- Type
- Final term past paper
- Course
- CS614 Data Warehousing
- Size
- PDF 682 KB
- Updated
- 23 Oct 2026
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- Establish Data Quality Management Environment
- Scope Data Quality Projects & Develop Implementation Plan
- Implement Data Quality Projects (Define, Measure, Analyze, Improve)
- Evaluate Data Quality Management Methods
How to improve Data Quality? (Page#183)
The four categories of Data Quality Improvement
- Process
- System
- Policy & Procedure
- Data Design
Quality Management Maturity Grid: (Page#184)
There are five stages and these are:
Stage 1: Uncertainty Stage 2: Awakening Stage 3: Enlightenment Stage 4: Wisdom Stage 5: Certainty
Parallelize: (Page#188)
Parallel execution improves processing for:
- Large table scans and joins
- Creation of large indexes
- Partitioned index scans
- Bulk inserts, updates, and deletes
- Aggregations and copying
Speed -Up & Amdahl’s Law: (Page#192)
Reveals maximum expected speedup from parallel algorithms given the proportion of task that must be computed sequentially. It gives the speedup S as
Parallelization OLTP Vs. DSS (Page#193)
There is a big difference.
DSS:
- Parallelization of a SINGLE query
OLTP:
- Parallelization of MULTIPLE queries
- Or Batch updates in parallel
Parallel Hardware Architectures consists of:
Symmetric Multi-Processing (SMP) Distributed Memory or Massively Parallel Processing (MPP) Non-uniform Memory Access (NUMA)
Parallel Software Architectures:
Parallel Software Architectures consists of:
Shared Memory Shard Disk Shared Nothing
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