Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 35 hours
Course Outline
Core Principles of Data Warehousing
- Objectives, key components, and structural design of warehouses.
- Comparing data marts, enterprise warehouses, and lakehouse architectures.
- Basics of OLTP versus OLAP and the necessity of workload separation.
Dimensional Modelling Techniques
- Understanding facts, dimensions, and data grain.
- Evaluating star schema against snowflake schema structures.
- Managing various types of Slowly Changing Dimensions.
ETL and ELT Workflows
- Methods for extracting data from OLTP systems and APIs.
- Performing transformations, data cleansing, and ensuring conformance.
- Implementing load patterns, orchestration, and managing dependencies.
Data Integrity and Metadata Governance
- Applying data profiling and validation criteria.
- Aligning master and reference data sources.
- Tracking lineage, maintaining catalogs, and documentation.
Analytical Performance and Optimisation
- Concepts of cubing, aggregation, and materialised views.
- Utilising partitioning, clustering, and indexing for analytics.
- Managing workloads, caching strategies, and query optimisation.
Security Frameworks and Governance
- Implementing access controls, role definitions, and row-level security.
- Addressing compliance requirements and audit trails.
- Establishing backup, recovery, and reliability protocols.
Contemporary Architectural Approaches
- Cloud-based warehouses and their elastic capabilities.
- Streaming data ingestion and near real-time analysis.
- Strategies for cost efficiency and system monitoring.
Capstone Project: Source to Star Schema
- Translating business processes into factual and dimensional models.
- Constructing a complete ETL or ELT end-to-end pipeline.
- Deploying dashboards and verifying metric accuracy.
Conclusion and Recommendations for Further Learning
Requirements
- A solid grasp of relational databases and SQL syntax.
- Prior experience in data analysis or reporting activities.
- Foundational knowledge of either cloud-based or on-premises data infrastructure.
Target Audience
- Data analysts moving into warehousing roles.
- Business Intelligence developers and ETL specialists.
- Data architects and technical leads.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 8000 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (2)
A journey through the Spark world: a very intense course. DSL, spark sql, partitioning vs bucketing for me.
Georgiana Elisabeta
Course - Apache Spark Fundamentals
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
