Top data engineer interview questions and answers for 2023

Some of the integral skills are:

SQL: Data engineers are in charge of dealing with massive amounts of data. Structured Query Language (SQL) is necessary for relational database management systems to interact with structured data. As a data engineer, you must be proficient in utilizing SQL for simple and sophisticated queries, as well as be able to optimize queries based on your needs.

Data Architecture: Data engineers are in charge of designing and developing complicated database management systems. They are regarded as the guardians of business-relevant data and must design and implement data-processing systems that are safe, secure, and efficient.

Data Warehousing: It is critical for data engineers to understand and operate with data warehouses. Data warehouses enable the collection of large amounts of data from many sources, which may then be processed and analyzed.

Programming Skills: Python and R are the most prominent programming languages utilized in the field of Big Data, hence it is essential to be proficient in at least one of these programming languages.

Data modeling is a strategy for defining and analyzing the data requirements required to support business activities. It entails developing a visual representation of a full data system or a subset of it.

Some ways in which missing values can be handled in Big Data are as follows:

Delete rows or columns with missing values from a table: Rows or columns with missing values in a table can simply be eliminated from the dataset. If more than half of the rows in a column have null values, the column may be removed from the analysis. For rows with missing values in more than half of the columns, a similar strategy can be employed. In circumstances where a high number of values are missing, this strategy may not be very effective.

In a dataset, the columns with missing values and the data type of the column are both numeric: the missing values can be filled in by using the median or mode of the remaining values in the column.

Imputation method for ordinal attributes: If the data in a column can be categorized, the missing values in that column can be replaced with the most often used category. A new category variable can be used to place missing values if more than half of the column values are empty.

Missing value prediction: regression or classification approaches can forecast values depending on the nature of the missing values.

An outlier in a dataset is a value that is abnormally far apart from the other values in a random sampling from a given data collection. It is up to the analyst to establish what constitutes aberrant behavior. Before data points can be labeled as abnormal, the normal observations must first be identified and categorized. Outliers might be generated by measurement variability or a specific experimental error. To avoid any difficulties, outliers must be detected and deleted before further data analysis.

The probability of a discrete result given an input variable is modeled using logistic regression, which is a classification model rather than a regression model. It's a quick and easy way to solve binary and linear classification issues. Logistic regression is a statistical method that is effective with binary classifications but may also be applied to multiclass classifications.

A/B testing is a randomized experiment in which two variations, 'A' and 'B,' are compared. The purpose of this method is to compare a subject's response to variant A to its response to variant B in order to discover which version is more effective in obtaining a specific end.

To offer fast access to data, a distributed cache pools the RAM of numerous computers that are networked together into a single in-memory data storage. The majority of traditional caches are housed in a single physical server or piece of hardware. Distributed caches, on the other hand, expand beyond the memory limits of a single computer by connecting numerous computers and so giving more processing capability. In contexts with high data loads and volumes, distributed caches are beneficial. They enable scaling by adding more computers to the group and allowing the cache to expand in response to demand.

Recommendation engines use a technique called collaborative filtering. Collaborative filtering is a technique for making automatic predictions about a user's tastes based on a collection of information about the interests or preferences of a large number of other users. This method is based on the assumption that if person 1 and person 2 have the same opinion on one subject, then person 1 is more likely to have the same opinion as person 2 on another problem than a random individual. Collaborative filtering, in its broadest sense, is the process of filtering data using procedures that entail collaboration across many data sources and perspectives.

User-defined data types are similar to primitive types in that they are based on the same concepts. However, in the end, they allow users to create their own data structures, such as queues, trees, and linked lists.

NumPy is an open-source data analysis library that includes support for Python's multi-dimensional arrays and matrices. NumPy is a Python library that can perform a wide range of mathematical and statistical operations.