With the rise of transformative technologies and connected digital ecosystems, almost everything we touch produces large amounts of data, in disparate formats, and on a rapid scale. Companies need to harness such voluminous information aka “big data” to cultivate insights. In order to do this, however, they need to capture or ingest large files of data from myriad sources into a data management system where it can be stored, analyzed, and accessed.
The large file data ingestion solutions enable firms import massive datasets, structured and unstructured datasets, and external and internal datasets into data lakes in a fast, automated, governed and cost-effective manner. With an agile data ingestion architecture, these innovative tools enable users deal with a vast amount of complex customer data feeds without compromising quality or speed.
Contrary to the traditional approach where ingestion happened manually, the modern data lake ingestion embraces an automated, user-friendly approach to ingest big data faster and use high-quality insights extracted from it to improve customer experiences in real-time. So, with a modern data ingestion strategy in place, it would be a coup for organizations to manage the lifecycle big data and get it ready for operations, reporting, and analytics quickly – without heavy coding and infrastructure.