A practical guide to real-time analytics in 2026
Summary
Rather than relying on bolted-on serving layers that duplicate data and fragment governance, a lakehouse delivers millisecond query performance and high concurrency directly on governed data. Learn how to evaluate workloads across freshness, latency, and concurrency, alongside best practices and industry use cases for scaling real-time analytics.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
What's New in Lakeflow Designer - October 2026 Roundup
Lakeflow Designer now includes new operators for data cleaning, deduplication, quality checks, and flexible joins, along with canvas support for materialized views, table merges, and live cursor presence. The update also previews upcoming additions, such as Databricks Apps integration, full-output data profiling, and native migration tooling with Lakebridge.
Databricks is a Visionary the Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms
Databricks achieved the highest debut performance in the 20-plus-year history of the Gartner® Magic Quadrant™ for Analytics and BI Platforms, earning recognition as a Visionary with one of the highest scores for Completeness of Vision. Explore how Databricks AI/BI, Genie, and the Data + AI Platform are driving the market shift toward converged platforms, agentic analytics, and centralized semantics.
Brickster Voices: Meet Clara Kurian
Databricks is driving regional expansion across India through strategic campus recruiting, anchored by the launch of its inaugural Bengaluru intern program. Clara Kurian offers an inside perspective on these growth initiatives and outlines what Data and AI companies evaluate in early career talent.
How Flo Health built safer, more useful open models with Databricks AI Runtime
Flo Health improved open model medical safety from 34% to approximately 61% and usefulness from 42% to 76% by running distributed fine-tuning on 32 NVIDIA H100 GPUs with Databricks AI Runtime. The team achieved these results without human-annotated data by combining privacy-first synthetic datasets, supervised fine-tuning, and Direct Preference Optimization with an evaluation framework calibrated against clinician reviews.
How California built its Behavioral Health Public County Profile on Databricks
California launched the Behavioral Health Public County Profile, a public-facing dashboard powered by Databricks that unifies fragmented behavioral health data across all 58 counties. Built to support the Behavioral Health Services Act, the platform establishes standardized metrics, robust governance, and end-to-end data lineage across disparate county systems to transparently track local funding, service delivery, and homelessness initiatives.
From post-training to production: How TinyFish runs its web-native model on Databricks AI Runtime
TinyFish established a continuous path from post-training to deployment by using Databricks AI Runtime on managed GPUs, MLflow for evaluation, and Databricks Model Serving for production inference. Tested across 2,178 web-agent tasks, the resulting web-native model achieved a 77.6% task-success rate in internal go/no-go benchmarking.