by Shubham Sharma — venturebeat — 2023 was the year of generative AI. However, as every company moved to strengthen their AI strategy, they also realized the value of clean and high-quality data — circling back to the need for robust infrastructure into the mix. From Snowflake to Microsoft, data ecosystem vendors cashed on this opportunity and moved, sometimes even acquired notable players, to give their customers the ability to tap their data for various AI applications as well as implement various AI capabilities into their products.
These are VentureBeat’s top 5 data stories of 2023
1. Microsoft’s move to beat Amazon and Google in the cloud war
In May, Microsoft announced Fabric – an end-to-end, analytics platform that combines all the data and analytics tools organizations need, including Azure Synapse Analytics and Power BI, into a single unified product. We spoke with analysts to understand what makes this offering, which aims to unlock the potential of data and lay the foundation for AI, unique and might help Microsoft “leapfrog” Amazon and other cloud providers, such as Google. At least when it comes to serving large enterprise companies. “With all these capabilities coming together, Microsoft definitely has a slight advantage over the other hyperscalers at the moment,” Noel Yuhanna, an analyst at Forrester, told VentureBeat.
2. The rise of vector database, a new kind of database for AI era
With generative AI being the talking point for every business, Charles Xie, the CEO and founder of Zilliz, discussed the rise of vector databases, a new category of database management, and a paradigm shift for making use of the exponential volumes of unstructured data sitting untapped in object stores. Vector databases offer a mind-numbing new level of capability to search unstructured data in particular, but can tackle semi-structured and even structured data as well. Xie also talked about how companies should approach vector databases to target their respective use cases.
3. Databricks’ $1.3 billion acquisition of MosaicML