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Comparison · Analytics

Holistics vs Deepnote

Side-by-side trajectory, velocity, and editorial themes.

Holistics logo
Holistics
ANALYTICS
7.5

Holistics leans into analytics-as-code with agentic dev workflows and a Power BI migration path

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

D
Deepnote
ANALYTICS
6.3

Deepnote reshapes the data notebook into agent-operable infrastructure.

◆ Current state

Deepnote, a collaborative data-science notebook, is steadily making itself agent-native: MCP tools now let AI agents create and wire integrations end-to-end, and OpenAI's Codex connects natively to a Deepnote workspace's notebooks, schedules, and data. Underneath, it keeps shipping solid workflow features — run snapshots, Git and GitLab sync, Polars, PDF export.

◆ Where it's heading

Two tracks are converging: reproducibility and engineering rigor (immutable run snapshots, Git sync, notebook interoperability) and agent-operability (MCP tools, Codex context). Deepnote is positioning the workspace as the trusted context layer that AI agents act through, not just a place humans write notebooks.

◆ Prediction

Expect more MCP tooling that lets agents operate Deepnote projects autonomously, plus deeper native hooks for external coding agents — the workspace-as-agent-context bet will likely expand beyond Codex.

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