neo4j-spark-skill

Q: How do I install neo4j-spark-skill?

Run npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-spark-skill in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/neo4j-spark-skill/ (personal) or .claude/skills/neo4j-spark-skill/ (project).

by neo4j-contribSecurity

Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.

Installs: 777
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-spark-skill

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads neo4j-spark-skill automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Neo4j Connector for Apache Spark

When to Use

Reading Neo4j nodes/relationships into Spark DataFrames
Writing Spark DataFrames to Neo4j as nodes or relationships
Databricks notebooks connecting to Neo4j
Delta Lake → Neo4j ingestion pipelines
Partitioned parallel reads from large Neo4j graphs

When NOT to Use

Python bolt driver / execute_query → neo4j-driver-python-skill
Cypher query writing → neo4j-cypher-skill
GDS graph algorithms → neo4j-gds-skill
Spring Boot + Neo4j → neo4j-spring-data-skill

Version Matrix

Connector	Spark	Scala	Java	Databricks Runtime	Neo4j	Maven coordinate
6.0.x	4.0, 4.1	2.13	17+	17.3 LTS	5.x, 2025.x, 2026.x	`org.neo4j.connectors:spark:6.0.0-s_2.13`
5.5.x / 5.4.x	3.4, 3.5	2.12, 2.13	8+	14.3–16.4 LTS	4.4, 5.x, 2025.x, 2026.x	`org.neo4j:neo4j-connector-apache-spark_2.13:5.5.0_for_spark_3`

Group ID changed in 6.0 — org.neo4j:neo4j-connector-apache-spark_<scala> is now a relocation POM pointing at org.neo4j.connectors:spark. On Spark 3.x stay on 5.5.x.

6.0 breaking changes

Change	Migration
Spark baseline 3.5 → 4.0/4.1; Scala 2.12 and Java 8–11 dropped	Upgrade to 5.5.0 first, then Spark 4.x + Scala 2.13 + Java 17
Maven coordinate `org.neo4j.connectors:spark:<version>-s_2.13`	Replace old `_for_spark_3` coordinate
`schema.optimization.type` removed	`schema.optimization.node.keys`, `schema.optimization.relationship.keys`, `schema.optimization`
`$stream.offset` in partitioned reads removed	Use `partitions` + `query.count`
`;`-separated multi-statement `script` removed	`script.1`, `script.2`, … `script.N` — executed in numbered order
`relationship.save.strategy` default `native` → `keys`	Set `.option("relationship.save.strategy", "native")` explicitly to keep old behaviour
`query` option rewritten for Data Source V2 predicate push-down	No action; verify plans on upgrade

Setup

Standalone Spark (PySpark)

from pyspark.sql import SparkSession

spark = (SparkSession.builder
    .appName("neo4j-app")
    .config("spark.jars.packages",
            "org.neo4j.connectors:spark:6.0.0-s_2.13")   # Spark 3.x: org.neo4j:neo4j-connector-apache-spark_2.13:5.5.0_for_spark_3
    .config("neo4j.url", "neo4j+s://xxxx.databases.neo4j.io")
    .config("neo4j.authentication.type", "basic")
    .config("neo4j.authentication.basic.username", "neo4j")
    .config("neo4j.authentication.basic.password", "password")
    .getOrCreate())

Standalone Spark (Scala)

val spark = SparkSession.builder
  .appName("neo4j-app")
  .config("spark.jars.packages",
    "org.neo4j.connectors:spark:6.0.0-s_2.13")
  .config("neo4j.url", "neo4j+s://xxxx.databases.neo4j.io")
  .config("neo4j.authentication.type", "basic")
  .config("neo4j.authentication.basic.username", "neo4j")
  .config("neo4j.authentication.basic.password", "password")
  .getOrCreate()

Databricks — Cluster Installation

Cluster → Libraries → Install New → Maven
Coordinate org.neo4j.connectors:spark:6.0.0-s_2.13 on DBR 17.3 LTS; org.neo4j:neo4j-connector-apache-spark_2.13:5.5.0_for_spark_3 on DBR 14.3–16.4 LTS

Cluster → Advanced Options → Spark tab — add config:

neo4j.url neo4j+s://xxxx.databases.neo4j.io
neo4j.authentication.type basic
neo4j.authentication.basic.username {{secrets/neo4j/username}}
neo4j.authentication.basic.password {{secrets/neo4j/password}}

Use Single user access mode (Unity Catalog shared mode not supported)

Databricks — Secrets (preferred over plaintext)

# Store credentials once:
# databricks secrets create-scope --scope neo4j
# databricks secrets put --scope neo4j --key url
# databricks secrets put --scope neo4j --key username
# databricks secrets put --scope neo4j --key password

neo4j_url  = dbutils.secrets.get(scope="neo4j", key="url")
neo4j_user = dbutils.secrets.get(scope="neo4j", key="username")
neo4j_pass = dbutils.secrets.get(scope="neo4j", key="password")

spark.conf.set("neo4j.url", neo4j_url)
spark.conf.set("neo4j.authentication.type", "basic")
spark.conf.set("neo4j.authentication.basic.username", neo4j_user)
spark.conf.set("neo4j.authentication.basic.password", neo4j_pass)

Key Configuration Options

Option	Description	Default
`neo4j.url`	Bolt/Neo4j URI	— (required)
`neo4j.authentication.type`	`none`, `basic`, `kerberos`, `bearer`	`basic`
`neo4j.authentication.basic.username`	Username	driver default
`neo4j.authentication.basic.password`	Password	driver default
`neo4j.authentication.bearer.token`	Bearer token	—
`neo4j.database`	Target database	driver default
`neo4j.access.mode`	`read` or `write`	`read`
`neo4j.encryption.enabled`	TLS (ignored with `+s`/`+ssc` URI)	`false`
`neo4j.db.transaction.timeout`	Transaction timeout (ms)	driver default
`neo4j.db.transaction.metadata.<key>`	Custom transaction metadata surfaced in query log [6.0]	empty
`neo4j.authentication.type` = supplier name	Custom `AuthenticationTokenSupplierFactory` (e.g. `keycloak` via `org.neo4j.connectors:commons-authn-keycloak`) for expiring OAuth/OIDC tokens	—

Cypher version and query tuning [6.0]

Option	Effect
`cypher.version`	Cypher language version — `5` (default) or `25`
`cypher.tuning.<param>`	Emits `CYPHER <param>=<value>` preamble on every generated query

Valid with labels, relationship, query on reads and writes; rejected with gds.

df = (spark.read.format("org.neo4j.spark.DataSource")
    .option("query", "MATCH (o:Object) RETURN o.id AS id, o.name AS name")
    .option("cypher.version", "25")
    .option("cypher.tuning.runtime", "parallel")           # CYPHER 25 runtime=parallel
    .option("db.transaction.metadata.app", "spark-etl")    # tags transactions in query.log
    .load())

Reading from Neo4j

Three mutually exclusive read modes — use exactly one per .read() call.

Label scan (nodes)

# PySpark
df = (spark.read.format("org.neo4j.spark.DataSource")
    .option("labels", ":Person")
    .load())
df.printSchema()
df.show()

// Scala
val df = spark.read
  .format("org.neo4j.spark.DataSource")
  .option("labels", ":Person")
  .load()

Multi-label filter (AND): .option("labels", ":Person:Employee")

Result includes <id> (internal Neo4j id) and <labels> columns.

Cypher query read

df = (spark.read.format("org.neo4j.spark.DataSource")
    .option("query", "MATCH (p:Person)-[:ACTED_IN]->(m:Movie) RETURN p.name AS actor, m.title AS movie, m.year AS year")
    .load())

Use explicit RETURN aliases — they become DataFrame column names. No SKIP/LIMIT in query (connector handles pagination).

Relationship scan

df = (spark.read.format("org.neo4j.spark.DataSource")
    .option("relationship", "BOUGHT")
    .option("relationship.source.labels", ":Customer")
    .option("relationship.target.labels", ":Product")
    .load())

Result columns: <rel.id>, <rel.type>, <source.*>, <target.*>, plus relationship properties.

Read partition tuning

df = (spark.read.format("org.neo4j.spark.DataSource")
    .option("labels", ":Transaction")
    .option("partitions", "10")        # parallel partitions (default: 1)
    .option("batch.size", "5000")      # rows per partition batch (default: 5000)
    .option("schema.flatten.limit", "100")  # rows sampled for schema inference
    .load())

Full read options reference: references/read-patterns.md

Writing to Neo4j

SaveMode

SaveMode	Cypher	Requires
`Append`	`CREATE`	nothing extra
`Overwrite`	`MERGE`	`node.keys` (nodes) or `*.node.keys` (rels)
`ErrorIfExists`	`CREATE` + error if exists	—

Always create uniqueness constraints on node.keys properties before writing in Overwrite mode.

Write nodes — Append (CREATE)

from pyspark.sql import Row

people = spark.createDataFrame([
    {"name": "Alice", "age": 30},
    {"name": "Bob",   "age": 25},
])

(people.write.format("org.neo4j.spark.DataSource")
    .mode("Append")
    .option("labels", ":Person")
    .save())

Write nodes — Overwrite (MERGE)

(people.write.format("org.neo4j.spark.DataSource")
    .mode("Overwrite")
    .option("labels", ":Person")
    .option("node.keys", "name")       # comma-separated; df_col:node_prop if names differ
    .save())

node.keys with rename: .option("node.keys", "df_col:node_property,id:personId")

Write nodes — Scala

import org.apache.spark.sql.SaveMode

peopleDF.write
  .format("org.neo4j.spark.DataSource")
  .mode(SaveMode.Overwrite)
  .option("labels", ":Person")
  .option("node.keys", "name")
  .save()

Write relationships

Use coalesce(1) before relationship writes to avoid deadlocks.

rel_df = spark.createDataFrame([
    {"cust_id": "C1", "prod_id": "P1", "qty": 3},
    {"cust_id": "C2", "prod_id": "P2", "qty": 1},
])

(rel_df.coalesce(1)
    .write.format("org.neo4j.spark.DataSource")
    .mode("Append")
    .option("relationship", "BOUGHT")
    .option("relationship.save.strategy", "keys")
    .option("relationship.source.labels", ":Customer")
    .option("relationship.source.save.mode", "Match")          # require existing nodes
    .option("relationship.source.node.keys", "cust_id:id")
    .option("relationship.target.labels", ":Product")
    .option("relationship.target.save.mode", "Match")
    .option("relationship.target.node.keys", "prod_id:id")
    .option("relationship.properties", "qty:quantity")
    .save())

relationship.source.save.mode / relationship.target.save.mode:

Match — find existing nodes (fail if missing)
Append — always CREATE new nodes
Overwrite — MERGE nodes

Pre-write scripts [6.0]

script.N runs Cypher once before write operations, in numbered order. Required for index/constraint setup when using query mode (schema.optimization.* rejected there).

(df.write.format("org.neo4j.spark.DataSource")
    .mode("Overwrite")
    .option("query", "MERGE (p:Person {email: event.email}) SET p.name = event.name")
    .option("script.1", "CREATE CONSTRAINT person_email IF NOT EXISTS FOR (p:Person) REQUIRE p.email IS UNIQUE")
    .option("script.2", "CREATE INDEX person_name IF NOT EXISTS FOR (p:Person) ON (p.name)")
    .option("index.await.timeout", "300")   # db.awaitIndexes seconds; 0 disables
    .save())

script (single statement) and script.N are mutually exclusive. Semicolon-separated statements inside one script fail on 6.0.

Full write options reference: references/write-patterns.md

Databricks — Delta Lake → Neo4j Pipeline

# Read from Delta table (Unity Catalog or DBFS)
delta_df = spark.read.format("delta").table("catalog.schema.customers")

# Optional: filter/transform in Spark before writing
filtered = delta_df.filter("active = true").select("customer_id", "name", "region")

# Write to Neo4j
(filtered.write.format("org.neo4j.spark.DataSource")
    .mode("Overwrite")
    .option("labels", ":Customer")
    .option("node.keys", "customer_id")
    .option("batch.size", "20000")
    .save())

Pipeline pattern for relationships — load both node sets first, then write edges:

# Step 1: ensure nodes exist
customers_df.write.format("org.neo4j.spark.DataSource").mode("Overwrite") \
    .option("labels", ":Customer").option("node.keys", "customer_id").save()

products_df.write.format("org.neo4j.spark.DataSource").mode("Overwrite") \
    .option("labels", ":Product").option("node.keys", "product_id").save()

# Step 2: write relationships (single partition)
orders_df.coalesce(1).write.format("org.neo4j.spark.DataSource").mode("Append") \
    .option("relationship", "ORDERED") \
    .option("relationship.save.strategy", "keys") \
    .option("relationship.source.labels", ":Customer") \
    .option("relationship.source.save.mode", "Match") \
    .option("relationship.source.node.keys", "customer_id:customer_id") \
    .option("relationship.target.labels", ":Product") \
    .option("relationship.target.save.mode", "Match") \
    .option("relationship.target.node.keys", "product_id:product_id") \
    .save()

Write Performance Tuning

Scenario	Recommendation
Node writes (no lock contention)	`repartition(N)` where N ≤ Neo4j CPU cores
Relationship writes (lock risk)	`coalesce(1)` — single partition
Large datasets	`batch.size` 10000–20000 (adjust to heap)
MERGE-heavy loads	Add uniqueness constraint on `node.keys` properties first

# Aggressive batch — monitor Neo4j heap; OOM risk above 50k
(big_df.repartition(8)
    .write.format("org.neo4j.spark.DataSource")
    .mode("Overwrite")
    .option("labels", ":Event")
    .option("node.keys", "event_id")
    .option("batch.size", "20000")
    .save())

Common Errors

Error	Cause	Fix
`ClassNotFoundException: org.neo4j.spark.DataSource`	JAR not on classpath	Add `spark.jars.packages` or attach library
Deadlock on relationship write	Multiple partitions locking nodes	`coalesce(1)` before write
Duplicate nodes on Overwrite	No uniqueness constraint on keys	`CREATE CONSTRAINT ON (n:Label) ASSERT n.prop IS UNIQUE`
OOM on Neo4j side	`batch.size` too large	Reduce to 5000–10000; check heap
Schema all `string` columns	No APOC, schema not sampled	Set `schema.flatten.limit` higher; or use `query` mode with explicit types
`Access mode is read` error on write	Session opened in read mode	Remove `neo4j.access.mode` or set to `write`
Databricks Shared cluster fails	Unity Catalog shared mode unsupported	Switch to Single User access mode
`NoSuchMethodError` / `IncompatibleClassChangeError` on Spark 4	5.x connector on a Spark 4 runtime	Use `org.neo4j.connectors:spark:6.0.0-s_2.13`
Relationship write ignores `rel.` / `source.` columns after upgrade	6.0 default strategy is `keys`, not `native`	`.option("relationship.save.strategy", "native")`
`script` option rejected with multiple statements	6.0 removed `;`-separated scripts	Split into `script.1`, `script.2`, …

Checklist

FAQ

What does the neo4j-spark-skill skill do?

How do I install neo4j-spark-skill?

Run npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-spark-skill in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/neo4j-spark-skill/ (personal) or .claude/skills/neo4j-spark-skill/ (project).

Which AI agents can use neo4j-spark-skill?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is neo4j-spark-skill safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains neo4j-spark-skill?

It is published by neo4j-contrib in neo4j-contrib/neo4j-skills. It has 777 recorded installs.

More from neo4j-contrib/neo4j-skills

View all →

neo4j-cypher-skill

neo4j-contrib/neo4j-skills

Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x. Use when writing new Cypher queries, optimizing slow queries, graph pattern matching, vector or fulltext search, subqueries, or batch writes. Covers MATCH, MERGE, CREATE, WITH, RETURN, CALL, UNWIND, FOREACH, LOAD CSV, SEARCH, expressions, functions, indexes, and subqueries. Does NOT handle driver migration or API changes — use neo4j-migration-skill. Does NOT cover DB administration or server ops — use neo4j-cli-tools-skill.

Backend & APIs1.5K installs

neo4j-modeling-skill

neo4j-contrib/neo4j-skills

Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.

Backend & APIs1.2K installs

neo4j-graphrag-skill

neo4j-contrib/neo4j-skills

Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrievalquery Cypher fragments, queryparams, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama), embedder setup, index creation, token usage tracking, Cypher 25 SEARCH clause, and LangChain/LlamaIndex integration. Does NOT handle KG construction — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.

AI & Agents1.1K installs

neo4j-driver-python-skill

neo4j-contrib/neo4j-skills

Neo4j Python Driver v6 — driver lifecycle, executequery, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, executequery, executeread, executewrite, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is neo4j (not neo4j-driver) since v6. Python =3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

Code Quality & Workflow984 installs

neo4j-document-import-skill

neo4j-contrib/neo4j-skills

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.

AI & Agents934 installs

neo4j-query-tuning-skill

neo4j-contrib/neo4j-skills

Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.md.

916 installs

Popular security skills

View category →

azure-compliance

microsoft/azure-skills

Run Azure compliance and security audits with azqr plus Key Vault expiration checks. Covers best-practice assessment, resource review, policy/compliance validation, and security posture checks. WHEN: compliance scan, security audit, BEFORE running azqr (compliance cli tool), Azure best practices, Key Vault expiration check, expired certificates, expiring secrets, orphaned resources, compliance assessment.

OfficialSecurity567K installs

minimal-run-and-audit

lllllllama/rigorpilot-skills

Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized reprooutputs/ files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

Security477.4K installs

seo-audit

coreyhaines31/marketingskills

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," "indexing issues," "stuck on page 2," "striking distance keywords," "title tag rewrite," "low CTR," "local SEO," "Google Business Profile," or "map pack." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.

Security238.7K installs

firebase-security-rules-auditor

firebase/agent-skills

Audits Firebase (Firestore, Cloud Storage) security rules for vulnerabilities, privilege escalation, role bypasses, create vs update inconsistencies, resource exhaustion, type safety, size limits, and hasOnly ownership checks. Use when auditing/reviewing rules, running red-team rule assessments, or scoring against auditor checklists. Don't use for Firebase CLI (login, deploy), Auth, Crashlytics, Remote Config, or database queries.

OfficialSecurity139.1K installs

audit

pbakaus/impeccable

Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.

Security81.9K installs

competitors

coreyhaines31/marketingskills

When the user wants to create competitor comparison or alternative pages for SEO and buyer-facing use. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'comparison page,' '[Product] vs [Product],' '[Product] alternative,' 'competitive landing pages,' 'how do we compare to X,' 'competitor teardown,' 'audit our competitor pages,' 'are our comparison pages out of date,' or 'competitive asset audit.' Use this for any content that positions your product against competitors. Covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. For auditing existing claims (not researching competitors from scratch, which is competitor-profiling; not technical SEO on these pages, which is seo-audit), use the asset audit here. For internal battle cards and sales-specific competitor docs, see sales-enablement.

Security79.3K installs

neo4j-spark-skill

Install

Install

Describe the task

Review the result

SKILL.md

Neo4j Connector for Apache Spark

When to Use

When NOT to Use

Version Matrix

6.0 breaking changes

Setup

Standalone Spark (PySpark)

Standalone Spark (Scala)

Databricks — Cluster Installation

Databricks — Secrets (preferred over plaintext)

Key Configuration Options

Cypher version and query tuning [6.0]

Reading from Neo4j

Label scan (nodes)

Cypher query read

Relationship scan

Read partition tuning

Writing to Neo4j

SaveMode

Write nodes — Append (CREATE)

Write nodes — Overwrite (MERGE)

Write nodes — Scala

Write relationships

Pre-write scripts [6.0]

Databricks — Delta Lake → Neo4j Pipeline

Write Performance Tuning

Common Errors

Checklist

FAQ

More from neo4j-contrib/neo4j-skills

Popular security skills