Highlights of the Azure stack include Synapse, Synapse SQL Pool, Azure Data Factory, Azure Stream Analytics, Azure Databricks Premium Tier, HDInsight, Power BI Professional, Azure Machine Learning, Azure Active Directory P1, and Azure Purview. AWS was 9.8% higher, Google 46% higher and Snowflake was 2.5 times higher. It had a cost of $1.6M for a one-year (annual) cost to purchase the analytics stack. When almost every additional demand of performance, scale, or analytics can only be met by adding new resources, it gets expensive.īased on our approach described in the next section, and using the assumptions listed in each section mimicking a medium enterprise application, Azure was the lowest cost platform. Some architectures look integrated, but in reality, may be more complex and more expensive. We have learned that the cloud analytic framework selected for an enterprise, and for an enterprise project, matters to cost.īy looking at the problem from a cost perspective, we’ve learned to be wary of architectures that decentralize and decouple every component by business domain, which enables flexibility in design, but blows up the matrix of enterprise management needs. We decided to take four leading platforms for machine learning under analysis. Greater complexity will lead to more technical debt and administrative burden to cobble together and maintain the flow of data between point solutions. As more components are added and more integration points among those components arise, complexity will increase substantially. This aspect reduces complexity in administration and scaling data pipelines. A key differentiator is the overarching management, deployment, governance, billing, and security. The platform chosen should bring a multitude of data services onto a single cohesive platform. They need a selection that allows a worry-less experience with the architecture and its components. They need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning.
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