Building graph analytics solutions on Databricks

When organizations think about Databricks, they think SQL warehouses, ETL pipelines, and machine learning. Graph analytics rarely makes the list. Yet the platform already has everything needed to build a complete graph-based analytical solution – from data ingestion through algorithms to interactive applications – without leaving its boundaries.

My article will walk you through the building blocks Databricks provides. It shows what each one does and how they come together to form a graph analytics platform. The patterns described here apply to any domain with cross-cutting dependencies – supply chains, financial networks, application architectures, fraud detection, knowledge graphs – wherever entities form a network of relationships.

Why graphs?

Graphs are the natural data model for connected systems. They model entities as nodes and relationships as edges, which makes them especially useful when:

• The same entity participates in multiple relationships of different kinds
• Disruption in one entity propagates through dependencies to others
• You need to reason about paths between entities, not just individual records
• Cross-cutting visibility is required across systems that normally operate in silos

Classic SQL joins handle some of this, but they struggle with recursive structures (cascades, transitive closures) and with the visual exploration that stakeholders often need. Graph algorithms – propagation, connected components, shortest paths, centrality – are designed for exactly these problems.

The Databricks building blocks

To create a complete graph analytics solution, we need five layers: data ingestion, storage, graph algorithms, visualization, and interactive analysis. Here’s what Databricks offers at each layer.

Medallion architecture (Bronze / Silver / Gold)

A pattern for organizing data lake storage by quality and purpose:

• Bronze holds raw ingested data – append-only, schema-on-read.
• Silver has cleaned, conformed, and enriched data – the operational „source of truth” tables.
• Gold holds business-ready data products – aggregates, derived structures, analytical models.

For graph analytics, the gold layer is where you store graph tables: vertices (nodes with attributes) and edges (with source, target, type, and edge attributes). These are just Delta tables – queryable with SQL, joinable with other data, versioned, ACID-compliant. The graph isn’t a separate database; it’s a representation of relationships on top of the same data you already have.

Unity Catalog

Databricks’ unified governance layer for data and AI assets, Unity Catalog handles permissions (table-level, column-level, row-level), data lineage, audit logs, and discoverability across all workspaces in an account. It separates the data layer from the compute layer – meaning catalogs and tables live at the account level and can be accessed from any authorized workspace.

For graph solutions, this means graph tables get the same governance as the rest of your operational data. The same user identity, the same access controls, the same audit trail. No separate vendor onboarding, no parallel permission model.

GraphFrames

A graph processing library built on top of Apache Spark DataFrames, GraphFrames lets you represent data as a graph and run distributed algorithms – PageRank, connected components, shortest paths, and Pregel-style message-passing – directly on Spark clusters.

The key thing about GraphFrames is that it doesn’t introduce a separate graph database or platform. The graph is just two Spark DataFrames (vertices and edges), and algorithms run on existing compute. For batch analytics on large graphs, this is what you want — distributed, scalable, and integrated with the rest of the Spark ecosystem. A one of possible patterns is to use Pregel for problems that propagate through the graph in parallel (cascade analysis from multiple sources, multi-source shortest paths, custom propagation logic):

from graphframes import GraphFrame

from graphframes.lib import Pregel

g = GraphFrame(vertices_df, edges_df)

result = g.pregel \

.setMaxIter(5) \

.withVertexColumn(

colName=”propagated_value”,

initialExpr=col(„seed_value”),

updateAfterAggMsgsExpr=…

) \

.sendMsgToDst(…) \

.aggMsgs(max(Pregel.msg())) \

.run()

Recursive CTE in Spark SQL

For graph traversal problems where SQL is natural (cascade analysis, hierarchical queries, transitive closures), Spark SQL supports recursive Common Table Expressions. The syntax is standard ANSI SQL: a CTE that references itself, seeded by an anchor query and extended by a recursive query.

Recursive CTE often outperforms a distributed graph engine on small-to-medium graphs by avoiding the overhead of building a distributed graph context. The choice between CTE and GraphFrames is per-problem: CTE wins for many independent seeds at modest scale; GraphFrames wins for multi-source parallel propagation and graphs that genuinely need distributed computation.

WITH RECURSIVE propagation AS (

— Anchor: seed nodes

SELECT id AS seed, id AS current_node, 0 AS depth, ARRAY(id) AS path

FROM vertices

UNION ALL

— Recursive step: traverse one edge

SELECT p.seed, e.dst, p.depth + 1, CONCAT(p.path, ARRAY(e.dst))

FROM propagation p

JOIN edges e ON p.current_node = e.src

WHERE NOT ARRAY_CONTAINS(p.path, e.dst)  — cycle protection

)

SELECT seed, COUNT(DISTINCT current_node) AS reachable_count

FROM propagation

GROUP BY seed

AI/BI Dashboards

Databricks’ native dashboard product, built on Delta tables and SQL warehouses. Dashboards bind directly to Unity Catalog data – no separate semantic layer, no data duplication. They support parameters, filters, drill-throughs, and a growing library of visualizations. Genie (the natural-language analytics layer) can be embedded for ad-hoc questions over the same data.

For graph solutions, dashboards consume the output of batch algorithms – ranked tables of high-impact nodes, scenario comparisons, drill-down views into propagation paths. Stakeholders see numbers and charts; the graph machinery underneath stays invisible.

Databricks Apps

A serverless Python runtime for hosting custom applications inside the Databricks workspace. Apps run as Dash, Streamlit, Flask, Gradio, or any other Python framework, with full access to Delta tables, Unity Catalog, SQL warehouses, and the broader Python ecosystem.

Apps matter for graph solutions because some questions don’t fit a precomputed dashboard. „What if THIS specific node is affected – show me exactly what happens, right now” requires sub-second response. An app can load the graph into memory, run algorithms on the fly, and serve interactive visualizations. The compute is integrated (no separate hosting), the auth is integrated (SSO via Databricks), the data access is integrated (same Unity Catalog).

NetworkX (in the App layer)

NetworkX is a Python library for in-memory graph analytics. It’s single-process, single-threaded, and works on graphs that fit in RAM – but for those graphs, operations are essentially instantaneous. BFS, DFS, shortest paths, custom traversals – all run smoothly on graphs with thousands of nodes.

In a Databricks App context, NetworkX complements GraphFrames. GraphFrames handles batch and scale; NetworkX handles latency and interactivity. A typical pattern: load the graph from Delta into NetworkX at app startup, then run user-triggered algorithms on each click.

import networkx as nx

# Load once at app startup

vertices_pdf = spark.table(„catalog.gold.vertices”).toPandas()

edges_pdf = spark.table(„catalog.gold.edges”).toPandas()

G = nx.from_pandas_edgelist(

edges_pdf, source=”src”, target=”dst”,

edge_attr=True, create_using=nx.DiGraph()

)

nx.set_node_attributes(G, vertices_pdf.set_index(„id”).to_dict(„index”))

# Each user interaction: instant

def propagate(seed_node, magnitude):

# BFS through the graph with custom logic

…

Cytoscape.js (in the App layer)

A browser-side JavaScript library for visualizing and interacting with graphs. Cytoscape.js supports layouts (force-directed, hierarchical, circular), styling rules for nodes and edges, custom event handlers, and animations. It’s mature, widely used in bioinformatics and network analysis, and integrates cleanly with Python frameworks via dash-cytoscape.

For interactive graph apps, Cytoscape.js handles what the user sees and clicks. The app backend (NetworkX) computes results; Cytoscape.js renders them. Together they make a complete interactive graph experience: select a node, run an algorithm, see the result visually – all within the Databricks workspace.

How the Building Blocks Compose

A typical graph analytics solution on Databricks looks like this:

Source data lands in bronze, gets cleaned in silver, and is transformed into graph structures (vertices and edges) in gold. Batch algorithms run via GraphFrames or recursive CTE, with results written back to Delta. From there, two consumption modes coexist: dashboards for the precomputed, stakeholder-facing view, and a custom app for the live, interactive analysis – both reading from the same governed data layer.

Why This Composition Matters

The point isn’t that any one of these building blocks is unique to Databricks. GraphFrames is open-source. NetworkX is open-source. Cytoscape.js is open-source. Recursive CTE is standard ANSI SQL. Lakeview dashboards have alternatives in any BI tool.

What’s distinctive is the composition – and specifically, the absence of glue work between layers. The same identity authenticates the dashboard, the app, the SQL warehouse, and the data. The same governance covers operational tables and graph tables. The same Delta storage holds raw data and graph structures. The same workspace hosts notebooks, dashboards, and the application.

For organizations already running their operational data on Databricks, adding a graph analytics layer is incremental – it opens entirely new types of analysis without introducing a separate platform, a new vendor relationship, or a parallel governance model. The graph capability sits on top of the same data, with the same identity, the same access controls, and the same compute.

When to Reach for Graph Analytics

Graph analytics isn’t always the answer. For straightforward analytical questions, SQL aggregations and joins are simpler and more transparent. Graphs become valuable when:

• Your data has cross-cutting dependencies that span multiple operational systems
• Questions involve „how does X affect Y” or „what happens if we change Z”
• You need to identify central, critical, or bridging entities (centrality, betweenness)
• Recursive or transitive relationships matter more than direct ones
• Stakeholders need visual exploration of the network structure

Common domains for such a solution are airline operations (flights, crews, aircraft, gates, passenger connections), supply chain and bills of materials (parts, suppliers, assemblies), financial dependency analysis (counterparty exposure, fraud rings, payment networks), IT and application architecture (service dependencies, blast radius of outages), knowledge graphs (entities and their relationships in documentation, customer data, or research).

If your data shape matches any of these – and you’re already on Databricks – the platform has more of the toolkit than most teams realize.

***