The machine learning-powered service, accessible via a no-code interface in the AWS Management Console, can be used to match data from multiple data lakes or AWS storage, the company said.
While Apache Kafka is slowly introducing KRaft to simplify its approach to consistency, systems built on Raft show more promise for tomorrow’s hyper-gig workloads.
For businesses and their customers, the answers to most questions rely on data that is locked away in enterprise systems. Here’s how to deliver that data to GPT model prompts in real time.
The Delta Lake updates aim at helping data professionals create generative AI capabilities for their enterprise with foundation models from MosaicML and Hugging Face, among others.
The updates in Delta Lake 3.0 include a new universal table format, dubbed UniForm, a Delta Kernel, and liquid clustering to improve data read and write performance.
While Snowpark Container Services and an Nvidia partnership will help enterprises manage large language models, Streamlit and Git updates are squarely aimed at easing developers’ tasks.
SingleStore Kai for MongoDB brings real-time analytics to JSON documents by translating MongoDB queries onto SQL statements that are executed on SingleStoreDB. No changes to schema, data, or queries required.
In addition to integrating Google Cloud’s Vertex AI foundation models, MongoDB is adding features aimed at making Atlas a complete developer data platform.
The new Schema GPT Translator is designed to free developers to focus on other aspects real-time data pipelines instead of coping with the time-consuming process of manually creating schema mappings.
The two companies are also partnering to launch an open source project, CassIO, aimed at making Apache Cassandra more compatible with AI and large language model workloads.