The Tech Platform
Sep 22, 20212 min
Conversion of data means translating the data to suite target system (data should be formatted according to target system) and then move the translated data using Interface Programs/APIs.
Identify the data to be imported to new system (Business requirement).
Extract into flat file/Staging table
Translate/Convert/Format the data
Load the data into Interface Table (using SQL* Loader/DB Link/Others) after validation (If loading the data using Interface) and then launch standard Interface concurrent program to load the data to Oracle Apps Base Tables
If using API, fetch the data, validate it and then call API to import the data
Conversion can be complex because you need to have a complete understanding of the source you’re converting from, and then format you’re converting to. If you don’t, you run the risk of compromising your data and ruining its integrity during the conversion process.
Migration of data means moving the data from one system to another using Interface Programs/APIs where both the systems have same structure of data.
Process of Migrating of data:
Identify the data to be imported to new system (Business requirement).
Extract the data into flat file/Staging table
Load the data into Interface Table (using SQL* Loader/DB Link/Others) after validation (If loading the data using Interface)
The migration process is very detailed and can take months to complete. Here is a general process a typical data migration would follow.
Database Review: Review of the database and a review of the current implementation is conducted.
Data Mapping: Thoroughly review the tables and data in the database to find unique tables and columns, as well as potential data discrepancies or inconsistencies. It’s important to track which tables the data currently resides in and where the data will be migrated to using data mapping.
Migration: Once the mapping is complete, the migrated data can be transferred from the original database to the new database that can be tested and put into production.
Data integration is the practice of consolidating data from disparate sources into a single dataset with the ultimate goal of providing users with consistent access and delivery of data across the spectrum of subjects and structure types, and to meet the information needs of all applications and business processes. The data integration process is one of the main components in the overall data management process, employed with increasing frequency as big data integration and the need to share existing data continues to grow.
Here we have data integration techniques:
Extract, Transform and Load: copies of datasets from disparate sources are gathered together, harmonized, and loaded into a data warehouse or database
Extract, Load and Transform: data is loaded as is into a big data system and transformed at a later time for particular analytics uses
Change Data Capture: identifies data changes in databases in real-time and applies them to a data warehouse or other repositories
Data Replication: data in one database is replicated to other databases to keep the information the information synchronized to operational uses and for backup
Data Virtualization: data from different systems are virtually combined to create a unified view rather than loading data into a new repository
Streaming Data Integration: a real time data integration method in which different streams of data are continuously integrated and fed into analytics systems and data stores
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