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Data Engineering Basics
YOUR PATHWAY TO SUCCESS
This 5-day course provides a foundational understanding of data engineering principles and practices. You’ll explore the data engineering lifecycle, from data ingestion and storage to data transformation and processing.
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Course Duration
5 Days
Course Details
This 5-day course provides a foundational understanding of data engineering principles and practices. You’ll explore the data engineering lifecycle, from data ingestion and storage to data transformation and processing. We’ll cover essential concepts like ETL (Extract, Transform, Load) processes, data warehousing, and working with different data storage systems. You’ll learn how to build data pipelines, automate data workflows, and ensure data quality. This course emphasizes practical application, and you’ll gain hands-on experience working with data engineering tools and technologies.
This course focuses on building practical data engineering skills. You’ll learn how to use tools like Apache Kafka for data streaming, Apache Airflow for workflow management, and cloud-based data warehousing solutions. We’ll also cover best practices for data modeling, data governance, and data security. By the end of this course, you’ll be prepared to tackle real-world data engineering challenges and build robust data pipelines.
By the end of this course, learners will be able to:
- Understand the data engineering lifecycle.
- Design and build data pipelines.
- Work with different data storage systems.
- Automate data workflows.
- Ensure data quality.
- Apply data engineering best practices.
- Data engineers, data scientists, and data analysts.
- Individuals who need to build and manage data infrastructure.
- Anyone who wants to learn about data engineering.
Course Outline
5 days Course
- Introduction to Data Engineering:
- What is data engineering?
- The data engineering lifecycle.
- Data engineering tools and technologies.
- Practical exercise: Exploring data engineering use cases.
- Data Ingestion and Storage:
- Data ingestion methods: Batch and streaming.
- Working with different data formats: CSV, JSON, Parquet.
- Data storage systems: Relational databases, NoSQL databases, data lakes.
- Practical exercise: Ingesting data from different sources.
- ETL (Extract, Transform, Load) Processes:
- Extracting data from various sources.
- Transforming data using different techniques.
- Loading data into target systems.
- Practical exercise: Building an ETL pipeline.
- ETL (Extract, Transform, Load) Processes:
- Data Warehousing and Data Modeling:
- Data warehousing concepts and architectures.
- Data modeling techniques: Star schema, snowflake schema.
- Practical exercise: Designing a data warehouse schema.
- Data Warehousing and Data Modeling:
- Data Pipeline Automation and Data Quality:
- Workflow management tools: Apache Airflow.
- Data pipeline automation.
- Data quality monitoring and validation.
- Practical exercise: Automating a data pipeline and implementing data quality checks.
- Data Pipeline Automation and Data Quality:
