The Tutorial Structure
The Getting Started Guide contains tutorials that include lessons and tasks.
Lessons
Each lesson introduces concepts that will help you understand the tasks to complete in the lesson. The lesson provides business requirements from the overall story. The objectives for the lesson outline the tasks that you will complete to meet business requirements. Each lesson provides an estimated time for completion. When you complete the tasks in the lesson, you can review the lesson summary.
If the environment within the tool is not configured, the first lesson in each tutorial helps you do so.
Tasks
The tasks provide step-by-step instructions. Complete all tasks in the order listed to complete the lesson.
Tutorial Prerequisites
Before you can begin the tutorial lessons, the Informatica domain must be running with at least one node set up.
The installer includes tutorial files that you will use to complete the lessons. You can find all the files in both the client and server installations:
- •You can find the tutorial files in the following location in the Developer tool installation path:
<Informatica Installation Directory>\clients\DeveloperClient\Tutorials
- •You can find the tutorial files in the following location in the services installation path:
<Informatica Installation Directory>\server\Tutorials
You need the following files for the tutorial lessons:
- •All_Customers.csv
- •Boston_Customers.csv
- •Customer_Order.xsd
- •LA_customers.csv
- •orders.csv
Informatica Analyst Tutorial
During this tutorial, an analyst logs into the Analyst tool, creates projects and folders, creates profiles and rules, scores data, and creates reference tables.
The lessons you can perform depend on whether you have the Informatica Data Quality, Informatica Data Explorer, or Informatica Data Services products.
The following table describes the lessons you can perform, depending on your product:
Lesson | Description | Product |
|---|
Lesson 1. Setting up Informatica Analyst | Log in to the Analyst tool and create a project and folder for the tutorial lessons. | Data Quality Data Explorer Data Services |
Lesson 2. Creating Data Objects | Import a flat file as a data object and preview the data. | Data Quality Data Explorer |
Lesson 3. Creating Quick Profiles | Creating a quick profile to quickly get an idea of data quality. | Data Quality Data Explorer |
Lesson 4. Creating Custom Profiles | Create a custom profile to configure columns, and sampling and drilldown options. | Data Quality Data Explorer |
Lesson 5. Creating Expression Rules | Create expression rules to modify and profile column values. | Data Quality Data Explorer |
Lesson 6. Creating and Running Scorecards | Create and run a scorecard to measure data quality progress over time. | Data Quality Data Explorer |
Lesson 7. Creating Reference Tables from Profile Results | Create a reference table that you can use to standardize source data. | Data Quality Data Explorer Data Services |
Lesson 8. Creating Reference Tables | Create a reference table to establish relationships between source data and valid and standard values. | Data Quality Data Explorer Data Services |
Informatica Developer Tool
In this tutorial, you use the Developer tool to perform several data quality operations.
Informatica Data Quality and Informatica Data Explorer users use the Developer tool to create and run profiles that analyze the content and structure of data.
Informatica Data Quality users use the Developer tool to design and run processes that enhance data quality.
Complete the following lessons in the data quality tutorial:
Lesson 1. Setting Up Informatica Developer
Create a connection to a Model repository that is managed by a Model Repository Service in a domain. Create a project and folder to store work for the lessons in the tutorial. Select a default Data Integration Service.
Lesson 2. Importing Physical Data Objects
You will define data quality processes for the customer data files associated with these objects.
Lesson 3. Profiling Data
Profiling reveals the content and structure of your data.
Profiling includes join analysis, a form of analysis that determines if a valid join is possible between two data columns.
Lesson 4. Parsing Data
Parsing enriches your data records and improves record structure. It can find useful information in your data and also derive new information from current data.
Lesson 5. Standardizing Data
Standardization removes data errors and inconsistencies found during profiling.
Lesson 6. Validating Address Data
Address validation evaluates the accuracy and deliverability of your postal addresses and fixes address errors and omissions in addresses.