FET Blogs
Table of Contents
Data is the foundation of all data science, AI-driven applications, and any platform driven by digital intelligence. Data Engineering plays a critical role in managing and processing accurate data. It is therefore important for anyone who aspires to build a career in this field to understand the essentials of the data engineering syllabus.
In simple terms, it is an engineering discipline that specialises in designing, building, and maintaining systems and infrastructure to collect, store, process, and transform raw data into meaningful formats. It focuses on the creation of data infrastructure required for analytics, reporting and supporting machine learning applications. As data volumes and complexity increase, the role of data engineering becomes more complex and important.
It serves as a backbone of any data-driven organisation, software platform or business by providing well-structured data and actionable insights.
This aids decision-making among leaders, data analysts and data scientists.
Whether at Indian tech startups or global MNCs, the demand for skilled professionals who can bridge raw data to actionable insights continues to surge.
Digitalisation of processes worldwide has driven sharp growth in data generated across all sectors. Banking, e-commerce, healthcare, and government have moved from physical paper processes to digital data collection and processing. With platforms like Aadhaar and UPI, data engineering underpins national-scale digital initiatives, making the role even more critical.
In the rapidly changing landscape of the digital world, where data volumes are high and scalable systems are essential, establishing the framework and support for analytics is crucial for driving innovation.
A data engineer builds and optimises data pipelines, manages the database and data warehouse, ensures the quality of data, and maintains the integrity and security of the given data. They also work across teams to deliver usable and accessible data sets.
Data engineers create scalable solutions for ingesting and processing information from multiple, rapidly growing data sources to support both batch and real-time analytics. As the volume and complexity of data increase, the role of data engineering becomes more important, especially in sectors such as finance, healthcare, e-commerce, and digital media.
The primary role of a data engineer is to build and manage processes that transform scattered and vast raw data into structured and reliable datasets. These datasets are analysed and used for advanced modelling. In current workspaces, it involves:
The data engineer plays a key role in transforming acquired data into valuable insights for the company. This makes data engineering a key aspect of business in the age of analytics-driven decision-making.
The data engineering course syllabus is usually combined with other related fields, such as data informatics and data science. In some cases, it is also a subject of specialisation. The following is the broad overview of the data engineering syllabus.
Covers core concepts, definitions and the evolution of data engineering. It also highlights the differences between data engineering, data analytics, and data sciences. Students are also familiarised with data lifecycles, right from ingestion to data visualisation.
Involves understanding relational databases such as MySQL, PostgreSQL, and Oracle, along with database schema design and normalisation. It covers NoSQL databases (MongoDB, Cassandra, HBase), their applications, and types such as key-value, document, column, and family databases. Data warehousing using Snowflake, Amazon Redshift, OLAP, data marts, star and snowflake schemas are also included. Distributed file systems: Hadoop HDFS, Amazon S3, and Google Cloud Storage are also a part of the learning.
Involves learning about Batch vs. stream processing paradigms. using tools such as Apache Spark, MapReduce, data parallelism, and real-time stream processing. In-memory computing: Redis, Memcached, and Apache Ignite are also a part of the learning.
Covers the extraction of data from structured/unstructured sources. Transforming, enriching, and aggregating for downstream analytics. Loading into target systems (data warehouses, lakes). Using ETL/ELT tools: Apache NiFi, Talend, Informatica.
Involves conceptual, logical, and physical data models. It includes understanding dimensional modelling for data warehouses, such as star, snowflake schemes. The topics also cover learning about best practices for scalable, flexible data architectures, an introduction to data governance, metadata, and data lineage.
Automation of workflow, including scheduling and dependency management with tools like Apache Airflow, Luigi, AWS Step Functions. While also monitoring pipeline performance and failure handling.
Understanding data profiling, validation, and cleansing strategies. Automated testing frameworks for data quality assurance. Version control for data pipeline code.
Introduction to cloud environments: AWS, Azure, Google Cloud. Managed services: AWS Glue, Google Dataflow, Azure Data Factory. Cloud storage, serverless data processing, and hybrid architectures.
Data encryption (in transit and at rest), masking, and governance. Regulatory compliance (GDPR, HIPAA) and data privacy frameworks. Secure access control and authentication mechanisms.
Designing for high-volume data ingestion and processing. Learning Concepts: horizontal/vertical scalability, partitioning, sharding, and performance tuning. Load balancing and optimisation for distributed systems.
Research projects help deepen understanding in areas such as Banking Digitalisation, E-commerce Analytics, and Healthcare Data Management. Capstone projects involve designing, implementing, and troubleshooting complete data pipelines from start to finish.
Big data and machine learning integration. DataOps practices and automation. Serverless and microservices in the data domain.
The final project involves the implementation of a comprehensive data engineering workflow with a project presentation and peer review.
With a strong foundation in core concepts, the data engineering syllabus also aligns with industry needs. It brings about hands-on tools, aligning with the current ecosystem demands:
The success of a data engineer lies in deep knowledge across several subjects. The subjects become the core areas that create the structure for further learning. They include several programmes such as databases, data structures, algorithms, data warehousing, DataOps and DevOps, and workflow orchestration; each for a specific purpose:
With a composite course, a data engineer gains expertise in both theoretical learning and practical execution of projects. Most data engineering curricula have an integrated live project, hackathons, and internships as part of programme completion. This empowers learners to be ready to face the changing and growing needs of the industry.
Modern data management is a vast area of study that offers many areas of learning and jobs. The role of the Data Engineer becomes essential as a steward of digital transformation. It is foundational in architecting pathways from raw information to actionable intelligence, driving digital innovation. The success of roles is also due to the scalability of data and its worldwide applicability, aligning with the industry's growing needs. Recognising the importance of this field, several educational institutions are developing this area as a specialised programme within their engineering streams.
If designing and orchestrating large data sets is of interest to you, then BTech Artificial Intelligence and Data Engineering from JAIN (Deemed-to-be-University) Faculty of Engineering will help you gain the expertise for your job as a Data Engineer.
A1. In this information-heavy age backed by data, data is a key aspect of any business. Therefore, data engineering is important in managing and transforming available raw data into insights that are usable for decision-making based on facts and also the working efficiency of a company. It makes sure that data is accurate, scalable, secure, and cost-effective to manage, accelerating data analytics and business innovation.
A2. Basic maths is important for the role of a data engineer who uses skills of algebra and statistics on a daily basis. This includes an understanding of concepts such as averages, variance, linear algebra, and probability distribution for data modelling.
A3. AI is aiding data engineers and not replacing them. It is helping engineers in automating repetitive tasks such as data cleaning and anomaly detection so that focus on strategic activities. AI is becoming a strong assistant in improving productivity and accuracy. AI has not replaced human expertise necessary for data architecture and pipeline development.
A4. Because data engineering involves solving complex problems, managing large data sets and ensuring data security - it can become stressful. The stress also varies based on the culture of the workplace, the amount of workload and the coping mechanisms of individuals and the aids given by the company to handle stress. Having a work-life balance, a routine and a realistic workload helps in reducing stress.
A5. The primary roles and responsibilities of a data engineer are:
A6. Transitioning to data engineering from software development is possible by developing skills in SQL, data modelling, cloud platforms, ETL processes, and data pipeline architectures. Software engineering experience provides a strong base for coding, problem-solving, and system design needed in data engineering. Programmes like the BTech Artificial Intelligence and Data Engineering will help boost the prospects.
A7. A typical Data Engineering Syllabus includes building