Abstract
In the age of big data, the way we store and analyze heterogeneous data has changed. The complexity of various data inputs in the lakes indicates the significant importance of data ingestion that aids companies in making sense and getting more value from the variety and volume of data that is growing exponentially. In this paper, we propose a scalable and robust data lake architecture based on Apache NiFi for managing big data ingestion from various data sources. To assess the usefulness of our proposal, we evaluated its performance through a benchmark study with the architectures proposed in the literature. The analysis of this comparisons has proven that our contribution exceeds the existing one in terms of the features it supports.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
