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.