This article was published as part of the Data Science Blogathon
Introduction
First, we are surrounded by data on a daily basis. Shows us that Software Engineering want an additional category to have data engineering, which is useful on many real-time platforms as data warehousing, transport, etc.
In this article, we will learn concepts like
- The role of data engineering
- Data Engineer Responsibilities
- Data engineering skills
- Other fields related to data engineering
The role of data engineering:
Data engineering is the field associated with analysis and tasks to obtain and store data from other sources.. Later, process that data and convert it into clean data to be used in other processes, as data visualizations, Business analysis, data science solutions, etc.
Data engineering converts Data science More productive. If there is no such field, we will have to spend more time preparing data analysis to solve complex business problems. Therefore, data engineering requires a complete understanding of technologies, the fastest tools and execution of complex data sets with reliability.
The goal of data engineering is to provide an organized standard data flow to enable data-driven models such as ML models, data analysis. The aforementioned data flow can pass through multiple organizations and teams. To achieve the data flow, we use the method called data pipeline. It is the system that has independent programs that perform various operations on the stored data.
Data engineering is responsible for the design, maintenance, extension and build support of data pipelines. Many data engineering teams are creating data platforms. Many organizations can't manage with a single pipeline to save data to a databaseA database is an organized set of information that allows you to store, Manage and retrieve data efficiently. Used in various applications, from enterprise systems to online platforms, Databases can be relational or non-relational. Proper design is critical to optimizing performance and ensuring information integrity, thus facilitating informed decision-making in different contexts.... SQL. Therefore, have many teams with various types of techniques to access the data.
Data Engineer Responsibilities:
Data engineer is a technical person responsible for architecture, construction, data system testing and maintenance. They are responsible for finding recent trends in data sets and creating efficient algorithms to make the data more useful.. They need the necessary skills like programming, maths and computing, experience and also soft skills to communicate data trends that help business growth.
Some of the key responsibilities are:
- Get the data sets required for the problem statement
- Develop, build and maintain architectures
- Align architecture with business requirements
- Develop the dataset process
- Use of programming languages and tools to execute data sets.
- Find the method to improve data reliability and efficiency
- Use large data sets to solve business problems
- Import statistical and machine learning methods
- Made machine learning models as predictive and prescriptive
- Use the necessary data to prepare tasks to be automated
- Deliver the results to stakeholders based on the analysis that has been carried out.
The different types of approaches taken by data engineers are:
Data flow:
We have to get input data in the form of XML data, batches of videos updated every hour, weekly batches of tagged images, etc. Data engineers consume data, design a model that can take that data from various sources, convert and store them.
NormalizationStandardization is a fundamental process in various disciplines, which seeks to establish uniform standards and criteria to improve quality and efficiency. In contexts such as engineering, Education and administration, Standardization makes comparison easier, interoperability and mutual understanding. When implementing standards, cohesion is promoted and resources are optimised, which contributes to sustainable development and the continuous improvement of processes.... and data modeling:
Data normalization involves tasks that make that data more convenient for customers. Includes processes like cleaning the data, remove duplicates and tailor data to a specific data model. Data engineers store normalized data in a relational database or data warehouse. Normalization and data modeling are part of the transformation step of ETL(extract, to transform, load) pipelines. Another way to transform the method is data cleansing.
Data cleansing:
Data cleansing is the process of correcting or removing incorrect data, corrupt, incorrectly formatted, duplicates or incomplete within the data set. If we combine many data sets, there are many problems like doubling, wrong labeling, wrong results, unreliable products.
In this method, we eliminate duplicates or irrelevant observations, we correct structural errors, we filter out unwanted outliers, we handle the missing data and finally give us the effective dataset without any null value.
Data accessibility:
It is one of the important responsibilities of the client side data engineering team. Data accessibility means the ability of the user to access or retrieve data stored in a database or other repository.
Data engineering skills:
Data engineering skills are mostly the same as the skills required for software engineering. In this section, we will see important skills like:
1. Programming languages
2. Databases
3. Cloud engineering
Programming languages:
Data engineers must have a basic understanding of design concepts such as Data structures Y algorithmsand object-oriented programming. The most popular programming language used for data engineering is Python. It is also widely used by machine learning and Artificial intelligence equipment. Scala it is also a popular language like python, which is a functional language that runs on the Java virtual machine (JVM).
Databases:
If we have more data to use, we need some databases that can store that data in a warehouse. Most used database technologies, What SQL Y NoSQL. SQL databases belong to the category of relational database management systems (RDBMS). NoSQL databases are databases that can store non-relational data, as document stores in MongoDB, graphic databases are stored in Neo4j, and so on.
Cloud engineering:
In this technique, we use a method to have independent segments of a pipeline running on separate servers created by a message like Apache KafkaApache Kafka is a distributed messaging platform designed to handle real-time data streams. Originally developed by LinkedIn, Offers high availability and scalability, making it a popular choice for applications that require processing large volumes of data. Kafka allows developers to publish, Subscribe and store event logs, facilitating system integration and real-time analytics..... These systems require many servers and distributed teams generally need to access data frequently.. There are as many private cloud providers as AWS(Amazon web services), Microsoft Azure, Y Google Cloud which are the most popular tools for building and developing distributed systems.
Other fields related to data engineering:
There are some of the fields that are closely related to data engineering as follows:
1) Data science:
Data science is the subset field of data engineering in which data scientists gain insights from various data sets, while data engineers create reusable programs using software engineering techniques. Data scientists use Stats, machine learning algorithms, Piton O R language to explore efficient data that will be reusable, extensive.
2) Machine learning engineering:
Machine learning engineering is the field of use Software Engineering analytical data science skills and insights and create a new efficient machine learning model that is useful to users or consumers of the product. For instance, a ML Engineer can develop a new recommendation algorithm for a company's product, while a data engineer provides the data used to train and test the algorithm created by the ML engineer.
3) Business intelligence:
Business intelligence is the process by which companies use strategies and technologies to analyze data in order to improve Decision making and provide a competitive advantage. Data science focuses on doing forecast and future predictions, while business intelligence focuses on providing insight into the current state of the business. These teams relied on data engineers to build some tools that made them analyze and report relevant data..
Data Engineer Salary:
This professional career gives us the greatest advantage. The average salary of data engineering roles Come in $ 65,000 Y $ 135,000 and it also depends on your educational qualifications, professional certifications, experience (in years) in the relevant field, additional skills, etc.
The annual salary for some of the highest positions, according to the Bureau of Labor Statistics in 2019, so that:
1. Database administrator: 93.750 Dollars
2. Computer network architects: 112.690 Dollars
3. Computer Research Scientists – $ 112,840
According to Glass door, the estimated base salary for data engineers in 2020 it was of $ 102,864 year.
As reported by Indeed.com, data engineers can earn up to $ 129,415 per year with a possible additional bonus of $ 5,000.
As of April 2021, the average salary of a data engineer in the US. UU. Falls between $ 90,000 Y $ 126,133.
Conclution:
Now, you can get an idea about some concepts and the importance of data engineering in real world scenarios. This field is best suited for those who have an interest or an academic background in the fields of computer science and technology. I hope you are excited about the blog. Are you fascinated by data engineering? Let us know your thoughts in the comments!!
Thanks for reading my article!
About the Author:
Vikram Rajkumar – I am currently pursuing my Bachelor of Engineering (BE) in Electronic and Communication Engineering from Sri Krishna College of Engineering and Technology, Coimbatore. I have done projects and internships in the domain of data science and the analyticsAnalytics refers to the process of collecting, Measure and analyze data to gain valuable insights that facilitate decision-making. In various fields, like business, Health and sport, Analytics Can Identify Patterns and Trends, Optimize processes and improve results. The use of advanced tools and statistical techniques is essential to transform data into applicable and strategic knowledge.... business and I have also become interested in data analysis, data visualizations.
LINKEDIN: https://www.linkedin.com/in/vikram-rajkumar-3953a81b0/
GITHUB: https://github.com/Viki183
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