IDQ & CDQ — Informatica Data Quality
Data Quality is the silent crisis at every enterprise — and IDQ / CDQ specialists are the rarest, highest-paid niche in the Informatica ecosystem (₹10-25 LPA at mid-level). In two months you'll master Informatica Data Quality (IDQ on-prem) and Cloud Data Quality (CDQ) — profiling, standardisation, address validation, match-merge, governance, rule libraries, and quality monitoring. Real projects on customer master data, address quality, and dedup workflows — the kind of work that protects regulatory compliance + revenue at every BFSI enterprise.
What you'll learn
- Profile any dataset and identify quality issues systematically
- Build standardisation + parsing rules for names, addresses, numbers
- Implement match-merge identity resolution for customer dedup
- Configure address validation + verification at scale
- Design + deploy reusable rule libraries across the enterprise
- Integrate IDQ with Axon Data Governance + Enterprise Data Catalog
- Embed quality checks into operational systems via REST APIs
- Crack IDQ interviews at BFSI, healthcare, telecom consulting firms
- Land Data Quality Specialist roles paying ₹10-25 LPA
Technologies Taught
Course Unique Features
- Niche specialisation — fewer competitors, premium salaries
- Hands-on with real-world dirty datasets — addresses, names, dedup
- Cover IDQ on-prem + CDQ cloud side-by-side
- Daily 90-minute live class with live data profiling sessions
- Build a rule library you can take to your first IDQ job
- Integration with Axon + EDC — full governance stack
- Trained by Informatica-certified Data Quality consultants
- Resume + LinkedIn polish for Data Quality roles
- Mock interview rounds covering IDQ + data governance patterns
- Direct intros to BFSI + healthcare firms hiring DQ specialists
You Can Work As
Upcoming In-Demand Jobs
Course Curriculum
Informatica Data Quality (IDQ) Training
66 topics
Informatica Data Quality (IDQ) Training
- •Overview of Data Quality concepts
- •Role of IDQ in enterprise data management
- •IDQ on-premise architecture
- •Domain and Node structure
- •Services: Model Repository, Data Integration, Profiling
- •Client Tools: Developer Tool, Administrator Console
- •IDQ vs PowerCenter vs Informatica MDM: Positioning and Use Cases
- •Setting up IDQ Domain and Node
- •Understanding Services and Integration Service Configuration
- •Security: User Roles, Permissions, Repository Access
- •Connectivity setup: Flat Files, RDBMS (Oracle, SQL Server, etc.)
- •Backup and Recovery best practices
- •Connecting to the Repository using Developer Tool
- •Project and Folder creation
- •Overview of objects: Mappings, Mapplets, Profiles, Rules, Scorecards
- •Importing/Exporting objects between environments
- •Version Control and Check-in/Check-out process
- •Column Profiling: Nulls, Distinct values, Patterns
- •Rule Profiling: Apply reusable DQ rules
- •Join Profiling: Discover relationships between datasets
- •Setting thresholds and alerts
- •Interpreting profiling results for DQ insights
- •Creating and configuring scorecards
- •Metrics, filters, dimensions
- •Creating trend reports and visualizations
- •Linking rules to scorecards
- •Monitoring ongoing data quality
- •Standard Transformations: Expression, Filter, Router, Aggregator
- •Lookup, Joiner, Union, Sorter
- •Data Quality-specific Transformations: Parser, Labeler, Tokenizer, Identifier Cleanse, Address Validator, Match, Consolidator
- •Creating mappings using Parser/Labeler Using Cleanse and Lookup for standardization
- •Integrating external AV engines (e.g., AddressDoctor)
- •Configuring AV transformations
- •Handling multi-country address formats
- •Licensing considerations for AV/Name3
- •Reference Table object configuration
- •Using lookup and value replacement
- •Maintaining reference data updates
- •Building dynamic rule logic using reference lists
- •Match Path and Match Columns setup
- •Match Key Generation Techniques
- •Fuzzy vs Exact Matching: Configuration and Tuning
- •Creating and fine-tuning match rules
- •Consolidation and Survivorship logic (basic)
- •Creating exception tables and handling rejected data
- •Filtering and routing bad records
- •Notifying stakeholders through workflow/email
- •Best practices for exception management
- •Exporting IDQ mappings to PowerCenter
- •Parameter file management
- •Using IDQ logic within ETL workflows
- •End-to-end DQ pipeline design (ETL + DQ)
- •Mapplets for rule encapsulation
- •Parameterization techniques
- •Shared folders and cross-project references
- •Rule/Mapplet versioning and migration
- •Object lifecycle: Dev → QA → Prod
- •Export/Import procedures using XML
- •Execution logs: Session log, Transformation log
- •Audit table design
- •Monitoring services via Admin Console
- •Performance tuning tips
- •Customer Master Standardization
- •Product Attribute Cleansing
- •Lead De-duplication across CRMs
- •Implementing an End-to-End Data Quality Flow: Source Ingestion → Profiling → Standardization → Matching → Exceptions → Export
Course Instructed By
A seasoned Data Quality and Integration expert with 10+ years of experience in Informatica IDQ and Cloud Data Quality (CDQ) on IICS. Has led enterprise data quality projects across industries, specializing in profiling, cleansing, matching, and exception handling. Known for practical, real-world training that has mentored hundreds into job-ready IDQ/CDQ developers. Approved trainer by Raj Cloud Technologies.
Approved trainer by Raj Cloud Technologies
Course content
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