Big Data Engineering Course Description
In today’s world, where data is a key asset, specialists who know how to handle it are worth their weight in gold. Do you want to expand your professional horizons and master the technologies that shape the future of IT?
We offer a comprehensive program developed by expert practitioners that will allow you not only to master advanced technologies but also to significantly increase your competitiveness in the labor market. The Big Data Engineering course by Sigma Software University is a strategic investment in your professional success.
Who Is This Big Data Engineering Course For?
The target audience of Big Data Engineering training covers a wide range of participants with different levels of experience and goals of learning and deepening their knowledge. This course is ideal for:
Students and graduates
Those who have a technical education, graduates and students of Computer Science and related fields and are interested in expanding their competencies in programming for Big Data.
IT professionals
Developers and information technology professionals who want to master and/or deepen their knowledge in the world of Data Engineering: from a thorough understanding of the Data Lifecycle stages to the consideration of specific technologies (databases, cloud services, visualization tools, AI, machine learning), modern techniques for processing and storing and managing data.
Benefits of Big Data Engineering Course by Sigma Software University
By choosing the Big Data Engineering course, you gain several key benefits:
Focus on practical skills
We understand that theory without practice is not enough. That’s why the course is designed in such a way that you get maximum practical experience. You will work with real cases, and use modern Big Data tools and technologies, which will allow you to immediately apply the knowledge gained in your professional activities.
A comprehensive approach to Big Data
The course covers the entire spectrum of knowledge required for successful work with big data: from collection and storage to processing, analysis, and visualization. You will gain a holistic understanding of the Big Data ecosystem and be able to work effectively at any stage of the data lifecycle.
Relevance and advanced technologies
We keep our finger on the pulse of technological innovation. The course has been updated to reflect the latest developments in Big Data. You will learn the technologies that are most in-demand today and shape the future of the industry. You will have the opportunity to get to know Sigma Software better, as well as learn more about our training programs, including Python and Data Science.
During the Big Data Engineering Course, You Will:
- Gain a deep understanding of the concepts and architecture of Big Data systems
- Master tools for efficient processing, storage, and analysis of large amounts of data
- Learn to use Spark, Airflow, Kafka, S3, databases, AWS cloud services, and other key technologies
- Create data processing pipelines and develop analytical solutions for various tasks
- Increase your value as a specialist and open up new career opportunities. You will be able to gain in-depth knowledge to make effective decisions when designing data systems, optimally balancing the functionality and cost of this solution
- Receive a Sigma Software University certificate confirming your professional level in the field of Big Data
Big Data Engineering Course Trainers
Big Data Engineering Course Program
The course includes 16 classes held twice a week (Tuesday and Saturday) for 3-3.5 months. Saturday classes start at 10:00 a.m., and each lesson lasts 2 hours. The training format is online lectures with demonstrations and interactive Q&A sessions.
The course will cover the following topics:
- Theoretical part
- Historical overview and development of Big Data
- General overview of key approaches to designing modern systems (architecture, patterns, approaches)
- Basic methods of data storage, processing, analysis, and visualization
– Storage (HDFS, NoSQL, DBMS, Cloud storages)
– Processing and analysis (Spark, Glue, Lambda)
– Visualization (Notebooks, Grafana)
- Cloud systems (AWS)
- Process orchestration and synchronization (Airflow)
- What is Big Data?
- What problems does Big Data solve?
- Practical aspects of Big Data
- Overview of the Data Engineer position and its responsibilities
- Introduction to the Big Data technology stack
- Homework: Reviewing the training materials
- Basic concepts and glossary in the field of Data Platforms
- Introduction to data pipeline modeling
- Overview of processing tiers and related technologies
- Data storage
- Data processing
- Data ingestion
- Data querying
- Data visualization
- Introduction to cloud concepts and cloud computing (with a focus on AWS)
- PaaS/IaaS/SaaS service models
- Types of data exchange
- Ingestion
- Export
- Replication
- Overview of data sources
- Integrate and prioritize data sources
- Batch data integration
- Data Lakes design concepts
- Types of Pull/Push integration
- Architectural approaches (Lambda, Kappa, Delta)
- Real-time data integration
- Tools for getting streaming data
- Classifications of Data Warehouses
- Introduction to OLTP, OLAP, DWH
- Key principles of HDFS / Object Stores
- General concepts of DBMS
- Overview of NoSQL databases
- Cloud data warehouses
- Comparative analysis of Data Lakes and Data Warehouses
- CAP theorem
- Overview of data formats
- Concepts of partitioning/sharding/replication
- Complete and final data consistency
- Basic concepts of data modeling
- Data modeling within different types of storage
- Introduction to the data schema
- Overview of read/write schemas
- Batching/partitioning strategies
- Data granularity basics
- Stores without schemas
- Handling latency
- Historical context
- Introduction to working with ETL pipelines
- Fundamentals of the Map-Reduce model
- Distributed processing model and data locality
- Overview of the data processing technology stack
- Fundamentals of resource management
- Overview of the main frameworks for data processing
- Concepts of batch data processing
- Developing workflows
- Scheduling
- Orchestration
- Semantics of delivery/processing guarantee
- Concepts of streaming data processing
- Types of pipeline development or synchronization
- Basic AWS cloud services
- Resources for building data platforms
- AWS SDK for implementing data pipelines
- AWS CLI management tools
- Overview of data pipelines in the AWS cloud
- Registration in AWS cloud solutions
- Monitoring in AWS cloud solutions
- Introduction to continuous integration/continuous deployment in AWS
- Forms of data presentation
- Introduction to data visualization
- The concept of data windows, reports, views
- DWH concepts in the field of reporting
- Business intelligence tools
- Automating reports with Data Dashboards
- Data exploration using Notebooks
- Objectives of data regulation
- Data management procedures
- Concepts of data tracking, communication lines, versioning
- Data retention policy
- Concepts of data security/privacy/restricted information
- Tools and solutions for data regulation
- Discussion of architectural ideas
- Final Q&A session
Upon Completion of the Big Data Engineering Course, You Will:
- Understand the main conceptual problems of analyzing, processing, and storing big data
- Be able to analyze and process big data in various formats using various tools
- Be able to use up-to-date software for data storage, analysis, and processing in practical work
Requirements for Participants of the Big Data Engineering course:
- Basic knowledge of programming in one of the languages (Python, Scala, Java, etc.)
- Knowledge of SQL
- Basic knowledge of Linux/Bash operating systems
- Basics of working with Docker
Unlock your potential and new perspectives in Big Data and become a leader in the digital age! Together with Sigma Software University, you will dive into the world of data engineering, time series analysis, and scalable infrastructure development. Gain knowledge and skills that will help you become a highly skilled engineer capable of making informed strategic decisions and influencing the industry’s future!
FAQ
What level of English do I need to study at the Big Data Engineering course?
To make the Big Data Engineering online course as effective and successful as possible for you, the recommended level of English is Intermediate or higher, because, as mentioned above, English terminology will be used a lot in the classroom.
Are there any age restrictions for taking the course?
There are no age restrictions on our course – everyone, regardless of age, can come to the course and get the knowledge he or she needs.
Does the course program include practical classes?
Yes, the course program includes practical classes. You will have the opportunity to apply the knowledge gained in practice.
Can I work as a Big Data engineer after completing the Big Data Engineering course?
The course provides the necessary basic knowledge and practical skills to start a career as a Junior Big Data Engineer or Big Data Analyst. However, for successful employment, we recommend continuing your studies and gaining practical experience.
How much time per week do I need to allocate to studying in the Big Data Engineering course?
We recommend 4-6 hours per week: 4 hours of lectures and 2 hours of independent work.