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Integration platform as a Service (iPaaS) is a suite of multiple cloud services aimed at addressing a wide range of cloud, B2B, and On-Premise integration and governance scenarios. It consolidates multiple cloud services in a suite aimed . The suite aims  at integration and governance of any various combinations of , services - including On-Premise and Off-Premise, applications, Service oriented architecture (SOA), cloud services, processes and data, within or across, organizations. An iPaaS The iPaaS suite is an evolution of Integration as a Service and has been widely adopted for B2B and cloud services integration. It complements the application-development- and hosting-oriented application Platform as a Service (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, middleware, iPaaS or EAI. All underlying concepts and principles around integrations are translated into Extraction, transformation, and loading (ETL). ETL refers to the methods involved in accessing and manipulating source data and loading it into target database. 

The ETL process comprises of are three general three steps:  (1) mapping the data between source and target database; (2) cleansing source data in staging area; and, (3) loading cleansed source data into the target system. HoweverIt is worth nothing that, the 2nd step may not be necessarily be the same for all the ETL tools as . This is because a company's internal system architecture may vary from that of other companies.

Basics

The DBSync's iPaaS can be mostly related to ETL's concepts/terminology. 

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 . The metadata is then 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 - a typical 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.