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Data scaling is a technique to manage, store, and process the overflow of data. This typically refers to the ability of a system to handle increases in data volume without losing performance. It can involve scaling up (enhancing the capabilities of a single server, like increasing its memory) or scaling out (adding more servers to distribute the workload).

For a fast-growing startup, if your goal is to establish a production-ready environment specifically tailored for AI and machine learning, both Spark and IBM Watson are particularly strong candidates, with Spark providing extensive machine learning capabilities and Watson excelling in deploying AI solutions.

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