The success of any analytics initiative depends on how well you process your data. But optimizing data processing can be challenging—especially when moving to the cloud. There are many solutions in the market to help, such as Spark, Kubernetes, and Arrow. Yet no single solution possesses the capabilities to address all data management use cases. Although enterprises are using these tools as building blocks within their analytics ecosystems, using multiple point products for data management increases cost and complexity. As a result, many organizations are spending hundreds of person-hours building, optimizing—and then debugging problems in—their data management pipelines. The complexity and challenges multiply by degrees of magnitude as enterprises’ data grows, making long-term maintenance, governance, and scalability difficult. The “last mile” of these journeys is incredibly critical to fully realize all the benefits. However, few enterprises succeed in getting there.
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