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Since most companies are now shifting to cloud-based environments, this question lets the interviewer know how prepared you are to WORK in a cloud-based ENVIRONMENT. You should show your preparedness and familiarity with the cloud-based environment along with the pros of cloud computing such as:

• Its flexibility and scalability.
• Security and mobility.
• Risk-free data access from anywhere.

Data Engineering is a demanding career and it takes a  lot of effort to become one. As a data engineer, you must be prepared for data science CHALLENGES that may arise during an interview. Many problems have multi-step solutions, and having them PLANNED AHEAD of time allows you to map out solutions as you go through the interview process. Here, you will not only get information about commonly asked interview questions on data engineering, but you will also ace the interview with your responses.

Useful Resources:

• Big Data Interview Questions
• Python Interview Questions
• Azure Interview Questions
• AWS Interview Questions
• Additional Technical Interview Resources

This is a question that interviewers may ASK to test your SQL expertise. To reduce DUPLICATE data points, you can advise using the SQL keywords DISTINCT & UNIQUE. You should ALSO provide additional approaches, such as utilizing GROUP BY to DEAL with duplicate data items.

This question allows the hiring manager to determine whether the CANDIDATE understands the fundamentals of Python, which is the most COMMONLY used language among DATA engineers. NUMPY, which is used for EFFICIENT processing of arrays of numbers, and pandas, which is useful for statistics and data preparation for machine learning work, should be included in your solution.

With this question, the panel generally wants to know your problem-solving ability and how well you perform under pressure. To answer the question, first, brief them about the situations that lead to the problem. You should TELL them about your role in that situation. For example, if you PLAYED a leading role in solving that problem, that would tell the interviewer about COMPETENCY as a leader. After that tell them about the action you took to solve the problem. To end the answer on a positive NOTE, you should tell them about the CONSEQUENCES of the challenge and the learning you took out of it.

Interviewers SEEK to ANALYZE your decision-making abilities as well as your understanding of VARIOUS tools. As a RESULT, utilize this question to describe why you chose certain tools over others. Tell the interviewer about the tools you USED and why you used them. You can also mention the features and drawbacks of the tool you used. Also, try to use this opportunity to tell the interviewer how you can use the tool for the company’s benefit.

You must expect this question. The interviewer wants to KNOW how much you have researched before applying to this role. While ANSWERING this question, keep your explanation concise on how you would create a plan that works with the company set-up and how you would implement the plan, ensuring that it works by first UNDERSTANDING the company's DATA infrastructure setup. Reading job descriptions and researching the company will help you to TACKLE the question easily.

The INTERVIEWER wants to how much you have invested in this FIELD and whether you are an interested candidate. Mention all your CERTIFICATIONS related to the field in chronological order and briefly explained what you LEARNED to earn that CERTIFICATE.

FIRST, DECIDE which project you'd want to talk about. If you have a real-world example in your field of expertise and an algorithm relevant to the company's work, utilize it to capture the HIRING manager's attention. Maintain a list of all the models and analyses you deployed. Begin with simple models and avoid overcomplicating things. The hiring supervisors want you to describe the outcomes and their significance. There could be follow-up questions like:

• Why did you choose this algorithm?
• What is the SCALABILITY of your MODEL?
• If you were given more time, what could you improve?

The process of confirming the accuracy and quality of DATA is known as data validation. It is implemented by incorporating various CHECKS into a system or report to ensure that input and stored data are logically consistent. Common types of data validation approaches are

• Data type check: It confirms that the data entered is of the correct data type.
• Code check: A code check verifies that a field is chosen from a legitimate list of options or that it corresponds to specific formatting constraints. CHECKING a postal code against a list of valid codes, for EXAMPLE, makes it easier to verify if it is valid.
• Range check: It ensures that input falls in a predefined range.
• Format check: Many data types follow a predefined format. Format check confirms that. For example, a date has formats like DD-MM-YY or MM-DD-YY.
• Consistency check: It confirms that the data entered is logically correct.
• Uniqueness check: It ensures that the same data is not entered multiple times.

IT DEPARTMENTS must maintain many servers and apps, but doing it manually isn't scalable. The more complicated an IT system is, the more difficult it is to keep track of all the MOVING elements. As the requirement to combine numerous automated jobs and their configurations across groups of systems or MACHINES GROWS, so does the demand to combine multiple automated tasks and their configurations across groups of systems or machines. This is where orchestration comes in handy.

The automated configuration, management, and COORDINATION of computer systems, applications, and services are known as orchestration. IT can manage complicated processes and workflows more easily with orchestration. There are many container orchestration platforms available such as Kubernetes and OpenShift.

A data pipeline is a SYSTEM for transporting data from ONE location (the source) to ANOTHER (the destination) (such as a data warehouse). Data is converted and optimized along the journey, and it eventually reaches a state that can be evaluated and used to produce business insights.  The PROCEDURES involved in aggregating, organizing, and transporting data are referred to as a data pipeline. Many of the manual tasks NEEDED in processing and improving continuous data loads are automated by modern data pipelines.

One set of data can be KEPT in several files with various yet compatible schemas with schema evolution. The Parquet data source in Spark can automatically recognize and merge the schema of those files. Without automatic schema merging, the most COMMON method of dealing with schema evolution is to reload PAST data, which is time-consuming.

Since it dramatically reduces total DISC I/O requirements and the quantity of data you need to load from the disc, columnar storage for DATABASE tables is a critical factor in increasing analytic query speed. Each data block STORES values of a single column in MULTIPLE ROWS using columnar storage.

For a spark executor, EVERY spark application has the same fixed heap size and fixed number of CORES. The heap size is regulated by the spark.executor.memory attribute of the –executor-memory flag, which is ALSO known as the Spark executor memory. Each WORKER node will have one executor for each Spark application. The executor memory is a measure of how much memory the application will USE from the worker node.

A query language statement (SQL, Spark SQL, Dataframe operations, etc.) is TRANSLATED into a set of optimized LOGICAL and physical operations by an execution plan. It is a series of actions that will be CARRIED out from the SQL (or Spark SQL) statement to the DAG(DIRECTED Acyclic GRAPH), which will then be sent to Spark Executors.

The *args function ALLOWS users to specify an ordered function for use in the command line, WHEREAS the **kwargs function is USED to express a GROUP of unordered and in-line ARGUMENTS to be passed to a function.

The following are some of Hive's table CREATION FUNCTIONS:

• EXPLODE(ARRAY)
• Explode(map)
• JSON_tuple()
• STACK()

Serializer/Deserializer is popularly known as SerDe. For IO, Hive employs the SerDe protocol. Serialization and DESERIALIZATION are handled by the interface, which also interprets serialization results as SEPARATE fields for processing.

The Deserializer turns a RECORD into a Hive-compatible Java object. The Serializer now turns this Java object into an HDFS-compatible format. The storage role is then TAKEN over by HDFS. Anyone can create their own SerDe for their own data format.

Skewed tables are a type of table in which some VALUES in a COLUMN appear more frequently than others. The distribution is skewed as a result of this. When a table is created in Hive with the SKEWED option, the skewed values are written to separate files, while the REMAINING data are written to ANOTHER file.