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HomeBlogData ScienceHow to Become a Data Engineer in 2024?
Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process.
According to reports by DICE Insights, the job of a Data Engineer is considered the top job in the technology industry in the third quarter of 2020. Companies from startups to the Fortune 500s are looking out for the best and brightest individuals to fill up the role of Data engineers beating out data scientists, cybersecurity analysts, and web developers.
However, several questions may arise for an individual. What is Data Science? What are the roles and responsibilities of a Data Engineer? How does one become a Data engineer, and what skills are required? And many more. Our primary focus in this article will be to answer all these questions and take you a step forward towards your dream of becoming a Data engineer.
Let us first get a clear understanding of why Data Science is important.
If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured. This mainly happened because data that is collected in recent times is vast and the source of collection of such data is varied, for example, data collected from text files, financial documents, multimedia data, sensors, etc. Business Intelligence tools, therefore cannot process this vast spectrum of data alone, hence we need advanced algorithms and analytical tools to gather insights from these data. This is one of the major reasons behind the popularity of data science.
The importance of data science is that it allows an individual to make better decisions by performing predictive analysis and finding significant patterns in the data sets.
It is interesting to note the key things that an individual can achieve with the help of data science:
With the help of various scientific techniques and algorithms which help one to make predictions and map out data-driven solutions, data science is a field that helps an individual to generate inference and insights from structured, semi-structured, and unstructured datasets. It is an outcome of coordination between different statistical tools.
Data Science is the coordination of different statistical tools to determine meaningful inference and insights for better decision making.
Let us go through an example to understand Data Science more deeply. This example will help us understand Data Science more clearly. Let us consider your sleep quality for instance.
The kind of sleep you have had last night is 1 data point for every day.
On day 1, you have had a good sleep for 8 hours, not much movement or sleep awakenings. That is a data point.
On day 2 however, you slept for 7 hours, which is an hour less than the previous day. That is another data point.
By collecting and analyzing these data points for a month, you will be able to gather inferences about your sleeping pattern for that given month. When do you sleep for more than 7 hours, which are the days, weekends or weekdays, when you have undisturbed sleep, etc.
If you continue tracking these data points for over six months or a year, you will be able to gather more information about your sleeping patterns; when do you have short-awakenings at night, when do you sleep the most, how long do you sleep on holidays, etc.
Analyzing more data points will therefore give you a more detailed insight into your study.
The spectrum of sources from which data is collected for the study in Data Science is broad. It comes from numerous sources ranging from surveys, social media platforms, e-commerce websites, browsing searches, etc. These data have been accessible to us because of the advanced and latest technologies which are used in the collection of data. Businesses benefit at large with these data collection and analysis as they allow organizations to make predictions and give insights about products so that they can make informed decisions, backed by inferences from existing data, which, in turn, helps in huge profit returns to such businesses.
Data Engineers are engineers responsible for uncovering trends in data sets and building algorithms and data pipelines to make raw data beneficial for the organization. This job requires a handful of skills, starting from a strong foundation of SQL and programming languages like Python, Java, etc. Apart from that, they are also required to have excellent communication skills to work with other departments and help achieve the goal of the enterprise.
Data Engineers are skilled professionals who lay the foundation of databases and architecture. Using database tools, they create a robust architecture and later implement the process to develop the database from zero.
As a Data Engineer, you must develop Dashboards, reports, and other visualizations and learn how to optimize retrieving data. They are also accountable for communicating data trends.
Let us now look at the three major roles of data engineers. These are as follows:
They are typically responsible for every step of the data processing, starting from managing and making analysis and are usually part of small data-focused teams or small companies. This is considered a nice role for someone who wants to transition from a Data Scientist to a Data engineer.
Pipeline-centric data engineers work with Data Scientists to help use the collected data and mostly belong in midsize companies. They are required to have deep knowledge of distributed systems and computer science.
In bigger organizations, Data engineers mainly focus on data analytics since the data flow in such organizations is huge. Data engineers who focus on databases work with data warehouses and develop different table schemas.
Let us now understand the basic responsibilities of a Data engineer.
The first step towards becoming a Data engineer is understanding the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:
Raw data is unstructured data consisting of texts, images, audio, and videos such as PDFs and voice transcripts. The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data. This process helps convert the unstructured data into structured data, which can easily be collected and interpreted using analytical tools.
Data pipelines refer to the design systems used to capture, clean, transform and route data to different destination systems, which data scientists can later use to analyze and gain information. The data pipelines allow businesses to collect data from millions of users and process the results in real-time. Data scientists and data Analysts depend on data engineers to build these data pipelines.
A data engineer may also perform some of the responsibilities of Data Scientists or Data analysts depending upon the organization's size. They analyze datasets to find trends and patterns and report the results using visualization tools.
The basic responsibility of a Data Engineer is to build algorithms and data pipelines so that everyone in the organization can have access to raw data. To achieve this, understanding the organization's business needs is necessary to build a data ecosystem serving the organization's objectives.
Data engineers are responsible for completing the data. They have no missing values, are cleansed, and set out rules for the outliers.
Some organizations hire Data engineers to develop analytical software to improve data accuracy and enhance customization. They achieve this through a programming language such as Java or C++. However, they are also asked to manipulate data using SaaS tools or build an analytical stack.
Let us now look at the popular companies that hire Data engineers.
In the past, most companies thought that Data Scientists were enough to perform their role and perform the tasks of a Data Engineer. This is one of the major reasons for the shortage in the recruitment of Data Scientists.
However, the volume and speed of data have driven companies to widely recognize both Data engineers and data scientists as two separate, distinct roles.
They are both required in an advanced analytics team of any organization. It is difficult to work in data science without a data Engineer by your side, even though both roles' priority skills and knowledge are different. Knowledge of Python and data visualization tools are common skills for both.
Let us now look at the key differences between a Data Scientist and a Data engineer in a tabular format.
Basis for Comparison | Data Scientist | Data Engineer |
Definition | Generates insights from raw data for bringing information and value using statistical models | Creates APIs and frameworks for consuming data from various sources |
Area of expertise | Requires strong knowledge of mathematics, statistics, computer science, and domain | Requires knowledge of programming, middleware, and hardware |
Work Profile | Develops machine learning models for analysis and builds visualizations and charts | Works as a helping hand for Data Scientists by applying feature transformations for ML models |
Responsibilities | Responsible for the efficient performance of ML models | Responsible for the optimization of the whole data pipeline |
Output | Data products | Data flow, storage, and retrieval systems |
Since the evolution of Data Science, it has helped tackle many real-world challenges. It is in great demand across various industries, allowing business giants to become more intelligent and make better-informed decisions. This is the reason why Data Science and big data analytics are at the cutting edge of every industry.
The top companies that hire data engineers are as follows:
It is the largest e-commerce company in the US founded by Jeff Bezos in 1944 and is hailed as a cloud computing business giant. It was originally a book-selling company, but later it enlarged its branches to different digital sectors. Amazon Web Services, its cloud computing arm, is a multi-billion-dollar platform for cloud-based services for hundreds of thousands of customers all over the world. The average salary of a Data Engineer in Amazon is $109,000.
Founded by Bill Gates and Paul Allen in 1975, it is one of the leading global sellers of software, hardware, gaming systems, and cloud services. They are best known for their chain of operating systems - Microsoft Windows, Microsoft Office, Internet Explorer, and Edge Web Browsers. They are also responsible for developing, manufacturing, licensing support, and selling personal computers and other related accessories. The average salary of a data engineer in Microsoft is $165,000.
Google LLC is a US-based search engine company founded by Sergey Brin and Larry Page in 1998. It was initially an ancillary of Alphabet Inc. It is considered the heart and soul of an Internet user, and this tech giant handles more than 70% of online searches. It has another email, a word processor, software for phones and tablets. The average salary of a Data Engineer at Google is $127,100.
It is a social media platform created originally by Mark Zuckerberg for college students in 2004. You can connect with your friends and family via the internet on this networking website. Out of the many products developed by Facebook, some are Facebook app, Messenger, Facebook Shops, Spark AR Studio, etc. The average salary of a Data Engineer in Facebook is $175,880.
It is a global tech giant founded in 1911 by Charles Flint, originally known as Computing-Tabulating-Recording Company. It is responsible for providing software, hardware, and cloud-based services. Patenting is an important barometer of their continuous innovations for more than 100 years, and it is one of the top companies to receive US patents for the 20th consecutive year. The average salary of a Data Engineer at IBM is $91,000.
Data Science has taken over the corporate world, and every tech enthusiast is eager to learn the top skills to become a Data engineer. It is one of the fastest-growing career fields with a job growth rate of around 650% since 2012 and a median salary range of around $125,000.
Data Science is about combining the appropriate tools to get your task done. It helps you to extract the knowledge from data to answer your question. In layman's terms, it is a powerful tool that businesses and stakeholders use to make better choices and solve real-world problems.
So, as we learn new technologies and more difficult challenges come our way, making our base strong becomes significant. Let us learn in detail about the key skills you need to become a Data engineer in the 21st century.
You can have a lot of options in choosing your field. You can earn a Bachelor's degree in Computer Science and Statistics or even opt for Social Sciences and Physical sciences. The most popular fields of study that will provide you with the skills to become a Data engineer are Mathematics and Statistics (32%), Computer Science (19%), and Engineering (16%).
However, earning a bachelor's degree is not just enough. Most of the Data engineers working in the field enroll themselves in several other training programs to learn an outside skill, such as Hadoop or Big Data querying, alongside their Master's degree and PhDs. So you can do your Master's program in any field like Mathematics, Data Science, or Statistics and allow yourself to learn some extra skills, which will help you easily shift your career to being a Data engineer.
Finally, apart from your academic degree and extra skills, you can also learn to channel your skills practically by taking on small projects such as creating an app, writing blogs, or even exploring data analysis to gather more information.
As a beginner in Data Science, you would be suggested by many to learn machine learning techniques like Regression, Clustering, or SVM without having any basic understanding of the terminologies. This would be a very bad way to start your journey in the field of Data Science since promises of "Build your ML model in just five lines of code" are far-fetched from reality.
The first and the essential skill you need to develop at the beginning of your journey is to gather basic knowledge about the fundamentals of Data Science, Artificial Intelligence, and Machine Learning. To understand the basics, you should focus on the following topics that answer the following questions:
According to a survey by O'Reilly, 40 percent of respondents claim that they use Python as their major programming language. It is considered the most commonly used and most efficient coding language for a Data engineer and Java, Perl, or C/C++.
Python is a versatile programming language and can be used for performing all the tasks of a Data engineer. You can collect a lot of data formats using Python and can easily import SQL tables into your code. Data engineers can also create datasets using Python.
You can refer to the following links to learn about Python:
Amazon Web Services is a renowned cloud platform mostly used by programmers to gain agility and scalability. Data Engineers use the AWS platform to design the flow of data. Also, you need to know about the design and deployment of cloud-based data infrastructure.
You can refer to the following links to learn about AWS:
Kafka is an open-source processing software platform. It is used to handle real-time data feeds and build real-time streaming apps. The applications developed by Kafka can help a data engineer discover and apply trends and react to user needs.
You can refer to the following links to learn about Kafka:
Hadoop is an open-source software library created by the Apache Software Foundation. Hadoop is the second most important skill for a Data engineer.
It is used to distribute big data processing across various computing devices. The Hadoop platform has its own Hadoop Distributed File System (HDFS) to store large data and is also used to stream the data to user applications like MapReduce. Though it does not come under a requirement, having some user experience with software like Hive, Pig, or Hoop is a strong point in your resume. You should make yourself familiar with cloud tools like Amazon S3.
You can refer to the following links to learn about Hadoop:
SQL or Structured Query Language is a programming language that allows a user to store, query, and manipulate data in relational database management systems. You can perform operations like adding, deleting, and extracting data from a database, carrying out analytical functions, and modification of database structures.
NoSQL is a distributed data storage that is becoming increasingly popular. Some of NoSQL examples are Apache River, BaseX, Ignite, Hazelcast, Coherence, etc.
As a Data engineer, you need to be quite proficient in SQL and NoSQL. Learning it will develop a better understanding of relational database systems and boost your profile as a Data engineer.
You can refer to the following links to learn about SQL:
Apache Spark is a processing engine becoming one of the most renowned big data technologies globally. It can easily integrate with Hadoop and work with large and unstructured datasets. However, the only difference between them is that Spark is much faster than Hadoop. Spark stores its computations in its memory while Hadoop reads and writes to disk, making it slower.
Data engineers work with Spark due to its specific design, and it helps to run complex algorithms much faster than other tools. They use it to handle large chunks of complex unstructured datasets and disseminate the data processing. Spark can be used on a single machine or a cluster of machines.
Performing data analytics and distributing computing is a simple task in Spark. The X factor of this software lies in its speed and platform, which allows Data engineers to prevent any loss of data and carry out Data Science projects becomes easier.
You can refer to the following links to learn about SQL:
Although all data science roles don't require deep learning, data engineering skills, or natural language processing, if you want to stand out in a crowd of data engineers, you need to be acquainted with Machine Learning techniques. These include Supervised Machine learning, decision trees, logistic regression, k-nearest neighbors, random forests, ensemble learning, etc.
According to a survey by Kaggle, a small percentage of Data professionals are competent in advanced machine learning skills, including supervised and unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.
You can refer to the following links to learn about Machine Learning:
Curiosity is the desire of an individual to acquire more knowledge, not just about a particular subject but about a wide range of topics and ideas. An intellectually curious person is someone who loves to learn. As a Data Engineer, you are expected to ask many questions.
Curiosity is a trait that you need to develop from the beginning to succeed as a Data engineer. You can cultivate curiosity by relevant books, articles, and blogs about trends in data science. You need to make sense of the vast amount of knowledge hovering around the internet. In the beginning, you might not be able to extract many insights from your collected data. However, you will eventually learn to sift through the data with a curious approach to find patterns in it.
An individual with good communication skills can easily translate their technical insights to a non-technical member, such as a Marketing or Sales department member. A data engineer needs to understand the needs of his/her non-technical fellow workers.
Storytelling around the data is another skill you need to learn as a Data engineer to make it easy for others to understand. It is important as it allows you to properly convey your findings to other team members. For example, sharing information from your data in a storytelling method is much more effective to understand and gather than a simple data table.
According to Burning Glass's Nova Platform report, Data Engineer has been named the top job in the technical domain with an 88.3 percent increase in job postings. Although the demand for Data engineers is high, there is a shortage of qualified data engineers globally.
The salaries of Data engineers depend on several factors like which industry they are working in, how many years of experience they have, what is the organization's size, and so on. However, a big advantage of being a Data engineer is they are always in demand globally. If you get bored of working in a particular city or country, you always have the option of moving somewhere else because of the freedom and flexibility that comes with this role.
Let us look at the highest paying countries and the average annual salary of a Data engineer:
India's average annual Data engineer salary is over ₹830,000.
The average annual Data engineer salary in the USA is around USD 116,591.
Germany's average annual Data engineer salary is around €60,632.
The average annual Data engineer salary in the UK is around £43,725.
The average annual Data engineer salary in Canada is around CAD 80,000.
The average annual Data engineer salary in Australia is over AUD 103,346.
Denmark's average annual Data engineer salary is around DKK 42,321.
The average annual Data engineer salary in Singapore is around SGD 62,648.
According to Glassdoor, Data engineers in India have an average base pay of Rs. 8,56,643 lakhs per annum. A Data engineer in India with experience between 1 – 4 years has net earnings of around ₹7,37,257 per annum. On the other hand, an individual with experience of 5 – 9 years makes up to 1,218,983 per annum, and someone with more experience can earn more than 1,579,282 per annum in India. However, several factors are also associated while deciding the salary of a Data engineer.
Every company, big or small, globally now considers data science as an important sector and looks upon its potential to change the market trends. The decision-making authorities of the companies are focusing more on technology and consumers.
Now, let us understand the significant factors that affect the salary of a Data engineer in India are.
According to a survey by LinkedIn, an entry-level Data engineer with a Master's degree and experience of 1 – 5 years can get an annual salary of around ₹8 Lakhs and earn up to ₹10 Lakhs for a couple of years’ more experience. A senior engineer gets an annual salary of around ₹17 Lakhs or more with experience of 6 – 14 years. However, someone with a specialization in the field can even get a salary of around ₹21 Lakhs or more.
Let's see how experience affects the salary of a Data engineer in India:
Every industry around the world recruits Data Engineers. There has been a significant increase of individuals choosing this career path, which adds a lot of value and enhances the progress of different industries.
In an organization, the Data Engineers are directly responsible for the decision-making process. They achieve this with the help of meaningful information using statistical tools like Power BI, Tableau, and SQL. The progress impacts the salaries of these Data Engineers, which range between $60,000 to $90,000 at their entry level.
Marketing Research Engineers use sales data, customer surveys, and competitor research to optimize their products' targeting and positioning efforts. This industry has a pay scale ranging from $51,490 to $66,000 at the entry level.
Similarly, the Big Data Engineers working in the healthcare industry to maintain the daily administrative advancements and operations get an average annual salary of $45,000 to $70,000.
The highest number of Data Engineers and the average annual data salary in India is the highest in the Silicon Valley of India, a.k.a Bangalore.
Bangalore, Pune, and Gurgaon offer 20%, 9%, and 9% more than the average annual salary in India, respectively. On the other hand, Data engineers working in Mumbai get a salary ranging between ₹3 Lakhs to ₹15 Lakhs per annum, less than the national average. Hyderabad and New Delhi receive 5.6% and 4.1% less than the national average, respectively.
The top recruiters for Data Engineers in India are tech giants like Tata Consultancy Services, Infosys, Accenture, TCS, and IBM. In contrast, according to reports, the salaries offered are highest at Amazon, in the range of ₹5 Lakhs – ₹20 Lakhs per annum.
Skill is an important factor while deciding the salary of a Data engineer in India. You need to go beyond a Master's degree and Ph.D. qualifications and gather more knowledge of the respective languages and software.
Some useful insights on Data Engineering Salaries:
According to a global study by Capgemini, almost half of the global organizations have agreed that the gap between the skilled and the not-so-skilled is not only huge but also widening as years have passed.
With the increase in the application of Machine Learning and Artificial Intelligence, there has been a never-ending demand for skilled IT professionals across the globe. As the demand for data science has emerged, there has been a shortage of skills in this sector, making a huge concern for the tech giants.
As the demand and the supply gap has widened, there have been many opportunities created for data engineers worldwide. Let us see some of the top countries where Data engineers are in high demand.
India is considered the testing ground of most of the applications of Data Science and is expected to have a requirement of around 50% of professionals with data skills.
The ratio of skilled individuals to the jobs available in the Deep Learning field is around 0.53, and for machine learning, the figure stands at 0.63. This shows the demand for professionals with skills in Artificial Intelligence, Machine Learning, and user interface.
The regions in India where data professionals are highest in demand are Mumbai, Pune, Delhi, Bangalore, Chennai, and Hyderabad and the hiring industries include IT, healthcare, e-commerce, retail, etc.
Almost every major tech-savvy place in Europe, from Berlin, to Amsterdam, London, Paris, and Stockholm, have a great demand for data science professionals. The most rigorous technical jobs include Artificial Intelligence, Machine Learning, Deep Learning, Cloud Security, Robotics, and Blockchain technologies. Among the leading digitally driven countries globally, Sweden has the highest demand for Data Science professionals.
The demand for IT skills and the shortage of data science professionals have compelled these countries to fill out vacancies outside their regions. According to a German study, by 2020, European nations will face a shortage of 3 million skilled workers, with an appreciable number of IT professionals.
Canada is one such country that aspires to reach the top position in developing Artificial Intelligence in the global market. They have started investing heavily to create a framework on ethics, policy, and the legal inference of AI.
The topmost demanding data science jobs in Canada are Machine Learning Engineer, Full Stack Developer, and DevOps Engineer. Professionals with experience of around 1 – 5 years can earn $55,000 to $80,000 per annum. Furthermore, an individual with more than five years of experience can earn up to $110,000 or more.
The United Kingdom has a vast demand for Machine Learning skilled professionals, which has nearly tripled in the last five years, reaching around 231%. According to a survey, recruitment specialists in the United Kingdom claim that the demand for Artificial Intelligence skills is growing much faster than in countries like the US, Australia, and Canada.
In 2018, the number of AI vacancies in the United Kingdom was 1300 out of every million. This was double the vacancies produced in Canada and almost 20% more than in the US. Different regions saw different growth rates. For example, in Wales, it rose to 79% and 269% in the Northwest regions in the UK.
China is one of the top countries with a high demand for professionals in the Artificial Intelligence field. They have active participation in this sector and are investing immensely in innovations such as facial-recognition eyewear for police officers, which will help them locate wanted criminals.
Although the demand for AI professionals is high in China, they face an acute shortage due to which the job market is unable to fill up vacant job positions. Data Science professionals who have at least five years of experience in the field are a rare sight, so companies in China are continuously looking for skilled individuals worldwide and are readily active to give much higher average salaries than most countries.
Learning data science skills is how you can overturn your journey in this field. But finding a great job is not that easy, even if you have mastered your skills in Python, R, SQL, or other technical tools. You need to give time, effort and require the proper knowledge to find the right job.
The first step is identifying the different types of jobs you should be looking for.
Let us talk about some of the major roles in the data science world which you can undertake, starting from a Data engineer.
The average salary of a Machine Learning Engineer in the US is $144,800.
All machine learning engineers need to have at least some data science skills and a good, advanced understanding of machine learning techniques.
This title means an individual who can bridge the gap between a data engineer and data science at some companies. In contrast, it might mean a software engineer performing data analysis and turning it into some deployable software at other companies.
An overlap always occurs between a machine learning engineer and a data engineer.
The average salary of a Quantitative Analyst in the US is $130,674.
Big Data Engineers mostly come from software engineering backgrounds. They are close acquaintances of data scientists responsible for designing and building complex data pipelines.
A strong foundation of statistics is essential for them, and almost all data science tools are largely useful. They are experts in coding in programming languages like Python, Java, Scala, C++. They also require experience in Hadoop, Spark, Amazon Web services, etc.
The average salary of a Business Intelligence Engineer in the US is $105,599.
A business intelligence engineer is essentially a data engineer from a data warehousing background whose job is to understand and gather Business requirements and build reporting solutions.
This position requires knowing how to use analytical tools, such as Power BI, Tableau, Relational Data Management Systems, and MicroStrategy. They are responsible for supporting the data warehouses, dashboards, reports, and ETL.
The average salary of a Data Architect in the US is $132,617.
A data architect's job is to closely work with business users to meet business demands. Although it is a sub-category within Data Engineering, SQL and database management skills are crucial for this position.
They mainly belong in the software engineering background or database administration. As a data architect and being a part of the data engineering sector of the business, you will be responsible for developing data architecture and working with Data Engineers to implement the data strategies.
The average salary of a Computer Vision Engineer in the US is $123,852.
Computer Vision Engineers are specialists in Machine Learning and Deep Learning Techniques and have software engineering as their background.
They are a combination of data and machine learning engineers. They are well qualified to use Python, C++, Java, OpenCV, MATLAB, and Spark.
Their major skills include object detection, face recognition, pattern recognition, object tracking, and many more. Usually, A Computer Vision Engineer is expected to have a master's or a Ph.D. in Computer Science.
Data science is the multidisciplinary study of data where mathematics, statistics, and computer science collaborate in a single place. It had emerged as the most sought-after job in the 21st century mainly because of lucrative pay and many job positions.
Let us take a look at the key advantages of data engineering:
According to the latest industry trends, data science is a highly employable and appealing field and claims to create approximately 11.5 million jobs by 2026.
According to IBM, a Data engineer can earn up to $117,000 on an average per annum.
As Data Scientists take the top stage in the decision-making process, the demand for data engineers is also blooming at a high pace, and different kinds of job positions are coming up day by day.
According to StackOverflow's developer surveys, skills required in Data Engineering are among the highest paying skills. According to another survey by Linkedin, there are around 112,500 search results for the search term Data Engineer compared to 70,000 search results for Data Scientist.
According to a report by Business Insider, there will be more than 64 billion IoT devices by the year 2025, from about 10 million in 2018 and 9 billion in 2017. This indicates that Data Engineers are open to numerous ways by which they can pursue their interests and enhance their skills.
As a Data Engineer, you have many options to choose from the most popular data tools, such as Kafka, Hadoop, Spark, MapReduce, Azure, etc. You can have the freedom to choose from what you are working on and what tools you are working with.
One of the most important Python functions that Data Analysts and Data Scientists use is read_csv. The function of this library tool is to read Tabular data stored in a text file which can later be explored and manipulated. This particular tool is one of the central parts of software engineering: creating abstract, broad, efficient, and scalable solutions.
It is the work of Data Engineers to create tools like the read_csv function so that the rest of the team can concentrate on the data analysis part.
Data engineers are responsible for building the systems that allow data scientists to work on data and provide crucial insights to their senior staff members to make better decisions for the organization. Some industries benefiting from this are healthcare, finance, management, banking, and e-commerce.
In addition to all the free resources mentioned earlier, KnowledgeHut consists of various courses by which you can enhance your knowledge in the field of Data Science and help you grab the role of Data engineer in any popular industry.
Let us look at some of the Data Science tutorials offered by KnowledgeHut, along with their key learning points and ratings:
➔ 42 hours of live instructor-led training by certified Python experts
➔ Visualize data using advanced libraries like Pandas, Matplotlib, Scikit
Rating – 4.5
➔ 24 hours of Instructor-led Training with Hands-on Exercises
➔ Analyze and Visualize Data with Python libraries
Rating – 4.5
➔ 50 hours instructor-led training along with 45 hrs Python hands-on
➔ 80 hours of Python assignments with code review by professionals
Rating – 4.5
➔ Your launchpad to a data science career
➔ Get mentored by data science experts
Rating – 4.5
➔ 140 hours of live and interactive sessions by industry experts
➔ Immersive Learning with Guided Hands-on Exercises (Cloud Labs)
Rating – 4.0
➔ Data manipulation, data visualization, and more
➔ 40 hours of live and interactive instructor-led training
Rating – 4.5
➔ Create real-world, intelligent R applications
➔ 50 hours hands-on training from machine learning experts
Rating – 4.5
➔ Become a Deep Learning expert by working on real-life case studies
➔ 40 hours of Instructor-led Training with Hands-on Python
Rating – 4.5
A bachelor's degree in math, data analytics, computer science, or a related field. These degrees will help you learn about different programming languages and systems. Most occupations demand real-world experience in addition to formal schooling. Undergraduates can gain this experience by participating in internships.
Data Analyst or Database administrator gain experience and expertise in data manipulation, processing, and pipeline development, they can progress to roles like junior data engineer, data engineer, and senior data engineer. Further advancement opportunities may include positions such as data architect, data team lead, or even data engineering management roles.
Data engineers commonly utilize a range of tools and technologies to perform their tasks efficiently. These include popular programming languages like Python, SQL for database querying and manipulation, Apache Hadoop for distributed computing, Apache Spark for large-scale data processing, and ETL (Extract, Transform, Load) tools such as Apache Airflow or Apache NiFi.
Data engineering is concerned largely with the creation and upkeep of data infrastructure, pipelines, and systems for effective data storage, processing, and retrieval. Data science analyzes and interprets vast volumes of data in order to extract insights and build prediction models, which is commonly performed using statistical and machine learning approaches. Data analytics is concerned with analyzing data in order to discover patterns, trends, and significant insights for decision-making reasons.
Yes, a lot of businesses in a variety of industries place a high value on data engineering abilities. Massive volumes of data must be managed and processed, and tech giants like Google, Amazon, and Microsoft significantly rely on data engineering.
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