Data Engineer

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.

Introduction to Data Engineering

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

Database Design and Management

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.

Data Modeling

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.

ETL (Extract, Transform, Load) Processes

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.

Big Data Processing Technologies

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.

Data Warehousing

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

Data Pipeline Development

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

Data Integration

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

Real-time Data Processing

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

Data Quality and Governance

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).

Cloud Computing and Data Engineering

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

Machine Learning for Data Engineering

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

Data Security and Privacy

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

Performance Optimization

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.

Data Visualization and Reporting

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

Data Engineering in Practice

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

Scalable Data Architectures

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

Data Ethics and Responsible Data Engineering

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

Data Engineering Project Management

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