Datawarehousing Training Overview
CURRENCY: 9i & 10g
After presenting entity relationship (ER) and dimensional modelling
(DM) as competing alternatives, the presentation will focus on the
use of DM techniques when developing and implementing a very
large data warehouse. Using real-world business scenarios, SALES, for
example, the instructor will coach the participants from logical
through physical design of a data warehouse involving at least five
dimensions and one-or-more star schemas.
Thus hands-on exercises include:
- Design and creation of dimensions
- Design and creation of star schemas
- Design and creation of hierarchies
- Design and creation of indexes
- Design and creation of integrity constraints
- Extract, Transform and Load (ETL) options
- Parallelism
- Design and create materialized views (summary tables)
Throughout the foregoing, best practices and performance
issues are discussed.
Datawarehousing Training Prerequisites
Completion of our course ‘Data Warehouse Terms, Concepts &
Architecture’ (DWHTCA) or equivalent knowledge.
Datawarehousing Training Course duration
5 Days
Datawarehousing Training Objectives
Upon completion of this presentation, the participant should be able
to monitor and tune large data warehouses in a BI (Business
Intelligence) or DS (Decision Support) environment.
Datawarehousing Training Course outline
1. ORACLE ARCHITECTURAL OVERVIEW
- Processes
- Memory
- Directory
- Dictionary and catalog
- Connectivity
- Replication
- Partitioning
- Database
- Real Application Cluster (RAC)
2. LOGICAL DESIGN CONCEPTS
- Why data modelling
- Requirements analysis
- Normalization vs. denormalization
- Entity relationship modelling
- Dimensional modelling
- OLAP vs. OLTP
- Star vs. snowflake schemas
- Metadata considerations
- Data marts
- Workshop
3. DIMENSIONAL MODELLING DESIGN (1) – INITIAL STEPS
- How to establish business requirements
- How to choose a business process (e.g., sales)
- How to determine the business process grain (i.e., level of
- detail for fact table)
- How to choose dimensions (e.g., time)
- How to identify measurement (numeric facts) to
- populate the fact table
4. DIMENSIONAL MODELLING DESIGN (2) – FACT TABLE
- Definition
- Granularity selection
- Measurements
- Additive vs. non-additive measures
- Foreign keys
- Joins with dimension tables
- Staging
- Workshop, case study
5. DIMENSIONAL MODELLING DESIGN (3) – HIERARCHIES
- Definition
- Types
- Levels
- Level relationships
- Workshop, case study
6. DIMENSIONAL MODELLING DESIGN (4) – INTEGRITY
- CONSTRAINTS
- Scope and purpose
- Unique
- NOT NULL
- FOREIGN KEY
- Enforced vs. not-enforced
- Workshop, case study
7. DIMENSIONAL MODELLING DESIGN (5) – SCHEMA DESIGN
- Star or snowflake
- Data warehouse or data mart
- Naming conventions
- Maintenance requirements
- Workshop, case study
8. PHYSICAL DESIGN (1) – LARGE DATA WAREHOUSE
- CONSIDERATIONS
- The environment (e.g., machine configuration)
- Disk layout and placement (e.g., RAID)
- Table sizes (e.g., maximum size for materialized view)
- Database partition (e.g., how many?)
- Partition key considerations
- Initialisation parameters
- Buffer pools
- Data warehouse loads (e.g., parallelism options)
9. PHYSICAL DESIGN (2) – OBJECTS
- Table spaces
- Tables (partitioned vs. non-partitioned)
- Index options
- Integrity constraints
- Materialized views (i.e., summary tables)
- Creation of dimensions
- Creation of hierarchies
- I/O design considerations (e.g., striping and redundancy)
- Best practices
- Workshop, case study
10. PHYSICAL DESIGN (3) – PARALLELISM
- Definitions
- When to consider (e.g., bulk loads, summaries)
- How to enable parallelism
- Hardware requirements
- Query parallelism
- Partitioned and non-partitioned tables
- Data manipulation
- Types of parallelism (e.g., DML, DDL)
- How parallelism works
- Restrictions
- Best practices
- Workshop, case study
11. PHYSICAL DESIGN (4) – PARTITIONING
- Definition
- Types
- When to consider
- Table compression
- Partition pruning
- Join techniques
- Range partitioning
- Index partitioning
- Best practices
12. PHYSICAL DESIGN (5) – INDEXES
- Bitmap indexes
- B-tree indexes
- Compression
- Global vs. local indexes
- Best practices
- Workshop, case study
13. PHYSICAL DESIGN (6) – INTEGRITY CONSTRAINTS
- Rationale
- Constraint states
- Unique constraints
- Foreign key constraints
- Enforced vs. not-enforced constraints
- Materialized views considerations
- Query rewrite considerations
- Best practices
- Workshop, case study
14. PHYSICAL DESIGN (7) – CREATE DIMENSIONS
- Dimension hierarchical specification
- Integrity constraints
- Dimension validation
- Dimension maintenance (e.g., ALTER)
- Best practices
- Workshop, case study
15. PHYSICAL DESIGN (8) – MATERIALIZED VIEW CREATION &
MAINTENANCE
- Use cases
- Materialized view types
- How to create
- How to refresh
- How to partition
- How to tune
- Logs (e.g., staging options)
- Security considerations
- Query rewrite considerations
16. PHYSICAL DESIGN (9) – ETL
- Options
- Extraction options
- Transformation options
- Loading options
- Change data capture and publishing
17. INTRODUCTION TO ORACLE DATA WAREHOUSING TOOLS
- Oracle Warehouse Builder
- Oracle Discoverer
- Oracle Reports
- OLAP and data mining
18. INTRODUCTION TO ORACLE SQL ADVISOR
- Use
- Tuning materialized views
19. DW PERFORMANCE CONSIDERATIONS
- Query rewrite
- Schema modelling
- Aggregation
- SQL modelling
- EXPLAIN
- I/O design
- Parallelism
- Initialisation parameters
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