• In order to do AI, IoT, and other big data-dependent projects right, companies are beyond the confines of traditional relational databases.
• Two recent, related partnerships between highly specialized “graph” database developers, Neo4J and TigerGraph, and public cloud platform providers, Amazon and Google, underscores the importance surfacing insights that would otherwise remain hidden within traditional database architectures.
Organizations anxious to put AI to work as a means of driving innovation must first invest in big data. AI algorithms and predictive models are nothing without a constant influx of high quality data. The trouble is that not all data is created equal, at least in terms of its ability to match the demands of a given initiative, be that AI, IoT, mobility, or edge computing.
Such specific demands in turn drive the adoption of highly specialized data architecture, extending down to the database itself. There are traditional relational databases as well as those specializing in key-values, document storage, in-memory processing, time-series evaluation, transaction ledgers, and graph analysis. Each in turn solves very specific problems – e.g., self-driving cars won’t work without an underlying database capable of performing time-series analysis.
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