Data Integration

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Most of the organizations use multiple applications, meant for various

purposes (ERP, Vendor Management System, CRM, Loyalty, IoT Sensors,

POS Systems, etc.). These applications might be running in different

technologies and have their own data sets. In order to extract insights

from these data, it is important to integrate them seamlessly. Data

integration is the necessary step where data from different sources are

consolidated. It is the basic step in a Big Data Analytics ecosystem, which

is used to ingest, cleanse, transform and load the data coming from

different data sources to a common data storage (data lake, data

warehouse, data hub, etc.).


Depending on the goals and objectives of the organization, an ETL

process or ELT process can be obtained for Data integration. If huge

volume data needs to be stored in a data lake structured in raw format, an

ELT process will be suitable, where the transformation and treatment of

data happens after loading of the data.On the other hand, for a traditional

data warehouse architecture, the ETL process will be more suitable with

the transformation of data happening before loading the data.