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Integration platform as a service Service (iPaaS) is a suite of cloud services aimed at addressing a wide range of cloud, B2B, and onOn-premises Premise integration and governance scenarios. An iPaaS It consolidates multiple cloud services in a suite aimed at the integration and governance of any combination combinations of an on, On-premises Premise and offOff-premises Premise, applications, SOA ( Service oriented architecture (SOA) and , cloud services, processes , and data, within or across, organizations. An iPaaS complement complements the application-development- and hosting-oriented application platform Platform as a serviceService (PaaS). An iPaaS is an evolution of integration as a service, which has been widely adopted for B2B and cloud services integrations.

Fundamentals Of Integration

The integration approach can be either point-to-point or , middleware or , iPaaS or EAI, . All of underlying concepts and principles around integrations are translated to into Extraction, transformation, and loading also known as (ETL). ETL refers to the methods involved in accessing and manipulating source data and loading it into target database. 

The first step in ETL process is comprises of are general three steps:  (1) mapping the data between source and target database. The second step is cleansing of ; (2) cleansing source data in staging area. The third step is transforming ; and (3) loading cleansed source data and then loading into the target system. However, the second 2nd step may not be necessarily be the same for all the ETL tools as their a company's internal system architecture might vary internallymay vary from that of other companies.

Basics

The DBSync's iPaaS can be mostly related with the following to ETL's concepts/terminology. While reading in the next article you should be able to relate the core concepts of ETL with that of DBSync iPaaS. 

Source System : 

A database, application, file or other storage facility from which the data in a warehouse is derived.

Mapping: 

The definition of the relationship and data flow between source and target objects.

Metadata:

Data that describes data and other structures, such as objects, business rules, and processes. For example, the schema design of a data warehouse is typically stored in a repository as metadata, which is used to generate scripts used to build and populate the data warehouse. A repository contains metadata.

Staging Area:

A place where data is processed before entering the warehouse.

Cleansing:

The process of resolving inconsistencies and fixing the anomalies in source data, typically as part of the ETL process.

Transformation: 

The process of manipulating data . Any manipulation beyond copying is a transformation. Examples include cleansing, aggregating, and integrating data from multiple sources.

Transportation:

The process of moving copied or transformed data from a source to a data warehouse.

Target System:

A database, application, file, or other storage facility to which the "transformed source data " is loaded in a data warehouse.