Changelog
Follow up on the latest improvements and updates.
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Import Service: Support for List and Vector Datatypes
We are pleased to announce that the Import Service now supports List and Vector datatypes. Whether you are bringing in graph embeddings for GenAI applications or working with list attributes, you can now easily map and import these complex datatypes into Neo4j.
This support is fully integrated across both local file workflows and remote data sources. Here is what you can expect:
1. Local CSV Flow
For your local CSV workflow you can manage your formatting configurations directly within the files panel. Simply click the
...
menu next to your Data source and select Set delimiters
.
This opens a dedicated configuration modal where you can define the exact characters separating your array and vector values.
2. CSV Files in Cloud Buckets
When importing CSV files hosted in cloud storage buckets, you can configure array and vector delimiters during your data source setup flow. Providing these delimiters triggers the automatic detection of list and vector types, ensuring your cloud-hosted file properties are parsed automatically.
3. Strongly-Typed Data Sources
(Parquet, RDBMS, & Cloud Data Warehouses)If you are connecting to remote data sources that natively support structured or typed data—such as Apache Parquet files, relational databases, or cloud data warehouses—the Data Importer does the heavy lifting for you. Compatible source types are automatically recognized and offered as options mappable directly to their equivalent Neo4j types (such as
vector<float32>
, string[]
, or float[]
).
Once selected, you can easily fine-tune your configuration (such as specifying vector dimensions) right inside the property settings UI.
We Want to Hear From You!
We are continuously evolving the data loading experience. Please try out the new list and vector support and share your thoughts, feature requests, or bug reports with us at feedback.neo4j.com/data-importer.
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Import
Announcing the Aura Import API
We’re excited to introduce the Aura Import API, making it easier to automate and manage your data imports into Aura programmatically — whether you’re scheduling recurring jobs or integrating imports into your CI/CD pipelines.
This release is part of the new v2beta1 API, expanding Aura’s automation capabilities. The existing v1 API remains available for broader instance management.
New endpoints include:
- POST /import/jobs— Run an import job
- GET /import/jobs/{AURA_API_IMPORT_JOB_ID}— Check job status (with optional progress details)
- POST /import/jobs/{AURA_API_IMPORT_JOB_ID}/cancellation— Cancel a running job
Learn more:
- API specification
- Authentication guide
- Blog post walkthrough - featuring a Google Cloud Workflows example to schedule imports
The same functionality is also available via the Aura CLI.
We’d love to hear how this API fits into your workflows — share your thoughts or suggestions on our feedback page.
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🚀 New: Import from Cloud Object Storage
We’re excited to announce that Neo4j Aura now supports importing data directly from
AWS S3
, Google Cloud Storage
, and Azure Blob & Data Lake Storage
!
With this release, you can ingest data stored in object storage into your graph, using CSV or Parquet files as input. You can map one or more files—using glob patterns if needed—into temporary tables, enabling flexible transformations and modeling. These tables support the same intuitive mapping experience available for RDBMS and Cloud Data Warehouse sources, so you can stay productive no matter where your data lives.
Whether you're loading product catalogs, user activity logs, or complex relationship data, it’s now easier than ever to bring that data into Neo4j using our visual import tools.
- 📁 Supported file types: .csv,.parquet
- 🧩 Supports multiple files with matching schema
- 🛠️ Familiar modeling and mapping workflow
- 🌐 Works across AWS, GCP, and Azure
💬
Have feedback or ideas?
We’d love to hear from you! Visit feedback.neo4j.com to share your thoughts and help shape future improvements.new
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Import from Databricks
We're pleased to announce that the Import service now supports Databricks. You'll find the new option when adding a data source option under Data services > Import.
To get started, ensure you have a Personal Access Token (PAT) configured, then configure the connection form with details for your warehouse, catalog, schema and PAT.
As always, we'd love to hear any feedback. Please head here to share your thoughts.
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Import from BigQuery and VDC support
BigQuery
We're pleased to announce that the Import service now supports BigQuery. You'll find the new option when adding a data source option under
Data services > Import
To get started, simply provide your Project ID and Dataset Name along with your Service Account Key JSON.
Virtual Dedicated Cloud (VDC) Support
We've also now enabled the import service for VDC instances, so import is now available across all Aura tiers.
Note: At present, instances with private connection enabled are not supported.
As always, we'd love to hear any feedback. Please head here to share your thoughts.
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New in Neo4j AuraDB: Direct Import from Cloud Data Warehouses
We’re making it easier to bring your data into Neo4j AuraDB! The Import Service now supports direct connections to Snowflake alongside existing CSV and relational database imports. BigQuery and Databricks support will be coming next.
A new AI-powered schema analysis helps infer primary and foreign keys—even when they’re not explicitly defined—automating graph model generation. This means faster, more seamless imports from your enterprise data ecosystem.
Get started today in the Aura Console under Import Service. Read more in the blog post.
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🚀 Import from PostgreSQL
We’re pleased to announce the availability of our first new data source in the Console Preview. If you’re not already familiar with the Console Preview, head over to https://console-preview.neo4j.io.
With this release, you’ll see a new way of accessing import options under Data Services > Import. You’ll now find a new, more comprehensive set of data sources, the first becoming available being PostgreSQL, with more to follow soon. The existing CSV import option remains available.
Note: Your source database will need to be accessible over a public IP address to configure the data source.
Once successfully connected, you’ll be guided through steps to create a graph model and mapping ready for import. Whenever possible, constraints in your source database will be used to create a configuration automatically.
The graph models and mapping are saved for reuse under the Graph Models tab.
In contrast to the CSV import flow which streams files from your client machine, the import jobs for new data sources now run in Aura infrastructure. You can see the status of Import jobs in the corresponding tab and long-running import jobs continue running after you log out of the console.
Once you’re ready to see the results of your load, head over to Explore or Query to start interacting with your new graph.
If you have any feedback, head over to https://feedback.neo4j.com/data-importer to share your thoughts.
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New in Data Importer - Node property indexes
The latest release of Data Importer brings support for node property indexes. For properties you refer to frequently, indexes can speed up your queries dramatically. It's now possible to configure indexes directly in data importer. You'll find the UI to configure indexes in the details panel:
Read the docs to learn more!
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Data Importer - Preview and Files panel updates
This release of Data Importer brings a number of small changes that make using Data Importer that little bit easier :
Preview graph visualisation
The Import preview is now the last part of Workspace to migrate from the old visualisation to the new one used in Explore and Query. As well as being more visually consistent, it's now more responsive and easier to navigate.
We'll be exploring upping the limits on the number of preview nodes, do leave us feedback if you think that'd be useful.
Files panel
- Files are now sorted alphabetically, making files easier to locate in longer lists.
- Files can now be globally expanded and collapsed making it easier to work with longer lists.
That's all for now. We're working on some big new capabilities in import, more on that to follow in the coming months 😀. As always keep your feedback coming on feedback.neo4j.com.
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New in Data Importer
This release of Data Importer brings two new features:
Preview selected
We've made a slight tweak to the Preview feature, allowing you to optionally preview selected parts of your our graph. If you have a particularly complex model, it can sometimes be hard to see how all the different nodes and relationship types being previewed are linking. In these scenarios you can use "Preview selected" to only preview selected parts of the model. This can help you validate specific parts of the model are mapped as expected. In the example below, only the Product, Supplier and SUPPLIES parts of the model will be previewed when using the "Preview selected" option.
Generate Cypher script
The Data Importer UI gives you a convenient way to generate your graph model and map data, but what happens if you want to take the import logic and use it somewhere else without the UI? Or what if the Data Importer UI doesn't quite allow the complex transformations that you need?
For these scenarios, we are now providing you the option to view and download a Cypher script representing your model and mapping. You can use these as-is, or adapt for more complex needs.
Examples of where you might use this generated code include:
- The Workspace Query UI. Note that the files will need to be accessible to the DBMS you are connected to. At present this means Aura customers will need their files accessible on a public URL for the LOAD CSVcommand to access them.
- On the command line via cypher-shell
- Driver code - De-compose the script to re-use the CREATE CONSTRAINTandMERGEstatements to implement your loads
The load script itself is commented to help you understand the purpose of the different parts and where you might need to make changes, depending on where you're running it.
You can also choose to download the files with your script, which will give you a handy zip of the script and your files.
We hope you enjoy using these new features and as always please head over to https://feedback.neo4j.com/data-importer with any feedback.
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