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Neo4j ETL and Data Integration

Neo4j ETL reveals data connections within tabular data stored in an RDBMS and delivers an exceptional initial and ongoing experience moving data into the Neo4j graph database.

With Neo4j ETL you can:

  • Connect to popular databases via JDBC
  • Use a graphical interface to adjust values
  • Arrange table structures as labeled nodes and identify JOINs and JOIN tables as graph relationships
  • Map or change labels and properties prior to execution
  • Data is exported in graph-ready CSVs and fed to the Neo4j data importer

Graph connections are materialized through relational joins as data is imported and persisted permanently after import. For more information, read our ETL tool developer guide on installing and using the ETL tool and get your data into Neo4j quickly and easily!

Supported Sources

Community Edition

Enterprise Edition

PostgreSQL Oracle
MariaDB Teradata

Neo4j ETL Visual Editor

Transform Tabular Data into Graph Data
Neo4j Data Integration

Neo4j Data Lake Integrator

Data lakes struggle to derive value from their accumulated data, and arguments rage as to the utility of what can become an over-filled, swampy mess. While it’s easy to fill the lake, wrangling its contents, adding context and delivering it to analytical and operational use cases remains an IT challenge.

Finding and utilizing the connections within that data is all but impossible.

The Neo4j Data Lake Integrator surfaces value submerged in data lakes by making it possible to read, interpret and prepare data as both in-memory and persisted graphs for use in real-time, transactional applications and graph analytic exercises within the Neo4j Graph Platform. The Data Lake Integrator can help enterprises:

  • Build Metadata Catalogs: Discover definitions of data objects and their relationships with each other to weave a highly connected view of information in the data lake. The resulting metadata graph will help data exploration, impact analysis and compliance initiatives.
  • Wrangle Data: Combine data from the lake with other sources including Neo4j, wrangling it with Cypher – the SQL for graphs. Composable Cypher queries allow you to return data in an in-memory graph format using Apache Spark™, as well as in tabular or CSV formats.
  • Perform Graph Analytics: Import data directly from the data lake into the Neo4j Graph Platform for faster and intuitive graph analytics using graph algorithms such as PageRank, community detection and path finding to discover new insights.
  • Operationalize Data: Import data directly from the data lake into the Neo4j Graph Platform for real-time transactional and traversal applications.
  • Snapshot Graphs: The output of the Data Lake Integrator is a properly structured CSV for reconstituting Neo4j graph data. These files can be saved as snapshots, versioned, diffed, reused and backed up in HDFS.