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:
- With more devices used for transactions by customers, tools for acquiring data and analyzing them need to improve.
- The spread of data science would depend on the regulations at the regional level.
- While social networks and interconnected devices lead to a rapid rise in unstructured data, the lack of real-time information could challenge growth.
- Due to advanced technology and greater implementation, North America as a region would be the dominant force in this domain. The other regions in data science include South America, the Middle East, and Europe.
The market segmentation for data science engineering:
By component:
- Data management, data visualization, data discovery, and Big Data Analysis.
- There are two types of services available, managed and professional.
- Professional services include maintenance, support, consulting, integration, and implementation.
By deployment mode:
- There are two segments, cloud and local.
- Cloud can be sub-divided into private, public, and hybrid cloud.
By market organization size:
- Large
- Small and mid-size (SMB)
By business function
- Human Resources
- Finance
- Operations
- Sales and Marketing
By vertical
- BFSI
- Commercial applications
- Healthcare
- Transport and logistics
Who is a Data Engineer? What do they do?
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.
Roles and responsibilities of a Data Engineer
A certified data engineer has 3 major roles to play:
- General: You are part of a team, and you need to manage the entire process, from configuring data sources to integrating analytical tools.
- Warehouse Centric: While data engineers still store data using SQL databases, the warehouses are more dynamic. There could be multiple data engineers working on a single-warehouse architecture. You may work on different storage, integration tools, and Big Data tools.
- Pipeline Centric: You would be managing data integration tools that are the pipeline between different sources and the data warehouse. This could include mere information transfer to more specific tasks.
The responsibilities of a data analytics engineer include:
- Designing Architecture: The platform architecture is designed by data engineers.
- Developing Data-Related Instruments: You will use your programming skills for developing, customizing, and managing warehouses and databases.
- Maintenance and Testing of Data Pipeline: You could be working with the testing team or testing the system for reliability during the development phase.
- Deploying Machine Learning Algorithms: Data scientists design machine learning tools that are deployed into production by data engineers.
- Data and Meta-Data Management: Whether it is meta-data like exploratory data or structured or unstructured data, a data engineer has to manage and store it in a structured way.
- Data-Access Tools: Data engineers set up tools for non-technical users and business analysts to help them access and analyze data.
- Tracking Pipeline Stability: Parts of the pipeline may need monitoring since data or models keep changing.
Key skills a Data Engineer needs
A professional data engineer needs to have the following skills:
- Database tools: Knowledge of structure query language or SQL and NoSQL is important. Experience in data architecture and design is essential.
- Data Transformation Tool: Hevo Data and Matillion are tools that convert raw data into a usable format. The process can be simple or complex.
- Data Warehousing: Helps companies analyze data for their benefit by collecting it from different sources and converting it.
- Data Visualization: It is used by Big Data professionals to understand learnings and insights.
- Cloud Computing: A cloud store helps big data teams easily access stored data. Infrastructure could be a hybrid, in-house, or public type.
- Machine Learning: It helps detect patterns and trends for getting insights from data. Strong mathematics and statistics knowledge is required.
- Real-Time Processing: Effective insights can be generated with real-time data processing with frameworks like Apache Spark.
- Data Buffering: Helps temporary storage of data to ensure faster data processing.
- Data Ingestion: Data ingestion tools like Apache Kafka or Wavefront are required to move data from multiple sources to a single destination. Higher data volumes make data ingestion complex. Prioritization, validation, and dispatching of data ensure faster data movement.
- Data Mining: Vital information can be extracted from large data sets and analyzed with data mining.
Top tools to learn to become a Data Engineer
Here are some tools that you need to know if you want to become a professional data engineer:
Programming languages
While expert-level knowledge is not mandatory, you must have excellent programming skills to code the ETL process and build data pipelines.
Amazon Redshift
This cloud platform is a data warehouse that enables the query and analysis of semi-structured and structured data. It is a relational database.
Apache Kafka:
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.
Hadoop Ecosystem
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.
ELK Stack
You get three open source projects with ELK Stack. They are:
- Elasticsearch: This NoSQL database allows both full-text search and fuzzy matching too. It is designed for storing, searching, and analyzing high data volumes.
- Logstash: Data from any resource can be collected using this data collection pipeline tool.
- Kibana: If charts, maps, and tables need to be analyzed, this data visualization tool is a perfect choice.
Apache Spark
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.
Apache Airflow
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.
NoSQL Databases
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.
SQL Databases
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.
How to become a Data Engineer
If you are wondering how to become a big data engineer, follow these simple steps:
Complete a graduate degree
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.
Big Data, Computer Engineering, and analysis Skills
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.
Additional certifications
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.
Career Opportunities for a Data Engineer
Top career opportunities for a certified data engineer include:
- Data Warehouse Engineer
- BI Developer
- Hadoop Developer
- ETL Developer
Salary of Data Engineers
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:
- More than 70 live sessions
- Industry-relevant curriculum
- More than 7 Govt-data projects
- Top industry-acclaimed data engineering tools used
- Placement assurance and career support
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.
