This topic provides an overview of the field of data engineering, its importance in handling big data, and the role of data engineers in organizations.

Participants will learn how to design and manage databases, including concepts such as schema design, normalization, indexing, and database optimization.

This topic covers the process of transforming real-world entities and relationships into a logical data model, including techniques like entity-relationship modeling and data normalization.

Participants will learn how to extract data from various sources, transform it into a consistent format, and load it into data warehouses or other storage systems.

This topic introduces participants to popular big data processing technologies like Hadoop and Spark, and covers concepts such as MapReduce, data parallelism, and distributed computing.

Participants will learn about data warehousing concepts, including dimensional modeling, star and snowflake schemas, and the use of OLAP (Online Analytical Processing) cubes for reporting and analysis.

This topic focuses on developing efficient data pipelines, covering topics like data ingestion, data transformation, data quality checks, and error handling.

Participants will learn how to integrate data from multiple sources, including structured and unstructured data, and techniques for data cleansing and data deduplication.

This topic covers concepts and technologies for handling real-time data processing, including stream processing frameworks like Apache Kafka and Apache Flink.

Participants will understand the importance of data quality and the role of data governance in ensuring data accuracy, consistency, and compliance with regulations.

This topic explores the use of cloud computing platforms for building scalable and cost-effective data processing systems, including technologies like Amazon Web Services (AWS) and Google Cloud Platform (GCP).

Participants will learn how to leverage machine learning algorithms and techniques for data preprocessing, feature engineering, and data validation.

This topic covers best practices and techniques for securing data infrastructure, implementing data access controls, and ensuring privacy in data handling processes.

Participants will learn techniques for optimizing data processing systems, including query optimization, indexing strategies, and performance monitoring and tuning.

This topic covers tools and techniques for visualizing and reporting data effectively, including dashboard design, data storytelling, and the use of Business Intelligence (BI) tools.

This topic focuses on real-world case studies and scenarios, allowing participants to apply their knowledge and skills to solve practical data engineering problems.

Participants will learn about different data architectures for scalability, including sharding, replication, and distributed file systems like HDFS (Hadoop Distributed File System).

This topic explores ethical considerations in data engineering, including the responsible use of data, avoiding bias, and ensuring data privacy and protection.

Participants will learn project management techniques specific to data engineering projects, including requirements gathering, project planning, and agile methodologies.

This topic covers best practices for data engineering, including code versioning, documentation, code review, and test-driven development.