Informatica Cloud Data Integration Training - IDESTRAININGS

Eight styles of Data Integration:

Traditional Data Warehouse: Periodically refreshed from production data sources.

Data is gathered from multiple sources to create an collected repository of information. Data can be  extracted from production sources as it is generated (real-time information), or in periodic stages (latent information), making it simpler and more effective than accessing each system independently.

Real-time Data Warehouse: Constantly updated by trickle-feeding data from production data sources.

Trickle feeding the data warehouse – meaning new records are added right away. As soon as new data is entered into any one of the functional systems, it is extracted, transformed and loaded into a real-time repository. The data warehouse is updated simultaneously with functional systems, one record at a time.

Operational Data Access: A real-time view of business activity from operational data and applications.

Operational BI applications generally get their information from an automated workflow process or directly from production systems. There is low latency between when an event occurs and when the BI system is apprehensive of that event, putting business users in touch with the timelier information. Reports are generated directly from the functional system (or occasionally an exact copy of the functional system).

Data Virtualization: Provides real-time aggregation of corporate data across multiple sources.

Data virtualization refers to the real-time collection of corporate data across multiple data sources. It presents distributed data as if it exists in a single position. This distinguishes it from other types of data access technologies, since data is not permanently moved or replicated into new position or database. The source data remains intact. Data virtualization combines data from several sources, which can include functional systems and data warehouse.

Process Integration: Delivers real-time information based on a business event or as a part of a business process.

It enables operations to listen for possibility, identify them, generate them and discover which actions to take according to conditions that have been discovered in advance. Setting up triggers and alerts enables a process to interface with transaction systems and be triggered by possible occurring in those systems. There are three basic categories of process integration:

Real-time alerts

Process-driven BI

Transactional integration

In all  three cases, the operation acquires data before it ever gets loaded into a database.

Data is obtained as the business possibility occurs and is delivered even before it enters a database. Delivery targets can be any device (computer, phone or mobile device) or even another part of a process.

Search Technology: Rapidly scan index content, creating Google-style results from the data sources throughout the enterprise.

Search technology allows users to find data across disparate operations and databases, even when they don’t know what they are looking for. An enriched version of each transaction is transferred to a search engine in HTML format, in concert with the functional system. Subsequent searches link transactions to reports that will further reveal necessary information. This unleashes information that was preliminarily locked up in personal information systems. No data warehouse needed.

Data Access via Web Services: Expose or extract data from multiple sources, irrespective of underlying operating systems, applications or databases.

One of the most effective way to access data is via a web service and native APIs. Developers create web services to extract a subset of information from an internal database or operation, enabling multiple departments to access their own slices of the data. For example, the marketing department might need to tap into certain parts of a sales or finance system. A web service can reveal just the material data. Report results are attained by combining data from one or more data sources and web services. The web services are treated as relational tables.

Native APIs Cloud Data: Optimize the way cloud-based information is accessed and leveraged.

Data integration within the cloud comes in numerous forms. Two of the common synopsis include:

Leveraging cloud sources: Online databases for postal addresses, people, products or even the weather. Can also come in the form of online operations, such as Salesforce.com, which store functional data for an organization.

Storing data in the cloud: Some cloud providers offer the capability to host data offsite. When integrating with the cloud, multiple data integration patterns may be used. When used within a proper framework, cloud sources appear no differently than any other on-premise database or operation.  This flexibility allows the use of different integration patterns which leverage cloud, on-premise or both similarly.