As companies move to the public cloud to leverage modern, cloud-based infrastructures, the discussion often turns to “How do we get there?” For some companies, this single migration question can create an insurmountable roadblock that either keeps them from moving to the cloud or severely delays their migration.
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Join Snowflake's team of experts as we share a number of different data analytics trends and predictions that will emerge as this shift to the Data Cloud takes hold. Regardless of where you are in your data-driven transformation, we have a number of predictions for you to start thinking about today, for faster time to value from data tomorrow.
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As data and analytics environments become increasingly complex, organizations can no longer afford to perform many operations manually. According to TDWI research, automation (in general) is one of the top three priorities for analytics. We see automation occurring throughout the data and analytics life cycle. Automation increasingly leverages embedded AI/ML algorithms (i.e., infused in the software) to help perform tasks such as profiling and cleansing data, identifying sensitive data, data mapping, surfacing insights, or building machine learning models.
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Whether for on-demand movement and processing of data or real-time analytics, a rock-steady streaming platform is essential to doing analytics right. Such platforms help with transactional data too, enabling it to stream from the line-of-business applications where it’s born, directly into data warehouses and data lakes, in real-time. Today – especially in the context of digital transformation and operational intelligence – it’s stream or go home.
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