Data engineer Interview : Before preparing for Data engineer Interview let’s try to find out what is Data engineer and what does Data Engineer Do.
A broad range of technical abilities, including in-depth understanding of SQL database design and several programming languages, are necessary for this IT position. Effective communication is essential for data engineers to collaborate across departments and comprehend the objectives of business leaders regarding the organization’s vast datasets. They are frequently in charge of creating algorithms that allow for the access of raw data as well, but in order to do so, they must comprehend the goals of the organization or client. This is because it is critical to match data strategies with business objectives, particularly when dealing with sizable and intricate databases and datasets.
Systems for large-scale data collection, storage, access, and analytics are designed, constructed, and optimized by data engineers. They design data pipelines that transform unprocessed data into formats that data scientists, applications focused on data, and other data consumers can use. Making data available, secure, and accessible to stakeholders is their main duty.
In addition, data engineers need to be proficient in creating dashboards, reports, and other stakeholder visualizations as well as optimizing data retrieval. They might also be in charge of sharing data trends, depending on the organization. In order to better understand data, larger organizations frequently employ multiple data scientists or analysts, while smaller businesses may depend on a data engineer to fill both positions.
1.State What is Data Modeling
The process of expressing links between data points and structures by developing a visual representation of a whole information system or specific components is known as data modeling. The goal is to demonstrate the various forms of data that are used and kept in the system, along with their relationships, classification and arrangement strategies, formats, and features. Data can be modeled at different levels of abstraction in accordance with the requirements and needs. End users and stakeholders provide information about the business requirements at the start of the process. A concrete database design is then created by converting these business rules into data structures.
Basic question to be prepared for Data engineer Interview
2. What is HDFS
The Hadoop Distributed File System, or HDFS, is a distributed file system made to withstand faults and be used to store and handle massive volumes of data across several commodity hardware nodes. An open-source framework for distributed storage and processing of large datasets on a cluster of computers, HDFS is an essential part of the Apache Hadoop system.
As a fundamental part of the Hadoop ecosystem, HDFS is frequently used with other Hadoop initiatives, including Spark for in-memory processing, Hive for data warehousing, Pig for data flow scripting, and MapReduce for distributed processing. When combined, these parts allow massive clusters of commodity hardware to process enormous volumes of data.
3.Describe NameNode ?
The NameNode is an essential part of the Hadoop Distributed File System (HDFS) and is in charge of overseeing the file system’s namespace and metadata. In the HDFS architecture, it acts as the master server.It is significant to remember that in a typical HDFS architecture, the NameNode is a single point of failure. High Availability (HA) for the NameNode was introduced in Hadoop 2.x to address this issue, enabling the deployment of multiple NameNodes in an active-standby configuration. This guarantees improved fault tolerance and reliability by allowing another NameNode to take over in the event of a failure.
4.Describe Hadoop’s MapReduce feature.
The Hadoop MapReduce processing engine and programming model are intended for massively distributed data processing. It is a fundamental part of the Apache Hadoop framework and offers a fault-tolerant, scalable method of processing enormous volumes of data over a dispersed cluster of computers. The Map phase and the Reduce phase are the two primary stages of the MapReduce paradigm.
Map Phase: Input data is processed by the user-defined map function to produce intermediate key-value pairs. The map function converts the input data, which is usually in the form of key-value pairs, into a set of intermediate key-value pairs.
Reduce Phase: After the intermediate key-value pairs have been sorted and grouped, the user-defined reduce function takes the values associated with each key and applies a particular operation—typically an aggregation. A collection of finished key-value pairs is what the reduce function produces.
5.Describe What is COSHH
Classification and Optimization-based Scheduling for Heterogeneous Hadoop Systems is referred to as COSHH. As the name implies, it offers scheduling that directly affects job completion times at both the cluster and application levels.
6.Tell us about what is Star Schema?
One kind of database schema that’s frequently used in data warehousing is the star schema. In a business intelligence setting, it is intended to arrange data for effective querying and reporting. A central fact table connected to one or more dimension tables makes up the Star Schema. When the structure is visualized, it looks like a star, with the dimension tables encircling the fact table in the middle.
Although the Star Schema is a well-liked option for data warehousing, it’s vital to remember that there are additional schema designs, like the Snowflake and Galaxy Schemas, each with unique benefits and applications. The particular needs, properties, and analytical use cases of the data in the particular business environment all influence the choice of schema.
7.Tell us about what is Snowflake in brief
A Snowflake Schema is a type of database schema design used in data warehousing and databases where several dimension tables are connected to a central fact table to create a structure that resembles a snowflake. With the exception of dimension tables, which are normalized into numerous related tables to create a more complex and normalized structure, this design is an extension of the star schema.
Nevertheless, the snowflake schema has trade-offs. Because more joins are required as a result of the increased normalization, query execution plans may become more complicated. This may have an effect on query performance, particularly in situations requiring a large number of joins.
8. Describe What is Big Data
Large and complicated datasets that are difficult to handle, process, or analyze with conventional data processing tools are referred to as “big data.” The triple Vs of volume, velocity, and variety define the concept of big data, which is frequently extended to encompass veracity, value, and variability.
Big Data is being used extensively in a wide range of industries, such as finance, healthcare, e-commerce, telecommunications, and more, where the capacity to evaluate sizable and varied datasets can spur innovation, better decision-making, and competitive advantages.
9.Describe What is Apache Spark:
An open-source distributed computing platform called Apache Spark is made for handling and analyzing large amounts of data. It offers a productive and adaptable platform for handling massive data sets on multiple computer clusters. Spark is renowned for its quickness, user-friendliness, and ability to handle a wide range of data processing applications, such as graph processing, machine learning, batch processing, and real-time stream processing.
Spark is now a well-liked option for enterprises handling massive data processing and analytics because it provides a strong and adaptable answer for a range of data processing requirements.
10.Describe What is Data Lake
All of an organization’s structured and unstructured data can be kept in one place, at any size, using a data lake. It is made to manage massive amounts of unprocessed, raw data, offering a more adaptable and scalable substitute for conventional data warehouses. The growing amount, velocity, and variety of data produced in the current digital era gave rise to the idea of a data lake.