These days it's difficult to read about artificial intelligence (AI) without a mention of how China plans to dominate the AI industry worldwide and how it's going to relegate the U.S. to a distant second position. While it's not safe to make predictions about whether China will be successful in...
There is a massive trend to move data analytics and data engineering to the cloud. And there is a good reason for this trend. The fact that cloud environments have cheap storage and the computation costs are pay-as-you-go make initial startup costs of cloud analytics much more affordable than on-premise since there is no initial up-front capital cost. Plus, big data and data warehouse environments in the cloud are relatively easier to use when compared to on-premise offerings. They automatically spin up and spin down and the amount of infrastructure management required is significantly less. That said, there are still some significant challenges that you have to overcome to successfully implement production class data engineering pipelines. These are issues that the cloud vendors are reluctant to talk about but if you are moving to the cloud, you need to take these factors into account: - Hand coding by experts is still required - There is a big delta between ad hoc and production ready data pipelines - Data & data pipeline portability - Managing cross-cloud & hybrid deployments Learn about these 4 challenges in depth and discover how companies like Infoworks are constantly competing to improve their product offerings to simplify deployment of end to end analytics use cases.
Nearly every business leader (99% according to an HBR study) recognizes the need to build a data driven organization. However, so many struggle to use data for a variety of reasons, a big one of them being that data exists in all sorts of places. This is especially the case with startups, who use a number of out of the box tools that don’t connect to each other, and that only offer so much granularity and flexibility. The solution? A data warehouse.
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