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Information and data has become the lifeblood of businesses in today's digital world. Having access to the right data, at the right time, is critical for managers to make good decisions and for businesses to provide services to their customers. The volumes of data has proliferated over the last 10 years and we find companies are now dealing with and drowning in the “sea of data”.
This data needs to be moved between applications, between databases and often between companies. This data could both be structured data (like orders, invoices, point of sales data, employee data, marketing data etc.) or unstructured data (such as images, videos, scanned documents etc.). Moving of large data files is very challenging especially when it is structured data in XML, text, CSV or Excel formats.
In the data integration world, large files are a pain. Processing of large or flat files often leads to application failures and breakdown of enterprise data flows, resulting in incomprehensible information losses and painful delays in processing mission-critical business data.
Enterprises often try manual chunking of data into smaller data sets that need to be aggregated post processing, but it’s not a smart path to take. It almost always requires highly skilled developers for implementing a very complex mechanism of chunking and aggregating, and even then, it is difficult, prone to errors, and remarkably inefficient. While appliances like IBM DataPower can get the job done, the approach is too expensive and too hard to maintain or upgrade.
With the dawn of big data, enterprises are looking for smarter movement of large scale data that drives better business decisions and improves business bottom line. The need of the hour is of an approach that takes data from different external sources and disparate formats, combines them with internal sources and standards, and merges the large volume of data in real time.