of design schemas in data modeling. These are:
- Star (Star Join) schema. This is the basic type of schema in a database system. It features a star-like structure where the center of the star is a fact table that stores dimension key columns that relate to several dimension tables. The dimension tables describe the data being modeled and are organized into columns. The star schema is suitable for querying large data sets.
- Snowflake schema. The Snowflake schema is an expanded Star schema. It features a fact table at the center that connects to dimensional tables, which further split into additional tables, thus looking like a snowflake.
- Distinguish between relational and non-relational databases in data engineering.
A relational database has data organized into tables, with each table having a defined schema of columns and rows that represent unique key values for the data in the columns. This structure allows users to access data in relation to other tables.
A non-relational database, on the other hand, is schemaless. It does not have a rows and columns structure and allows data to be stored in a wide range of formats, for instance key/value pairs, graphs, JSON documents, and others which makes them more flexible.
- What is the difference between SQL and NoSQL?
The first and fundamental difference between SQL and NoSQL databases is that the former is a relational database while the latter is a non-relational database. In addition to the fact that SQL databases feature a defined schema and query language while NoSQL databases accommodate varying data formats and are best for unstructured data. SQL databases scale vertically, while NoSQL databases scale easily horizontally.
- How do you handle duplicate data points in an SQL query?
This depends on the kind of data that is being used. There are several occasions where data can be duplicated in a table. It is good to know the columns and values that have a higher likelihood of being duplicated.
In dealing with duplicate data points, fetching only the unique records becomes more effective than fetching all sets of duplicate data points.
This is done by applying the SQL keywords ‘DISTINCT’ and ‘UNIQUE’ are applied to eliminate the duplicate records.
Another way of dealing with duplicate data points is to use the ‘GROUP BY’ keyword and then filter.
- How is a Cache Database used?
Other than being used as a data storage solution, a database can also be used as a cache. Caching is a technique used to store records that are frequently queried temporarily. This improves both the speed and performance of the database by increasing the availability of these records even when the database server is down.
This ultimately reduces the workload in the form of frequent querying on your database for better performance. All types of databases can be installed with the caching function. Caching is a minimally invasive strategy for improving database performance.
- What are the various XML configuration files available in Hadoop?
There are four XML configurations in Hadoop which are:
- What is a NameNode in Hadoop?
A NameNode is the center of the Hadoop Distributed File System (HDFS). It is the daemon that runs on the MasterNode of a Hadoop cluster. Its function is to store the metadata of slave nodes in a Hadoop cluster to make it easily retrievable, also monitoring the slave nodes using the heartbeat technique. Another function of the slave node is to replicate data to other slave nodes to provide high availability.
- Which two messages does the DataNode transmit to the NameNode?
The two messages transmitted to the NameNode by the DataNode are:
- Block report. A block report is a report containing the data block IDs of blocks in the HDFS along with a generation stamp and the length of each block replica contained in a server.
- Heartbeat. During communication between the NameNode and the DataNode, a heartbeat is a signal that the DataNode sends to the NameNode regularly as an indication that it is present and operating normally.
- What is Context Object, and why is it used in Hadoop?
Hadoop uses Context Objects to allow the Mapper/Reducer to interact with the rest of the Hadoop system. Context Object comes packaged with configuration data and job details, information that it passes in setup(), cleanup(), and map() operations.
- What does the abbreviation COSHH stand for?
COSHH means Classification and Optimization-based Schedule for Heterogeneous Hadoop. It’s one of the schedulers available in Hadoop that schedules jobs based on cluster, workload, and heterogeneity.
- Explain FSCK
File System Check (FSCK) is a command applied to discover inconsistencies and other issues in an HDFS file.
- What is the use of HIVE in a Hadoop ecosystem?
Hive is a data warehouse software solution that reads, writes, and manages large data files stored in HDFS. This is done by converting Hive queries into simplified MapReduce tasks and eliminating the complexity associated with creating and running MapReduce jobs from scratch.
- Name the components of a Hive data model.
The following components are available in the Hive data model.
- Which complex data types does Hive support?
Hive supports the following complex data types.
- What is SerDe in Hive?
SerDe, the abbreviation for Serializer or Deserializer, is an interface that allows a user to read data from a table’s row and write it in a specific field in any format. SerDe has several implementations, including:
- How can one see the structure of a database using MySQL?
This is done by applying the ‘describe’ command as in the syntax below.
Describe table name;
- How can you search for a specific String in the MySQL table column?
To search for a specific String in the MySQL table column, use the regular expression (REGEXP) operator.
The major role of data engineers is to build and implement a complex infrastructure that supports data science and data analytics professionals in collecting, managing, analyzing, and visualizing large data sets. A typical day in the life of a data engineer involves collaborating with stakeholders to understand requirements and developing solutions for processing and analyzing large datasets effectively.