Dat data integratie verder gaat dan het werkterrein van het DWH klinkt soms vreemd in de oren, maar is wel degelijk waar. Om verwarring te voorkomen gelden 4 richtlijnen van het TDWI waarin DWH afwijkt ten opzichte van data integratie:
Guideline #1: Data integration is a family of techniques and best practices
The unfortunate knee-jerk reaction of many data warehouse professionals is that the term data integration is synonymous with ETL (extract, transform, and load) simply because ETL is the most common form of data integration found in data warehousing. However, there are other techniques (and best practices to go with them), including data federation, database replication, and data synchronization. Different techniques have different capabilities and prominent use cases, so it behooves a data integration specialist to know and apply them all.
Guideline #2: Data integration practices reach across both analytics and operations
In Analytic DI, one or more DI techniques are applied in the context of business intelligence (BI) or data warehousing (DW). Operational DI applies DI techniques outside BI/DW, typically for the migration or consolidation of operational databases, synchronizing operational databases, or exchanging data in a business-to-business context. Analytic DI and operational DI are both growing practice areas, and both are progressively staffed from a common competency center or similar organization.
Guideline #3: Data integration is an autonomous data management practice
In some old-fashioned organizations, DI is considered a mere subset of DW. It can be that, but it can also be independent. For example, the existence of operational DI proves DI’s independence from DW. Furthermore, hundreds of DI competency centers have sprung up in the last ten years or so as a shared-service organization for staffing all DI work — not just DI for DW.
Guideline #4: A data integration solution should have architecture
After all, other types of IT solutions have architecture. DI architecture helps you with DI development standards, the reuse of DI objects, and the maintenance of solutions. The preferred architecture among integration technologies — whether for data or application integration — is the hub-and-spoke. For this reason, most DI tools today lend themselves to hub-and-spoke. However, there are many variations of it, so you need to actively design an architecture for your DI solutions.
from “Data Integration in a Nutshell: Four Essential Guidelines”:
http://tdwi.org/Articles/2010/03/24/Experts-DI-Four-Guidelines.aspx
