Roadmap for Data Engineering 2025
Table of Contents
- jaro education
- 2, November 2024
- 12:00 pm
With the rise of data-centric decision-making in all fields, the position of a data engineer has become important. Data engineering includes construction of the data pipelines, extensive database management and, most importantly, helping the information move across different systems. This trend includes the uptake of advanced analytics, cloud technologies and real-time data solutions, which explains the rising demand for data engineering specialists.Â
The data engineering roadmap for 2025 provides an overview of the sequential steps to take while mastering basic skills such as data modelling, ETL, big data, and cloud engineering. Thus, whether you are a newbie or an IT specialist wishing to advance your career, this roadmap provides the necessary tools, programming languages, certifications and best practices that will ensure your success in this area.
What Does a Data Engineer Do?
The work and input of data engineers in the entire data ecosystem are significant since they are responsible for creating, implementing, and managing the entire infrastructure enabling any institution or individual to efficiently aggregate, process, and analyse vast amounts of information.Â
The main focus of data engineers is to make sure that the data provided to data scientists or business analysts is readily available, trustworthy, and prepared for analysis. It entails the construction of data pipelines for the purpose of transporting relevant data automatically from respective sources into databases or data warehouses.
Key Responsibilities of a Data Engineer
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- Data Collection and Integration: Engineers’ data collection efforts involve data extraction from multiple sources, APIs, databases, external providers and so forth. This helps in building up efficient data storage pipelines for the data to be ingested into database systems.
- Data Storage and Management: Data storage management is the subsequent stage where data engineers must develop ways to store and manage the collected data. This involves choosing the right database systems (SQL or NoSQL), modifying data structures for the best performance and maintaining consistency of the data.
- Building Data Pipelines: In the processes of data engineering, a great extent of work goes into building solid processes for ETL (Extract, Transform, Load). Such pipelines take the unstructured data and prepare it for analysis with quality assurance at every level.
- Collaboration with Data Teams: Attending to the analytical needs of the data scientists and analysts, data engineers make sure that infrastructure improves their analytics. In such a case, they assist in developing an effective system whereby optimised value is obtained from the raw data available.
- Monitoring and Maintenance: Data engineers deploy data systems and then monitor and evaluate them on dispensing tasks for dependability and effectiveness. If a problem arises, they fix it and look for ways to enhance performance.
- Data Security: Finally, the data storage problem for any sensitive information in any system has to be solved on time. Data protection mechanisms are put in place by data engineers to limit the access of this information to authorised personnel only.
Steps to Become a Data Engineer: Roadmap
This roadmap outlines the key stages and technologies one must follow to succeed in data engineering. Data engineering is a field that involves a lot of learning, and therefore be inquisitive and practice to get the hang of using their tools and techniques. Here are the basic steps you should go through to become a data engineer.Â
Step 1: Foundational Skills Building
It starts with learning the core practices of programming and computer science basics. For example, SQL is required to make any queries to a database. Start with Python, one of the few programming languages oriented on Data Engineering concepts. Python has a simple and easy to learn syntax. Even though the language is a programming language, its capabilities extend to data through libraries such as Pandas and NumPy, which are prospective language users.
Then, you can learn computer science concepts, such as memory, algorithms, and algorithm complexity. The knowledge of how data is built in computer architecture and how transformation and storage occur within it is essential when dealing with more complex levels of data engineering.Â
Another reason is that mastering SQL is very important because it is a database management and manipulation of standard language. Be equipped with the performance of basic functions such as SELECT, JOIN and WHERE for effective retrieval and processing of information.
Step 2: Exploring Different Types of Databases
It’s also important to know the types of databases after learning the fundamentals. Data engineers need to use various storage technologies appropriate to the type of data being processed as well as the existing architecture. Thus, knowing when to choose a relational database versus a NoSQL or data warehousing solution comes in handy. Data handled by MySQL and PostgreSQL is structured in tables and has a fixed schema in which data is inserted in rows and columns. These databases have their schemas defined ahead of time. You must know about the schema design and, more importantly, normalisation practices and means of efficiently querying the relational databases.
NoSQL databases, like MongoDB or Cassandra, where data is unstructured or semi-structured. These types of databases also offer more advantages relative to storing large amounts of data rather than more complex relational databases. Cloud databases, for instance Amazon Redshift and Google BigQuery, provide data warehousing services, allowing storage of a considerable amount of historical data for analysis purposes.
Step 3: Mastering Data Processing
Data engineering is the process of converting raw data into information that one can use effectively. This stage includes attaining a command of ETL and the concepts of batch processing and stream data processing. The key part of ETL refers to the data ingestion process and its movement into either a data warehouse or a data lake, where it is further analysed.
It is essential to know when to apply batch or streaming processing. A batch will be employed for a significant volume of data which is not databases that demand regular updates, while real-time streaming handling will fall under streaming processing. One notable tool is Apache Spark, it makes provision for big data processing and analysis in batch and stream mode processes, while Kafka takes care of the streaming of data in real-time.Â
Knowing these tools will help develop a pipeline that not only processes data but does so quickly and precisely.
Step 4: Cloud Technologies Exploration
With the increase in the volume of data, there is a need for scalable and affordable cloud solutions. Data engineers should have a basic understanding of the common cloud platforms such as AWS and GCP. These platforms include services related to computing, storage, and databases as a service. Knowing how to manage cloud assets, including virtual machines and databases and cloud storage services, allows one to work with data effectively.
Most cloud providers have free tiers or trial offers which enable you to play around with their services and tools. Many companies process their data in the cloud and it is vital to those who will be expected to use cloud services to do provision and configuration of cloud resources. It is also pertinent to understand how such platforms can be used to create and manage data storage and data processing structures.
Step 5: Learning Big Data Technologies
Working with huge databases calls for specific tools, and this step will take you to the world of big data. Hadoop is a popular term that describes a framework that allows storing and processing large amounts of data across many computers. It consists of different building blocks such as HDFS (for storage), YARN (for resource management), and MapReduce (for processing of the data). Understanding how these components interact will help you learn how to deal with a set of data distributed in several machines.
Apache Spark is another important tool, which processes in memory, thus making it speedier than the obsolete Hadoop MapReduce. It has batch and streaming processing allowing real-time data analysis. Understanding the core modules of Spark, including Spark SQL and Spark Streaming, will provide you with the capability of constructing strong data pipelines and processing massive amounts of data.
Step 6: Building Data Pipeline Skills
Out of all the responsibilities a data engineer is expected to perform, developing data pipelines is perhaps the most important. Data pipelines transfer data from sources like APIs, databases, or web-scraped content to storage facilities like data warehouses or lakes. In this step, you will understand how to utilise pipeline orchestration tools such as Apache Airflow, Luigi, and Prefect for workflow automation.
You can’t build a data pipeline without knowledge of ETL. This phase demands practical experience; hence you can do small projects that involve the construction of elementary pipelines. For example, a project can go from data extraction from CSV files through slight alteration to finally inserting the data into an SQLite database.
Step 7: Gaining Practical Experience
The ultimate phase of becoming a data engineer is acquiring practical experience by being involved in actual projects. Begin from simple projects like designing a basic web spider or using Python to preprocess and modify an openly available dataset. These will assist you in honing the most basic skills and help boost confidence levels.
Moving on, turn to intermediate projects for example, building data streaming pipelines or implementing data warehouses in the cloud. You can also create recommendation systems based on the principles of machine learning or process streams of data coming from specific sensors.Â
In the cases of Advanced projects, one gets to deploy an entire machine learning pipeline or create a real time analytic dashboard employing cloud computing and data engineering tools.
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Advanced Skills Required for a Data Engineer
To be a proficient data engineer, one has to acquire some higher level of skills. Some of the data engineer skills are discussed below:Â
- Cloud Computing: Knowledge of working on cloud platforms, such as AWS, Google Cloud Platform (GCP), Microsoft Azure, etc., is very important because most companies have started to shift their infrastructure onto the cloud.Â
- Big Data Technologies: Tools such as Apache Spark, Hadoop, Hive, and Kafka are used for processing large quantities of data.Â
- Data Warehousing Solutions: It would greatly help your skills if you knew how to architect and operate data warehouses inside Amazon Redshift or Google BigQuery.Â
- Data Modeling: It is also important to know how to develop data models that meet the needs of businesses and, at the same time, perform effectively.
- Machine Learning Basics: Not always, but it is helpful to have at least the basics of machine learning when working with data scientists.
Salary Expectations
The data engineer salary varies based on factors such as experience level, location, and industry:
| Post | Work Experience | Salary in INR per Year |
|---|---|---|
| Data Engineer | 0 to 1 year | 5L to 10L |
| 1 to 3 years | 5L to 11L | |
| 4 to 6 years | 9L to 18L | |
| 7 to 9 years | 14L to 25L | |
| 10 to 14 years | 14L to 26.3L | |
| 15+ years | 8L to 25L |
Tools for Data Engineers
Data engineers use a range of tools designed for particular purposes. Mastering all tools is not a requirement, but one should know the basic principles of some core technologies. Below are examples of data engineering tools:
- Databases
SQL is one of the fundamental tools for data engineers. Relational database management systems are based on a structured approach whereby data is organised in tables, and the language used for this is referred to as structured query language. MySQL, PostgreSQL, and Oracle are examples of popular SQL databases.
In contrast, NoSQL databases employ a non-relational approach where data is not restricted to being stored in tables. Several applications are designed for handling this kind of data. For instance, MongoDB, Cassandra and Redis are examples of applications developed for unstructured data storage and processing.
- Data Processing
Looking at contemporary businesses, the need for speed has made organisations embrace real-time data processing. This has led data engineers to build streaming pipelines which allow the processing of data to be instant. Apache Spark is extensively used when analytics and processing of data are required.Â
- Programming Languages
In a way, programming is the solution to any data challenge. Python has become the most preferred programming language for data engineers and other stakeholders in data science analytics due to its simplicity of learning, ease of syntax and vast libraries that can be used for data-related projects.
- Data Migration and Integration
Data migration refers to transporting data while retaining its quality and meaning, and data integration involves data from several systems into a single one to aid in its analysis. Striim is a popular real-time application that provides data migration as well as integration within and between the public and private cloud infrastructure.
- Distributed Systems
To cope with very large amounts of data, there is a need for distributed systems that allow computation and storage of data across several nodes. Hadoop is one of the most widely used platforms that allows data management through a distributed computing environment.
- Data Science and Machine Learning Tools
Data science tools are not a must-have for data engineers, but having an understanding of such core tools enables them to work well with data scientists. Two of the most used open-source machine learning libraries used for model development and deployment are PyTorch and TensorFlow allowing users to perform deep learning using CPUs and GPUs where applicable.Â
How Does Jaro Education Help?
Jaro Education serves as a career counsellor by guiding individuals towards leading higher education courses that align with their chosen career paths. By applying for the following programmes offered by prestigious IITs and IIMs—most of which have Jaro Education as a marketing and technology partner (with the exception of the programme provided by IIT Delhi, where we act as a service partner)—you can take a significant leap forward in your career as a data professional.
For degree programmes, such as the one offered by SSODL, we can assist you with comprehensive career counselling. Additionally, we facilitate networking with alumni through the Jaro Connect Portal! We address all your queries and provide personalised attention to each candidate.
1. Executive Programme in Applied Data Science - IIT Delhi
The Executive Programme in Applied Data Science – IIT Delhi is designed for professionals who aspire to integrate the data science capabilities of the organisation to the next level. Graduate participants acquire advanced analytical skills, machine learning and data visualisation. The program concentrates on the application area and provides participants with a means of working with data and making decisions based on it.Â
| Eligibility Criteria |
2. Professional Certificate Programme in Data Science for Business Decisions - IIM Kozhikode
The Professional Certificate Programme offered by IIM Kozhikode seeks to apply data science in solving business problems. It includes supervised and unsupervised learning, big data tools and techniques, and social media data analytics. The final research project in this course assists students in articulating their concepts in practice, increasing their chances of employment even more.Â
| Eligibility Criteria |
3. PG Certificate Programme in Cyber Security Management and Data Science - IIM Nagpur
The PG Certificate Programme in Cyber Security Management and Data Science – IIM Nagpur is customised for managerial professionals at the middle management level and aspiring executives willing to upgrade their knowledge of cyber security and data science. Participants are taught risk management, incident response and making informed decisions based on data.Â
| Eligibility Criteria |
4. Online Master of Science (Data Science) - Symbiosis School for Online and Digital Learning (SSODL)
The Online MSc in Data Science from SSODL incorporates a broad spectrum of topics, including the statistical underpinnings, the principles of machine learning, and large-scale data technologies. This online form of learning encourages interaction among students and also interacting with professionals employing webinars and workshops. It appeals to busy individuals who want to further their education without restrictions.
| Eligibility Criteria |
Final Takeaways
Data engineering is a useful subsection of data science. It includes the anthropogenic processes necessary for constructing systems that process big amounts of data. Having a vision for how to acquire and perfect one’s data engineering competencies with time – through education, work or even the right level of IT professionalism. Moreover, data engineering, which comes with a good salary and more opportunities in the current era, remains relevant in all sectors of the economy.
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