Holistics vs Neo4j
Side-by-side trajectory, velocity, and editorial themes.
Holistics leans into analytics-as-code with agentic dev workflows and a Power BI migration path
Holistics is a BI platform built around analytics-as-code, where models and dashboards are defined in its AMQL language and version-controlled in Git. Recent releases push on three fronts at once: competitive migration (a one-command Power BI importer), AI-native authoring (Claude Code setup skills and a conversational Ask AI), and steady breadth work like an Oracle connector and org-level GitHub App auth. The throughline is making the code-first workflow easier to adopt and operate.
The direction is to lower the switching cost from incumbent BI tools while betting that analytics teams will work through agents and code rather than point-and-click. Migration tooling and agentic setup skills both target the same friction: getting a team productive in Holistics fast. Parallel embed and dashboard-runtime polish (auto-run, KPI styling) point to a continued focus on the embedded-analytics use case.
Expect the migration story to extend to other incumbents and the agentic-development skills to deepen, given the back-to-back Power BI importer and Claude Code setup releases. Embedded-analytics controls look set to keep maturing.
Neo4j bends Aura toward GenAI: unstructured docs in, queryable graphs out
Neo4j's changelog is almost entirely Aura, its managed cloud. The last month layers two things onto the graph core at once: GenAI-facing ingestion (document-to-graph, vector datatypes, natural-language query) and enterprise plumbing (user-management APIs, project lifecycle, engine concurrency fixes).
The clear direction is lowering the barrier to graph adoption for AI builders — turning PDFs and DOCX into a modeled graph and letting users query in plain language rather than Cypher. In parallel, the Aura API is maturing into something DevOps and IAM teams can automate against, which is the groundwork for larger enterprise footprints.
Expect Document Intelligence to move from preview toward general availability and to tie more tightly to the vector/embedding import path, positioning Aura as a retrieval backend for GenAI apps.
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