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Businesses globally generate a lot of data ranging from sales performance to customer feedback, but analyzing and using the data to improve business performance is not easy. This is where data science engineering comes in.
The global data science and engineering market is estimated to grow 17.6%, from $39.5 billion in 2020 to around $87 billion by 2025.
The recent trends in data science engineering are as follows:
The market segmentation for data science engineering:
A certified data engineer designs, maintains and optimizes the data infrastructure required for collecting, managing, transforming, and accessing data. You make raw data useful for data scientists and other consumers of data.
A professional data engineer knows both software engineering and data science. Companies get the data they need to analyze at the right time, helping them make more effective decisions with data engineers.
Whatever the volume of data requests from the data science team, the data analytics engineer can design the required format or structure to process this information.
A certified data engineer has 3 major roles to play:
The responsibilities of a data analytics engineer include:
A professional data engineer needs to have the following skills:
Here are some tools that you need to know if you want to become a professional data engineer:
While expert-level knowledge is not mandatory, you must have excellent programming skills to code the ETL process and build data pipelines.
This cloud platform is a data warehouse that enables the query and analysis of semi-structured and structured data. It is a relational database.
Businesses need to track, analyze and process data in real-time, and Apache Kafka allows you to handle streaming data sets. Some insights have greater value to a business at a particular moment and lose value over time. This makes real-time data processing a vital tool for data engineers.
As the data being handled has become more complex, data storage systems need to be more dynamic to handle Big Data. A complex framework with multiple components to handle different operations is required.
Hadoop is a complex framework, and the components are called the Hadoop Ecosystem. Being an open-source project, it can be used or modified according to the need of the organization.
You get three open source projects with ELK Stack. They are:
This data processing framework requires a higher RAM for in-memory computing but its speed compared to Hadoop makes it a hot favorite among data engineers. Multiple programming languages like Java and Python are supported. It works 100 times faster than Hadoop.
This is one of the top automation tools and has helped companies operate with higher functional efficiency. You can focus more on your core data collection job from several databases since daily tasks get automated with Apache Airflow.
There is a huge demand for uploading an increasing amount of text, images, and videos on social media platforms like Twitter and Instagram. Different types of NoSQL Databases based on documents, graphs, or columns help handle such high volumes of data.
Handling databases or executing queries are core requirements for any data analytics engineer. The structured query language is something that data engineers need for record management, reports, or fetching data. Knowledge of this relational database is a must-have skill for getting into this industry.
If you are wondering how to become a big data engineer, follow these simple steps:
The basic qualification required for becoming a data engineer would be a Bachelor's Degree in Computer or Software Engineering or Computer Science. A foundation in applied math, statistics, and physics would be preferred.
Knowledge of basic programming languages like SQL is vital for the query and analysis of data. You must also understand Python, Hadoop, Spark, and Kafka to enhance your data engineering skills. Keep yourself updated on machine learning and data mining.
To gain a competitive edge, get an additional certification from vendors like Google. Employers prefer certification from recognized global vendors and a google cloud certified professional data engineer gets your CV shortlisted.
Top career opportunities for a certified data engineer include:
The average salary of a professional data engineer in India is INR 8,60,500/-, and the average base salary of a data engineer in the United States is $115,405/- per annum.
With an almost 18% annual growth in the data science engineering industry, the demand for data engineers with the right certification is expected to grow multiple times. There are many career options, from Data Engineers to Hadoop Developers.
There are premium learning platforms like Hero Vired that offer the latest courses on data science and engineering. There are customized programs like the Certificate Program in Data Engineering for aspiring data engineers. These courses cover topics like programming fundamentals like Python, Scala Programming for Spark, SQL, and more.
Work on live projects like Sales Forecasting With Data Engineering, Inflation, and WIP Big Data Engineering, to grasp the core concepts better. All courses are taught by leading faculty from the industry.
The course highlights include:
Some amazing benefits of this course include live instructor-led classes, 570 total learning hours, and a HeroVired Certificate. There is an EMI option to help you enroll for the course without financial hassles.
To learn data engineering, you need to have a bachelor's Degree in a related field, between 1 and 3 years of software development experience, and Python knowledge. Get the best data engineering jobs with an industry-recognized certification from Hero Vired.
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