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Engineering Data Profiling and Quality Diagnostics

30 Chapters
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01
Engineering data profiling fundamentals
definitions, objectives, and the role of profiling in data quality diagnostics.
profilingdefinitions
02
Data quality dimensions for engineering datasets
accuracy, completeness, consistency, timeliness, validity, and uniqueness.
dimensionsdatasets
03
Profiling structured data sources
schema discovery, column profiling, and metadata extraction from relational databases.
Profilingstructured
04
Profiling semi-structured and unstructured engineering data
JSON, XML, log files, and sensor streams.
Profilingsemi-structured
05
Statistical profiling techniques
distribution analysis, central tendency, dispersion, and outlier detection.
Statisticalprofiling
06
Data type inference and validation
detecting mismatches, implicit types, and domain constraints.
inferencevalidation
07
Missing data profiling
patterns, mechanisms, and impact on downstream engineering analyses.
profilingpatterns
08
Duplicate detection and record linkage
exact, fuzzy, and probabilistic matching methods.
Duplicatedetection
09
Value pattern analysis
frequency distributions, format checks, and regular expression profiling.
analysisfrequency
10
Cross-column and cross-table profiling
functional dependencies, inclusion dependencies, and relationship discovery.
Cross-columncross-table
11
Time-series data profiling
temporal patterns, seasonality, gaps, and timestamp quality checks.
Time-seriesprofiling
12
Profiling high-volume data
sampling strategies, approximate algorithms, and scalable computation.
Profilinghigh-volume
13
Data quality rule mining
deriving validation rules from profiling results and domain knowledge.
derivingvalidation
14
Anomaly detection in engineering data
statistical, distance-based, and density-based approaches.
detectionstatistical
15
Root cause analysis for data quality issues
tracing defects to source systems and processes.
analysisprocesses
16
Data quality assessment frameworks
scoring, metrics, and dashboards for engineering teams.
assessmentframeworks
17
Profiling data pipelines
monitoring data quality in ETL/ELT workflows and streaming architectures.
Profilingpipelines
18
Data cleaning and remediation strategies
imputation, standardization, and deduplication.
cleaningremediation
19
Case study
profiling and diagnosing quality issues in IoT sensor data.
profilingdiagnosing
20
Case study
data quality diagnostics for manufacturing process data.
diagnosticsmanufacturing
21
Case study
profiling and cleaning clinical trial data for regulatory compliance.
profilingcleaning
22
Case study
data quality assessment in financial market data feeds.
assessmentfinancial
23
Automated data profiling tools
open-source and commercial solutions, features, and limitations.
Automatedprofiling
24
Integrating profiling into data governance and stewardship programs.
Integrating profiling into data governance and stewardship programs.
Integratingprofiling
25
Data quality diagnostics for machine learning
training data validation and drift detection.
diagnosticstraining
26
Privacy and security considerations in data profiling
anonymization, masking, and compliance.
securityconsiderations
27
Scalable architectures for continuous data quality monitoring.
Scalable architectures for continuous data quality monitoring.
Scalablearchitectures
28
Designing data quality reports
visualization, storytelling, and actionable insights.
Designingvisualization
29
Emerging trends
AI-assisted profiling, active metadata, and data observability.
EmergingAI-assisted
30
Capstone project
end-to-end profiling and quality diagnostics for a real-world engineering dataset.
Capstoneend-to-end