How to Evaluate an Enterprise Analytics Platform
Summary
Evaluating an enterprise analytics platform should prioritize a unified data foundation for analytics, AI, and agents over just dashboards and features. Use seven weighted criteria, a proof of concept on your own data, a three-year TCO model, and a vendor question bank to thoroughly assess platforms.
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
The Ambulatory Intelligence Gap
Health Catalyst's Ambulatory Intelligence bridges the critical data gap in ambulatory growth by combining AI with healthcare expertise to unify disconnected access, referral, capacity, and financial data. This solution delivers same-week visibility and actionable insights through prebuilt metrics, enabling healthcare organizations to immediately identify what is driving their numbers and where to act.
Navigating a Synapse Migration to Databricks
Databricks now offers a field-tested playbook for migrating from Azure Synapse (Dedicated SQL Pools, Serverless SQL, and Spark Pools) to a unified Databricks Lakehouse. This phased program helps Synapse customers simplify architecture, improve performance, and lower costs by moving away from a fragmented warehouse not built for modern data workloads.
Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase
At Databricks, the way we build software is changing quickly as we aggressively adopt...
How Imperial College London is accelerating dementia research with a modern data platform
Imperial College London unified IoT, clinical, and research data on a modern data platform, accelerating dementia research and model development. This new architecture leverages Unity Catalog for governed data access and reduced IoT integration timelines from six months to one.
Barracuda makes security logs conversational with Genie
Barracuda Managed XDR now uses Genie to enable natural language search of security logs, letting analysts investigate threats across thousands of customers without SQL or schema expertise. Unity Catalog's row-level security enforces tenant isolation at the data layer, ensuring safe multi-tenant threat detection.
Automatic Upgrades: best practice features for your lakehouse tables
Automatic Upgrades now bring best-practice features like improved performance and reliability to your Unity Catalog managed tables. This first-of-its-kind capability verifies workload compatibility before enabling features, all while keeping them configurable per table.