Data Ingestion

Extract Data from Multiple Data Sources

Modern day businesses deal with a variety of data, both structured and

unstructured, coming from multiple sources. Most of the analytics use

cases deal with data that are coming from multiple data sources in order to

give a holistic insight. Hence, it is extremely important that data are

collected efficiently from multiple sources using the data integration tools.


Thus, it is often essential to integrate these data coming from distributed

and siloed data sources to a data lake or data warehouse, which can be

used for further development of descriptive, predictive, or AI driven use

cases. Data ingestion is the process of obtaining and importing data in a

database. Based on the requirement, data can be ingested in batches or

in real time. The data ingestion process needs to be effective and efficient

by means of prioritization, validation and proper routing. 


In order to attain competitive advantage and to get timely insights, the

speed and efficiency of the data ingestion process is extremely important

in today’s big data ecosystem, where there are a number of data sources

with high volume of data. Automation of data ingestion is of extreme

importance, where the data gets pulled from the data sources and pushed

in the destination storage automatically. For any BI and analytics project,

one of the most essential elements is to run the data ingestion process

successfully, efficiently and automatically.