Back

Fundamentals of Data Warehousing and ETL

This introductory course offers a comprehensive overview of the key principles of data warehousing.

Explore courses
Register Now
Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

– This introductory course offers a comprehensive overview of the key principles of data warehousing and the ETL (Extract, Transform, Load) processes, which are crucial for the effective management of extensive data systems.
– Participants will gain insights into the methodologies for collecting, storing, processing, and loading data into data warehouses, facilitating efficient querying and reporting capabilities.
– The course emphasizes the role of ETL pipelines in ensuring the smooth transfer and transformation of data from various source systems into a centralized data warehouse.
– Ideal for individuals aspiring to enter the field of data engineering or those seeking to improve their data management expertise, this course equips learners with essential knowledge and practical skills.
– By the end of the course, participants will be prepared to construct effective data pipelines and manage data storage solutions at an enterprise level.

CERTIFICATION:

  1. Upon successful completion of the course, participants will receive a Certificate in Data Warehousing and ETL Fundamentals, demonstrating their understanding of key concepts and skills needed for working with data storage and processing systems.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

– Comprehend the significance and function of data warehousing within contemporary data architecture frameworks.
– Recognize various data warehousing models and their specific applications in different scenarios.
– Acquire knowledge of the essential phases of the ETL (Extract, Transform, Load) process and their role in enabling effective data integration.
– Establish and oversee ETL pipelines utilizing widely adopted tools and technologies in the industry.
– Apply data transformation methods to refine and structure unprocessed data, ensuring its usability and accuracy.

Enroll Now

Course Curriculum

Introduction to Data Warehousing
  1. What is a data warehouse? Overview of its architecture and components
  2. The importance of data warehousing in business intelligence and analytics
  3. Types of data warehouses: Enterprise Data Warehouses (EDW), Operational Data Stores (ODS).
ETL Process Overview
  1. What is ETL? Introduction to Extract, Transform, and Load
  2. Understanding the ETL workflow and its role in data integration
  3. Key differences between ETL and ELT (Extract, Load, Transform)
  4. Best practices for designing efficient ETL pipelines
Extracting Data (E in ETL)
  1. Data sources: Databases, APIs, flat files, and cloud services
  2. Techniques for data extraction: Full extraction vs. incremental extraction
  3. Handling data from different formats (CSV, JSON, XML)
  4. Tools and technologies for data extraction: SQL, Python, and ETL platforms
Transforming Data (T in ETL)
  1. Data cleaning: Removing duplicates, handling missing values, and data validation
  2. Data transformation techniques: Aggregation, sorting, joining, and filtering
  3. Enriching data: Applying business rules and calculations
  4. Tools for data transformation: SQL, Python, Apache Spark, Talend
Loading Data (L in ETL)
  1. Strategies for loadingdata into data warehouses and data marts
  2. Batch vs. real-time data loading
  3. Managing large volumes of data: Partitioning, indexing, and optimization
  4. ETL tools for loading data: AWS Glue, Microsoft SSIS, Informatica, Apache NiFi.
Data Quality and Governance
  1. Importance of data quality in ETL processes
  2. Ensuring data accuracy, consistency, and reliability
  3. Data governance frameworks: Data lineage, metadata management, and auditing
  4. Security considerations in data extraction, transformation, and storage
Troubleshooting and Optimizing ETL Pipelines
  1. Common ETL challenges: Data duplication, performance issues, and schema changes
  2. Techniques for monitoring and debugging ETL processes
  3. Optimizing ETL performance: Parallel processing, caching, and incremental loading
  4. Best practices for maintaining ETL pipelines

Training Features

Video Tutorials

In-depth explanations of key concepts, tools, and techniques in data warehousing and ETL.

Interactive Lessons

Hands-on exercises and quizzes to reinforce learning and build practical skills.

Downloadable Resources

ETL workflow templates, checklists, and troubleshooting guides.

Live Q&A Sessions

Weekly opportunities to discuss course material with instructors and industry experts.

Practical Projects

Set up and execute an ETL pipeline using real-world datasets.

Certification

A globally recognized certificate upon completing the course.

Get in Touch

    Our Relevant Courses list