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Data Migration vs. Data Conversion vs. Data Integration

Data Conversion

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.

Data Migration

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.

  1. Database Review: Review of the database and a review of the current implementation is conducted.

  2. 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.

  3. 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

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

Data Integration vs Data Conversion vs Data Migration

Data Integration

Data Conversion

Data Migration

Process of combining data residing in different sources that provides users with a unified view of them

Process of translating data from one format to another.

Process of selecting, preparing, extracting and transforming data and permanently transferring it from one computer system to another

used when upgrading the existing system or replacing them

Used to prevent data loss or corruption by maintaining the integrity of the data and embedded structures

used to combine applications of two organization within the same organization

Involves combining data from several disparate sources, which are stored using various technologies

Involves translating the data from one format to another, to Understand, Analyse and present the information.

Involves in selecting, preparing, extracting and transforming data

Types : Manual Data Integration, middleware Data Integration, Application based integration, uniform Access integration, Common Storage integration

Types: Voice Data, Video Data, Photo Data, Paper Document Data, Product Data, Geographical data, Traffic data

Types: Storage Migration, database Migration, Application migration, Cloud Migration, Business process Migration, Data Center Migration, Trickle Data Migration Approach and so on.

The Tech Platform


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