Training Overview
Understand the fundamentals of Informatica Intelligent Cloud Data Quality, including the Cloud Architecture & GUI, Data Quality Assets/Transformations, & Cloud Mapping Designer. This training enables professionals to design & build Data Quality Cloud Processes to use in Data Migration, Data Integration, or Data Quality Projects. This training applies to Release 40.
Audience Profile
- Developer
- Business User
Learning Objectives
After completing this training, professionals should be able to:
- Describe Informatica Cloud Architecture
- Download & install Secure Agent
- Describe what Cloud Data Quality is & how it can be used
- Using Cloud Administrator to define connections
- Creating Mappings using Cloud Mapping Designer
- Profiling data to identify anomalies
- Creating Dictionaries to hold reference data for verification & standardization routines
- Using Rule Specifications to build rules to identify bad data
- Identify & labeling data in fields using a Labeler Asset
- Configuring the Cleanse Asset to cleanse bad data identified during profiling
- Configure the Parse Asset to parse data
- Use the Deduplicate functionality to identify & consolidate duplicate records
- Verify & enhance Addresses using the Verify Asset
Content Outline
- Introduction to Informatica Intelligent Cloud Services (IICS)
- Informatica Cloud Terminologies
- Informatica Cloud Architecture
- Informatica Cloud Services
- Runtime Environments
- Connections
- The Administrator Service
Lab:
Defining Connections
- What is Data Quality?
- Discuss the Data Quality Management Process Cycle
- List & explain the Dimensions of Data Quality
- Describe Data Quality functions, inputs, & outputs
- Cloud Data Quality Services & Assets
- Cloud Mapping Designer Overview
- Mapping Designer Terminologies
- Mappings & Mapplets
- Common Transformations
-
Lab:
- Create your training folder
- Create & run a mapping to load data into a table
- Profile Data
- Review Profiling Results & identify anomalies
- Profile Features
Lab:
- Profiling Data
- Profiling Insights
- What are Dictionaries & why are they used?
- Creating Dictionaries
- Lab: Create a Dictionary to standardize data
Lab:
- Copy & edit a Dictionary to validate data
- Create a Dictionary to enhance data
- Introduction to Rule Specifications
- Building Rule Specifications
Lab:
- Create a Rule Specification
- Create a Rule Specification with multiple rules
- Scorecard Overview
- Update a Profile & define Rule Occurrences
- Review Scorecards
Lab:
- Apply Rules to a Profile & Review
- Create a Scorecard
- Standardization Overview
- Introduction to the Labeler Asset
- Configure a Labeler Asset in Token Labeler mode
- Configure a Labeler Asset in Character Labeler mode
Lab:
Configuring a Labeler to mask non-numeric data in a field
- Introduction to the Cleanse Asset
- Cleanse, standardize & enhance data
- Build a mapping to cleanse & transform data
- Lab: Create a mapplet to cleanse & standardizing the Company
- Lab: Configuring a mapplet that helps derive a master contact name
Lab:
- Configuring a mapplet to remove noise from a numeric field
- Configure a mapping to cleanse, standardize & enrich data
- 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
- Reprofile & standardize the data
- Introduction to the Deduplicate Asset
- Matching Theory
- Identify matching or related records
- Configure the Deduplicate Asset to consolidate matched data
- Lab: Configure the Deduplicate Asset to identify duplicate or related records
Lab:
- Create a mapping to identify duplicate records
- Update the deduplicate asset to consolidate matched records
- Introduction to the Verify Asset
Verify Address Data
Lab:
Configure the Verify Asset to verify & correct US master records
Lab:
· Create a mapping to verify data US master records
FAQs
A: There are multiple potential reasons for poor data quality & i.e.
- Enormous amounts collected and too much Data to be collected leads to less time to do it, & shortcuts to finish reporting.
- Numerous manual steps, moving figures, summing up, etc.
- Imprecise definitions and wrong interpretation of the fields to be filled out.
A: Accuracy, reliability, timeliness, relevance & completeness are the five traits of finding good data quality.
A: There is a number of steps given below by which a user can improve the quality of data:
- Determine what you want from your data & how to evaluate quality. Data quality means something different across different organizations.
- Assess where your efforts stand today.
- Hire the right people & centralize ownership.
- Implement proactive processes.
- Take advantage of technology.
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We've offices located all across the country & partners across the globe.
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Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with individuals, the latest content in the curriculum, real-time projects & case studies during the training program.
Our faculty will provide you with the knowledge of each training from the fundamental level in an easy way & you're free to ask your doubts any time from your respective faculty.
Our trainers have patience & ability to explain difficult concepts in a simplistic way with depth & width of knowledge.
To ensure quality learning, we provide a support session even after the training program.
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