Informatica Cloud data quality(CDQ)
Participants will gain hands-on experience with the powerful tools and features offered by Informatica CDQ, Informatica Cloud Data Quality (CDQ) training is designed to provide participants with the knowledge and skills necessary to manage and enhance data quality within cloud-based environments. This training covers a range of essential topics, from data profiling and cleansing to implementing data quality rules and monitoring data integrity. Participants will gain hands-on experience with the powerful tools and features offered by Informatica CDQ, enabling them to. This comprehensive training is ideal for data engineers, data quality specialists, and business analysts who are working with cloud data platforms and need to ensure the integrity and accuracy of their organization's data.
Created By: Team InventModel
Requirements
Pre-requisite - We will start from very Basic and will go to advance level, no prior experience needed in any tools.
Job scope - This is one of leading Cloud DQ tool in market, having very huge demand globally. we have multiple openings with us for many of our clients.
demand is very high and supply of resources are very less.
✅ You
will get one Certification from Informatica for sure,
we
will take complete responsibility for this.
✅ You are directly connecting with trainer, so you can connect with me anytime after training completion as well. You can rejoin other batches anytime for revision.
✅ IDMC all important CDQ Services will be covered like MDM, CDI, Data quality and Data profiling etc. integration between CDQ, CDI and MDM SaaS
✅ 200 + Real-time project scenarios will be covered.
✅ 1 Real-time Projects will be covered.
✅ All Basic and Advance topics will be covered.
✅ All topics related to Installation, Development, Testing and Support will be covered.
✅ All materials will have life time access
✅ Separate and dedicated classes for Resume preparation, Interview preparation and Job support.
Trainer Profile - Trainer having 14 years of IT experience including 6 Years onsite Singapore experience.
https://www.linkedin.com/in/sujeet-p-83207161/
LAB Access for Practice - We will instruct you to get LAB access directly from Informatica. Informatica will provide Cloud access for practice.
Course Description
Demo Video:
Demo session - Informatica Cloud Data Quality (CDQ) Training
Course Content:
Chapter 1: Informatica Cloud Services Overview (IDMC and IICS)
● Introduction to Informatica Intelligent Cloud Services (IICS)
● Informatica Cloud Terminology
● Informatica Cloud Architecture
● Informatica Cloud Services
● Runtime Environments
● Connections
● The Administrator Service
● Lab: Defining Connections
Chapter 2: Cloud Data Quality Overview
● What is Data Quality?
● Discuss the Data Quality Management Process Cycle
● List and explain the Dimensions of Data Quality
● Describe Data Quality functions, inputs, and outputs
● Cloud Data Quality Services and Assets
Chapter 3: Cloud Mapping Designer
● Cloud Mapping Designer Overview
● Mapping Designer Terminologies
● Mappings and Mapplets
● Common Transformations
● Lab: Create your training folder
● Lab: Create and run a mapping to load data into a SQL table
Chapter 4: Cloud Data Profiling
● Profile Data
● Review Profiling Results and identify anomalies
● Profile Features
● Lab: Profiling Data
● Lab: Profiling Insights
Chapter 5: Dictionaries
● What are dictionaries and why are they used?
● Creating dictionaries
● Lab: Create a dictionary to standardize data
● Lab: Copy and edit an existing dictionary to validate data
● Lab: Create a dictionary to enhance data
Chapter 6: Rule Specifications
● Introduction to rule specifications
● Building rule specifications
● Lab: Create a rule specification to validate the company field
● Lab: Create a rule specification with multiple rules
Chapter 7: Scorecards
● Scorecard Overview
● Update a profile and define rule occurrences
● Review Scorecards
● Lab: Apply rules to a profile and review
● Identify matching or related records
● Configure the Deduplicate Asset to consolidate matched data
● Lab: Configure a Deduplicate asset to identify duplicate or related records
● Lab: Create a mapping to identify duplicate records
● Lab: Update the deduplicate asset to consolidate matched records
Chapter 8: The Labeler Asset
● Standardization Overview
● Introduction to the Labeler Asset
● Configuring a Labeler Asset in Token Labeler mode
● Configuring a Labeler Asset in Character Labeler mode
● Lab: Create a Labeler to mask nonnumeric data
Chapter 9: The Cleanse Asset
● Introduction to the Cleanse Asset
● Cleanse, standardize and enhance data
● Build a mapping to cleanse and transform data
● Lab: Create a mapplet to cleanse and standardize the Company name
● Lab: Configure a mapplet to derive a Master Contact name
● Lab: Configure a mapplet to remove noise from a numeric field
● Lab: Configure a mapping to cleanse and standardize data
Chapter 10: The Parse Asset
● Introduction to the Parse Asset
● Parsing data
● Lab: Configure the parse asset in prebuilt mode
● Lab: Configure the parse asset using a regular expression
● Lab: Update the Load Mapping to include both datasets
● Lab: Reprofile and standardize the data
Chapter 11: The Deduplicate Asset
● Introduction to the Deduplicate Asset
● Matching Theory
● Identify matching or related records
● Configure the Deduplicate Asset to consolidate matched data
● Lab: Configure a Deduplicate asset to identify duplicate or related records
● Lab: Create a mapping to identify duplicate records
● Lab: Update the deduplicate asset to consolidate matched records
Chapter 12: Verifier Asset
● Introduction to the Verifier Asset
● Verify Address Data
● Lab: Configure the Verifier Asset to verify and correct US master records
