CIMP in Data Integration

Data Integration (DI) is an essential information management capability that provides the foundation for enterprise-wide views of consistent, connected, and trusted business information

The exponential increase in the number and complexity of databases and interfaces between them, as well as the huge rise in the importance of efficient data governance and data quality management, has changed the landscape of metadata management. Similarly, the emergence of dimensional data shook the foundations of data modelling.

The alternative to data integration is data disparity which leads to miscommunication, misunderstanding, confusion, uncertainty, and misinformed business decisions and actions. Data integration is important, but it is complex and challenging. The variety of reasons for data integration (data warehousing, master data management, data migration, etc.), the increasing scope of data sources (enterprise data, external data, big data, web data, etc.), and the growth in data integration techniques and technologies (ETL, ELT, federation, virtualization, etc.) all contribute to data integration complexities. From defining integration requirements to acquiring and unifying data the integration choices are abundant. Effective and sustainable data integration systems depend on skilled and educated data professionals from architects to implementers.

Our Data Integration curriculum includes 11 online courses

Each course is accompanied by a Certified Information Management Professional (CIMP) exam.

Click on the course links to explore course details and outlines, learn more about the exams, or watch free Sneak Peeks.

You can purchase the courses individually or enrol in one of our comprehensive Education Packages at a great discount.

In order to meet CIMP requirements, you must complete Modeling and Metadata Management Fundamentals and at least two other "core" courses.

Product Code

Courses (in alphabetical order)

CIMP Track

Click on the links for course details

META

BD-01

Big Data Fundamentals

 C
DI-01

 Data Integration Fundamentals & Best Practices 

F

DI-03

Data Integration Techniques for Designing an ODS

 
MDM-02 Data Parsing, Matching and De-duplication  

DQ-06

Data Profiling

 

DI-02

Data Virtualization

C

DW-02

Data Warehousing Fundamentals

C

DM-04

DW and BI Data Modeling

 

DQ-04  Ensuring Data Quality in Data Integration  C
DI-04  Introduction to Graph Databases  

MDM-04

MDM Fundamentals: Architecture and Implementation

c

DM-01

Metadata Management Fundamentals

 
SC-08

Streaming Data: Concepts, Applications, and Technologies

 

Our comprehensive information management curriculum

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