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It’s no surprise that there are gaps between Big Iron (invented in the middle of the last century) and Big Data (invented a decade ago). But bridging them together can bring great business value: Customer 360, Real-time Fraud Detection, Active Archive to name a few.
Moving mainframe data into Hadoop storage formats is non-trivial, and processing that data in Hadoop is a challenge as well. Without the right tools, you may end up stalling your Big Data …
Data is fuel for artificial intelligence and machine learning applications and organizations with a wealth of legacy data are at an advantage in creating such applications because there’s more data to train them. Fed by more data, models and predictions are more accurate.
The challenge: Tools for AI and ML are targeted toward data in widely accessible modern formats; not legacy data structures. Additionally, many analytics teams are familiar with data in Hadoop and Spark, but are often much …