Source: See hereData warehousing, business intelligence, data analytics, and AI services are all coming together under one roof at Amazon Web Services.
This unification of analytics and AI services is perhaps best exemplified by a new offering inside Amazon SageMaker, Unified Studio , a preview of which AWS CEO Matt Garman unveiled at the company⁘s annual re:Invent conference this week. It combines SQL analytics, data processing, AI development, data streaming, business intelligence, and search analytics.
Another offering that AWS announced to support the integration is the SageMaker Data Lakehouse , aimed at helping enterprises unify data across Amazon S3 data lakes and Amazon Redshift data warehouses.
The move could help enterprises reduce IT integration overhead, complexity, and cost, and reflects a broader industry trend towards convergence of data and AI, enterprise demand for end-to-end platforms, and evolution of roles in the generative AI era, say analysts.
Enterprises are struggling with technical debt, silos, and added complexities because data and AI tools have more often than not been treated as islands, said Everest Group Senior Analyst Mansi Gupta.
⁘There has always been a need to streamline the integration and unify the data for a greater return on investment,⁘ she said.
Another driver for the change, according to IDC research director Kathy Lange, is that enterprises are looking to access their entire data estate within a single environment with a unified interface as they want to have ⁘robust⁘ governance across it.
The sudden arrival of generative AI in the enterprise is causing traditional roles such as data scientists, data engineers, and developers to evolve, magnifying demand for integration of analytics and AI services.
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