Team May 27, 2024 No Comments
You want to become a data engineer? That’s a smart choice because data engineers are in high demand right now.
And they earn impressive salaries. In fact, the average annual salary for a data engineer is ₹11,00,000 in India (Glassdoor).
But how do you break into this promising field? Well, you need a solid foundation. And that starts with an effective, up-to-date data engineering syllabus.
The syllabus is the roadmap that guides you through the essential skills and knowledge you need to land your dream job. Since technology is rapidly evolving, you need a relevant curriculum that focuses on the latest tools, techniques, and best practices.
In this blog post, we will explore the latest data engineering course syllabus. This will help you understand what you need to study and what skills you should develop in 2024 to step into this promising career path.
Data engineering is the field of study that involves building and maintaining data systems that collect, store, and manage data in an organization. This field is a mix of software engineering, database administration, and data science skills.
Data engineers ensure that the right data flows seamlessly from various sources into a centralized location like a data warehouse. This becomes possible because of the designing and implementing of data pipelines that extract, transform, and load (ETL) data from databases, APIs, sensors, and other sources.
Data engineers are also responsible for ensuring the quality and reliability of data. This involves cleaning and validating data, handling missing values, and addressing inconsistencies. They also implement data governance policies to ensure data privacy, security, and compliance with regulations.
Data engineers work closely with data scientists and analysts to understand their needs and design data models that are optimized for analysis and reporting. They may also develop custom tools and applications to streamline data workflows and automate repetitive tasks.
This way, data engineers play a crucial role in helping organizations utilize the power of data and gain a competitive edge in today’s fast-changing market. Now that you understand the basics of data engineering, let’s move on to the next section…
Here is the latest data engineering course syllabus. It is divided into four major sections focusing on four primary topics- SQL, Python, Big Data Processing, and Azure Cloud Engineering.
The following industry-relevant syllabus is strictly followed in the Data Engineering Certification course by Ivy Pro School, which is made in partnership with E&ICT Academy IIT, Guwahati.
If you want to learn data engineering and gain practical skills, you can join the course. It’s a live online program, so you can learn from anywhere. We will talk more about the course later. Let’s see the syllabus first:
Here’s an overview of the SQL for Data Engineering section:
This section of the data engineering syllabus provides students with a comprehensive understanding of SQL, from basic to advanced levels.
It begins with foundational SQL queries, including SELECT statements, filtering, and sorting data. Students also learn to clean and modify data, covering essential operations like updating, transforming, and deleting data while handling errors and validating results.
The course then progresses to more complex topics such as data aggregation, advanced data filtering with pattern matching, and the use of window functions.
Next, students explore working with multiple data tables through various JOIN operations and conditional logic with CASE statements.
Advanced topics include creating and managing databases with DDL statements and developing user-defined functions and stored procedures to automate SQL operations.
Throughout the section, students engage in hands-on exercises and case studies using real-world datasets from industries like eCommerce and retail to apply their SQL skills.
Here’s an overview of what happens in the Python Essentials for Data Engineering section:
The second section of the data engineering syllabus introduces students to Python programming, with a focus on its application in data engineering tasks. Starting with the basics, students learn about Python’s data types, variables, and basic operations.
The course then steps into data structures such as lists, dictionaries, and tuples and shows how to manipulate them using Python’s powerful libraries, particularly Pandas. Students are taught to write and use functions and modules, enabling them to create reusable code.
A significant part of the section is dedicated to data wrangling with Pandas, where learners practice creating, cleaning, transforming, and aggregating data within DataFrames.
Additionally, the course covers API interactions, allowing students to fetch and process data from web APIs and database connectivity using SQLAlchemy to perform CRUD operations.
Error handling and debugging are also emphasized, ensuring students can identify and resolve common issues. And finally, hands-on projects throughout the section help solidify these skills.
Here’s what happens in the third section of the data engineering syllabus:
The Big Data Processing section offers a comprehensive overview of big data technologies and their applications in data engineering. The course begins with an introduction to the fundamental concepts of big data and explores key technologies such as Hadoop, Apache Hive, and Apache Spark.
Then, students learn about the Hadoop ecosystem, including HDFS and MapReduce, and gain practical experience in data storage and processing using Hadoop.
The course then covers Apache Hive, teaching students to query large datasets using HiveQL and apply these skills in hands-on projects.
Apache Spark is introduced next, with a focus on its architecture, RDDs, and DataFrames, and students learn to process data in real-time using Spark. The section also addresses data ingestion and storage techniques, highlighting the use of NoSQL databases like MongoDB.
In the final section, students explore real-time data processing with Kafka and its integration with Spark. They complete practical projects that emphasize building and managing real-time data pipelines.
Here’s what happens in this fourth and final section of the data engineering syllabus:
The Azure Cloud Engineering section provides an in-depth understanding of Microsoft Azure and its application in data engineering.
Students begin with the fundamentals of Azure, including an overview of its services, infrastructure, and security concepts such as Azure Active Directory and role-based access control.
The course covers the creation and management of Azure virtual machines, along with the use of Azure storage services like Blob, Queue, and Table for efficient data storage and retrieval.
Advanced topics include building end-to-end data pipelines with Azure Data Factory, which involves data movement, transformation, and integration, and an introduction to Azure Databricks for collaborative data processing.
Real-time data streaming is also covered, focusing on Azure Event Hubs and its integration with Azure Data Factory.
The section addresses hybrid cloud scenarios, teaches students to manage data workloads across on-premises and multi-cloud environments, and emphasizes governance and compliance standards.
Practical, hands-on projects throughout the section ensure students learn to apply their knowledge in real-world settings.
If you want to become a skilled data engineer, you can join Ivy’s certification course. This course follows the exact same data engineering syllabus as above and is developed in partnership with the prestigious E&ICT Academy IIT Guwahati.
Here is why you should choose Ivy’s Cloud Data Engineering course?
The course helps you become job-ready in just 45 weeks. Interested in learning more? Visit our Data Engineering course page for a detailed syllabus and enrollment information.
Is data engineering more difficult than data science?
It depends on your skills, experience, and strengths. Data engineering requires strong programming skills to build data pipelines, handle large amounts of data, and ensure data quality. Data science requires proficiency in statistics, machine learning, data visualization, and communication to find valuable insights from data and convey them easily. You can research and network with professionals in both domains to gain a better understanding.
Can you be a data engineer without coding?
No, you can’t become a skilled data engineer without strong coding skills. Your job as a data engineer involves building data extraction, transformation, and loading systems, working with data pipelines, managing data, and debugging and troubleshooting data systems. All of these require programming skills. That’s why Ivy Pro’s IIT-certified Data Engineering course teaches all the essential coding languages for data engineers.
Which coding languages are best for data engineers?
Python, SQL, Java, R, and Scala are some of the top programming languages used by data engineers. You will also need proficiency in tools like Apache Spark, Hadoop, and ETL frameworks. No matter which language you use, you will need a good understanding of data structures and algorithms.
Is Python enough for a data engineer?
No, Python is not enough. Python is an essential language for data engineers, and it can help you with data manipulation and data analysis. However, you will also need to learn data warehousing concepts, SQL, big data technologies like Hadoop, Spark, and Hive, and cloud platforms like AWS.
Is Java good for data engineering?
Yes, Java is a good language for data engineering. Since it’s an object-oriented programming language, it helps you write code that is easy to read, reuse, and maintain, helping you easily build complex data systems. Besides, Java has excellent performance, wide adoption, numerous libraries, and a supportive community.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Leave a Reply