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Course Outline
Greenplum Architecture
- Parallel processing and symmetric multi-processing
- Segment roles and cluster configuration
- Scalability and data movement
- Greenplum Data Warehouse architecture
Greenplum Table Structures
- Distributed versus randomly assigned tables
- Heap versus append-only tables
- Row versus columnar storage formats
- Partitioned and clustered tables
Data Distribution and Hashing
- Hashing logic and distribution keys
- Skew handling and performance impact
- Hash maps and row placement strategies
Indexes and Performance Optimisation
- Clustered and non-clustered indexes
- B-tree and bitmap index use cases
- Index scan and storage behaviour
Physical Database Design
- Normalisation and logical model design
- User access strategies and distribution analysis
- Data demographics and indexing decisions
Denormalisation Techniques
- Derived data, summary tables, and pre-joins
- Columnar tables as vertical partitioning
- Data marts and materialised views
Advanced SQL and Query Execution
- Join strategies and redistribution
- OLAP and window functions
- Temporary tables, subqueries, and derived tables
EXPLAIN Plans and Query Tuning
- Reading and interpreting EXPLAIN output
- Cost analysis and plan optimisation
- Join movement and segment-local operations
Greenplum Utilities and Best Practices
- ANALYZE and VACUUM
- Data loading and movement with Nexus
- Security, permissions, and performance tips
Summary and Next Steps
Requirements
- A solid understanding of relational databases and SQL
- Experience with data warehousing or analytical systems
- Familiarity with Linux command line operations
Audience
- Data architects and engineers
- Database administrators and technical leads
- BI developers and analytics specialists working with Greenplum
21 Hours
Testimonials (1)
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