Explore resources for working with connections to data sources from a user’s perspective.
- Tutorial | Configure a connection between Dataiku and PostgreSQL (SQL part 0)
- Tutorial | Data transfer with the Sync recipe (SQL part 1)
- Tutorial | Data transfer with the Prepare recipe (SQL part 2)
- Tutorial | Remap connections in a Dataiku instance
- Tutorial | Integration with Amazon Redshift
- Tutorial | Integration with MongoDB
Connect to your existing infrastructure¶
The list of supported SQL databases, and information on how to connect to them, is available from our documentation on SQL datasets.
Accessing cloud storage and databases¶
Cloud File Storage
Fetching data from remote sources¶
It is possible to fetch data using various protocols, and caching the resulting dataset on the filesystem.
Dataiku can read and write in various file formats for files-based connections: filesystem, HDFS, Amazon S3, HTTP, FTP, SSH… See the list of readable file formats.
Accessing data through plugins¶
Many applications such as Google Sheets, SalesForce, Slack… provide capabilities to access their data through APIs. Dataiku plugins allow the addition of custom connections leveraging these APIs to easily define datasets that fetch data from a wide variety of applications.
Many of our users have shown interest in utilizing MS Access in Dataiku. In the interest of knowledge sharing, we wanted to demonstrate how to do just that.
How to open an MS Access file¶
jackcess-2.1.11.jar(including the ones from lib/) into
DATA_DIR/lib/jdbc/(see Installing database drivers for more details)
Configure a new connection:
JDBC driver class:
In the “SQL Dialect” field, select “MySQL < 8”
Create a new dataset using this connection to import an MS Access table in Dataiku.