Stephanie Chatagner's Blog

Well.

The quality of data is directly related to accurate insights and better decision-making.
Applications and business processes need combined data from multiple separate business systems into a single unified view. In order to provide consistent access and delivery data across subjects and structure types, this view is typically stored in a central data integration, DataWarehouse and is often a prerequisite to other processes including analysis, reporting, and forecasting.

To allow organizations to make better choices based on deeper understanding of their business data, there are a few data integration techniques:

• Extract, Transform and Load : data is copied from different sources, merged, reconciled and load into DWh.
• Extract, Load and Transform : data is loaded into a big data system and transformed later for a particular analytics uses.
• Change Data Capture : follow data changs in real-time and applies them to a Dwh.
• Data Replication : some data from is replicted to other databases to keep the information synchronized to operational uses and for backup.
• Streaming Data Integration : a real time data integration method. Different streams of data are continuously integrated and fed into analytics systems and data stores.

Benefits:

Data sources are most of the time disparate and stored into silo. Data Integration is useful for

• Data integrity and data quality
• Information transfer between systems
• Fast and easily accessible connections between data stores.
• Increased efficiency and ROI
• Complete view of business intelligence, insights, and analytics

So, step by step and keep learning!