Heterogeneous Data Integration with ELT and Analytical MPP Database for Data Analysis Application

Ina data analysis project, data integration is per-formed on a large volume of data collection from heterogeneous sources. Projects need to collect data from Web API, nonrelational, Relational databases, Edge AI, Connected Vehicles, Structured and Unstructured Files, and system logs. Before storing, data will be collected, Validated, and Transformed. This process is created in Extract Load Transform (ELT) and scheduled. Traditional Extract Transform and Load (ETL) systems process the data before loading it into the database, and the data transformation needs dedicated resources in the ETL server. Projects often encounter performance issues and high hardware and licensing costs while processing extensive data set, and this is handled efficiently in Extract Load and Transform (ELT) and Analytical Massively Parallel Processing (MPP). Data transform will take place in MPP Databases, and the resources are highly scalable in the cloud. ELT with Analytical MPP makes data analysis much faster and more efficient. We will discuss the roles of ETL and ELT tools in data integration for a successful data science application.