A data engineer is a person who has expertise in the design, development, operation, and maintenance of systems that collect, store, analyze, or otherwise process data. They are responsible for making sure data is stored and analyzed in a way that is useful to people. This includes both large-scale data storage and analysis as well as more specialized tasks such as creating data pipelines that can be used by different types of tools.
The job title is often seen as one that requires some type of mathematical knowledge, but this is not always the case. Data engineers can work on projects with very little programming experience, and the tasks they perform vary depending on the company and the team they work on. Some data engineers work with big data tools such as Hadoop and Spark while others use SQL queries to analyze large amounts of data. There are also plenty of opportunities for data engineers to work on projects that involve working with data in a more traditional sense.
The employment of data engineers is projected to grow 13 percent from 2016 to 2026, which is much faster than the average for all occupations. Earnings vary based on the amount of experience you have and the specific industry you’re in. Some most asked python related interview questions for data engineer are:
1. Which Python libraries are most efficient for data processing?
The most efficient Python libraries for data processing are NumPy and Pandas.
2. What is data smoothing? How do you do it?
Data smoothing is a technique used to make the data look more consistent and smooth. It is done by averaging the data points over a certain period of time.
3. What are the differences between OLTP and OLAP? OLTP and OLAP are two different types of data processing.
OLTP is typically used for operational data, such as sales data, while OLAP is used for analytical data, such as customer data. For example, a bank may use an OLTP system to track transactions that have occurred, such as deposits and withdrawals. The bank would use an OLAP system to analyze customers and their deposit history. As another example, a company may use an OLTP system to track sales by region, while the company may use an OLAP system to analyze sales by product and store. In some cases, a single system may be used for both OLTP and OLAP. In other cases, separate systems may be used for each type of data processing.
4. Differentiate between structured and unstructured data.
Structured data is data that is organized in a way that makes it easy to find and use. Unstructured data is data that is not organized in a way that makes it easy to find and use.
5. How do you perform web scraping in Python?
There are a few different ways to perform web scraping in Python. One way is to use the urllib library. This library provides a number of functions that allow you to access the contents of web pages. Another way is to use the Requests library.
- Access webpage using request library and URL
- Extract tables and information using BeautifulSoup
- Convert it into the structure for using Pandas
- Clean it using Pandas and Numpy
- Save the data in the form of a CSV file
6. How does Network File System (NFS) differ from Hadoop Distributed File System (HDFS)?
NFS and HDFS are both file system types used in distributed computing. NFS is a protocol used for sharing files between computers. HDFS is an open-source distributed file system designed at Google. NFS and HDFS are similar in many ways, but there are some important differences.
7. What is meant by logistic regression?
Logistic regression is a machine learning technique that uses a linear model to predict the probability of an event.
8. Briefly define the Star Schema in data science.
The Star Schema is a data science visualization tool that allows users to explore and analyze data in a tripartite way. The first part of the schema is the data itself, which is represented by stars.
9. What do you mean by collaborative filtering?
Collaborative filtering is a data science technique that uses a group of users to filter data. The group of users is called a collaborative filter.
10. What are some primitive data structures in Python? What are some user-defined data structures?
A list, dictionaries, sets, and tuples are some primitive data structures in Python. User-defined data structures can also be created using classes.
11. What are some biases that can happen while sampling?
There are a few biases that can happen while sampling. One is the sampling frame bias. This is where the researcher excludes certain groups from being included in their sample, which can lead to inaccurate results. Another is the selection bias. Sampling can be biased if the sample is not representative of the population. Additionally, sampling can be biased if the sample is not random.
12. List some of the essential features of Hadoop.
Hadoop is a distributed computing environment for large data sets. It provides a MapReduce framework for processing large data sets using parallel processing techniques. A MapReduce framework is a programming model for scalable, fault-tolerant, and high-performance data processing. It uses parallelism to process large amounts of data. It has two phases, Map and Reduce. In the map phase, a mapper program takes input from the key-value pairs in a data set and produces key/value pairs for each key. The reducer then combines the outputs from all of the mappers.
13. What is an ndarray in NumPy?
The NumPy library has a number of data structures for holding numerical data. One of them is the ndarray, which can be thought of as a matrix of numbers. This data structure is similar to Python lists in that it allows you to manipulate individual elements of the array, and to index into the array using row or column coordinates. In addition, ndarrays support operations that are not supported by regular arrays (like slicing and concatenation).
14. What is the difference between append and extend in Python?
Append extends a list, while extend adds an element at the end of a list.
15. What are Python namespaces?
A namespace is a specific location in a Python program where a specific set of variables and functions are defined. Namespaces are useful for organizing code and making it more readable.