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In this blog we have created a comprehensive Roadmap for aspiring Data engineers in 2024. It starts with foundational skills like coding (Python, SQL) and databases, then lets you explore deeper into concepts like data processing, cloud technologies, and big data tools like Hadoop and Spark. Finally, it helps you build practical experience through projects and portfolio building to launch your data.
If you find data interesting and want a job in technology, being a data engineer could be just right for you. It's a job that's both challenging and exciting, and it's not going away anytime soon and there is high demand for data engineers. But if you're wondering how to start, it's okay! We will try to answer all your questions through this blog.
This article will provide a complete roadmap for data engineering exploring what data engineers do? Their salary, job outlook and how to become one with a list of resources. Let's begin to learn more about data engineering .
Data engineering is about making information easy to understand. Data engineers use various skills like programming and setting up systems to do this. They do tasks like building data pipelines, using predictive models, and cleaning up data every day. It's an exciting job where you work with different tools and technologies related to data. It's connected to machine learning, and you create and deploy data and machine learning pipelines. The job is not boring, pays well, secure and will be in high demand because businesses will always have data and need data engineers.
Data engineers are often involved in the following tasks:
Data engineers are in high demand across different industries, including finance, healthcare, retail, and technology.
There are these three roles in the data world: data scientists, data analysts, and data engineers. People often get confused between them and think of them as one. Each role has its specialty, but are they really different? Let's find out
Data scientists specialize in using statistical and machine learning techniques to analyze large sets of data. They aim to uncover meaningful patterns and insights that can be used for making informed decisions.
Data analysts focus on gathering, cleaning, and analyzing data to help businesses make well-informed choices. They use their mathematical and business knowledge to interpret data and provide valuable insights for strategic planning and operational improvement.
Data engineers are experts in building and maintaining the infrastructure needed for data collection, storage, and analysis. They apply their skills in computer science and engineering to design, implement, and manage data pipelines and systems, ensuring a smooth and accessible flow of data for data scientists and analysts.
A lot of people are attracted to jobs that pay well, and that's a big reason why many people are interested in becoming data engineers. Since data engineers usually earn a lot of money, it's clear why this job is becoming more popular and desirable.
According to Glassdoor, total pay for data engineers in India ranges from ₹590K-₹2M/yr with a base pay ₹540K-₹2M/yr and additional pay ₹50K-₹200K/yr.
Top companies like TCS, Accenture, IBM, cognizant, and many more specially in India are keen to hire a data engineer.
A Data Engineer possesses a valuable skill set. If a Data Engineer is considering a career transition, there are several potential paths they could explore, depending on their interests and goals. Here are some possible career options:
1. Data Scientist: Data Engineers often work closely with Data Scientists. Transitioning to a Data Scientist role could involve gaining skills in statistical analysis, machine learning, and data modeling.
Salary: The average salary for Data Scientist is ₹13,50,000 per year in the India
2. Machine Learning Engineer: If you’re interested in the implementation of machine learning models and algorithms, becoming a Machine Learning Engineer could be a suitable option. This might involve gaining expertise in machine learning frameworks and algorithms.
Salary: The average salary for a Machine Learning Engineer is ₹12,49,145 per year.
3. Data Architect: Transitioning to a Data Architect role involves a deeper focus on designing and implementing data systems. Data Architects are responsible for creating blueprints that ensure data is available, accessible, and secure.
Salary: The average salary for a data architect in India is around ₹25L/year.
4. Cloud Solutions Architect: Data engineers work with different cloud technologies, becoming a Cloud Solutions Architect could be a natural progression. This role involves designing and implementing scalable and secure cloud infrastructure for data storage and processing.
Salary: The average salary for a cloud solutions architect in India ranges from ₹8L - ₹30L/yr.
5. Big Data Engineer: Specializing further in big data technologies can lead to a career as a Big Data Engineer. This involves working with large datasets and distributed computing frameworks like Apache Hadoop and Apache Spark.
Salary: The average salary for a Big Data Engineer ranges between ₹5L - ₹13L/yr.
6. Software Engineer: Leveraging programming and software development skills gained as a Data Engineer, transitioning to a more general Software Engineer role is a possibility. This could involve working on a variety of software projects beyond data-focused applications.
Salary: In India average salary for Software Engineer ranges between ₹5L - ₹12L/yr
7. Product Manager (Data Products): With a strong understanding of data and its applications, transitioning to a Product Manager role focused on data products could be an option. This involves defining product strategy, features, and working with cross-functional teams.
Salary: The average salary for Data Product Manager is ₹20L - ₹27L/yr
Source: Glassdoor
We've crafted a comprehensive roadmap to guide you in becoming a data engineer in 2024. The step-by-step plan spans from January to December. By following these carefully outlined steps, you can develop the skills needed to become a proficient data engineer by the end of the year.
The first step in building foundational knowledge is getting Proficient in coding languages, so consider taking courses to learn and practice your skills. Common programming languages include SQL, NoSQL, Python, Java, R and scala.
Begin with Python and focus on getting really good at it because it's great for beginners. Think of Python like your starting toolkit.
If you want complete roadmap to learn python check our blog
How to learn Python for Data Science in 2024
Next up is SQL – it's like the language databases speak. Learn how to query and manipulate data using SQL. So, Python is like your primary tool, and SQL is your data language tool. Both are essential skills to start your journey as a data engineer.
If you want complete roadmap to learn SQL check our blog
As a data engineer you’ll be working with many databases so it’s important to familiarize yourself with these database categories and specific tools within each, this will give you a strong foundation for handling different types of data in various scenarios. Let's break down each category of databases and data warehousing in brief:
In data engineering, relational databases are often used to store structured data, and SQL (Structured Query Language) is commonly employed to manipulate and query the data. Data engineers design and maintain the database schema, optimize queries, and ensure data integrity.
NoSQL databases are used when dealing with large volumes of unstructured or semi-structured data. They are often used in big data applications, real-time analytics, and scenarios where the schema is dynamic. Data engineers design data models, handle data ingestion, and optimize queries for NoSQL databases.
Data warehousing solutions are designed for storing and analyzing large volumes of data for business intelligence and analytics purposes. Data engineers design ETL (Extract, Transform, Load) processes to move and transform data into the data warehouse. They also optimize data storage, implement data partitioning, and ensure data quality for efficient analytics.
After working with databases it's time to understand data preprocessing. This month, you should focus on learning the tools which are used to process data.
While you are learning about data preprocessing, also explore the ETL concept. ETL stands for Extracting data from a source, Transforming it into the needed format, and Loading it to a specific place. Also Learn about ETL tools such as Apache NiFi, Talend, or Apache Spark and Data Engineers use ETL in every project!
In data processing, there are two main approaches: batch and streaming. For batch processing, Apache Spark is a great tool to learn. It helps handle large chunks of data all at once. On the other hand, for streaming processes where data comes in continuously, you can explore tools like Apache Kafka, Apache Flink, and Apache Storm. These tools are handy for dealing with data in real-time, making them essential for various projects in the field.
From this month you should start exploring cloud technologies by mastering platforms like AWS, Azure, or Google Cloud. Once you're familiar, move on to Data Lakes, which are storage solutions for diverse data types. Learn how to set up and manage them using services like AWS S3, Azure Data Lake Storage, or Google Cloud Storage. It's about efficiently handling and organizing data in the cloud.
Data Lakes:
During July and August, focus on learning Big Data Technologies.
In July, start with the Hadoop ecosystem. Learn about HDFS, a robust file storage system for large datasets across distributed clusters. Get acquainted with tools like Hadoop MapReduce for parallel processing and Hive, a data warehousing and SQL-like query language for Hadoop.
In August, focus on Apache Spark—a potent framework for distributed data processing. Explore its versatility in handling both batch and real-time data tasks, gaining skills crucial for modern big data analytics and effective data engineering.
From September to October, focus on building skills for Data Pipelines. Learn how to bring in data smoothly (Data Ingestion), shape and prep it for analysis (Data Transformation), and automate the workflow for efficiency. These skills are essential for creating effective and streamlined processes to handle data.
Now that you've built valuable skills, it's time to gain practical experience. To do this, engage in hands-on projects and create a portfolio. You can start with the following projects or develop your own.
Congratulations on completing the roadmap to becoming a Data Engineer! You've learned important skills, explored various tools, and are now ready to tackle real projects. With hands-on experience and a portfolio, you're set to make a mark in the world of data.
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