How AT&T Data Science Team Solved an Insurmountable Big Data Challenge on Databricks
Description
Data driven personalization is an insurmountable challenge for AT&T’s data science team because of the size of datasets and complexity of data engineering. More often these data preparation tasks not only take several hours or days to complete but some of these tasks fail to complete affecting productivity. In this session, the AT&T Data Science team will talk about how RAPIDS Accelerator for Apache Spark and Photon runtime on Databricks can be leveraged to process these extremely large datasets resulting in improved content recommendation, classification, etc while reducing infrastructure costs. The team will compare speedups and costs to the regular Databricks runtime Apache Spark environment. The size of tested datasets vary from 2TB - 50TB which consists of data collected from for 1 day to 31 days. The talk will showcase the results from both RAPIDS accelerator for Apache Spark and Databricks Photon runtime. Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/
Description from YouTube. Full content on the video page.
Topics
More from Databricks
ReleasesDatabricks launches across the Data + AI stack in 90 seconds
Databricks announced LTAP to unify lakebased and lakehouse data, eliminating ETL and enabling a single copy of data for analytical and operational needs. They also introduced Unity AI Gateway for governance, Genie Ontology for enterprise knowledge graphs, and open-sourced Omniant for managing multiple coding agents.
ReleasesIntroducing Omnigent: The Ultimate Meta-Harness for AI Agents
Omnigent is a new open-source meta-harness for AI agents that provides a unified interface for composition, control, and collaboration across multiple models and agent workflows. It enables stateful, data-centric policies for guardrails and allows real-time sharing and steering of live agent sessions with teammates.
NewsHow DEFRA and Natural England Accelerate Peatland Restoration with AI and Databricks
DEFRA and Natural England utilize AI and Databricks to accelerate peatland restoration by automating the mapping of peatland features and peat dams across England. This technology significantly reduces the time required for mapping, enabling faster identification and restoration of these crucial carbon-storing habitats.
NewsAI Stack Explained in 3 Layers (LLM, Agent Harness, Omnigent)
The AI stack now includes a third layer, the meta harness, which sits above individual agent harnesses. This meta harness, exemplified by Databricks' open-sourced Omnigent, allows for routing queries to appropriate agents and orchestrating tasks across multiple agents, enabling seamless interaction and context sharing between them.
NewsWhat’s coming next to Free Edition
Databricks announces the availability of Genie, GPUs, Agent Hooks, Lakehouse, and Lake Flow Designer on its Free Edition. This update provides virtually all of Databricks' production platform features for free, enabling users to learn and build data and AI projects.