Exasol

​Exasol is a in-memory database built for analytics. You can use it to interact with Zingg AI.
Prerequisites
To use the Exasol database with Zingg, first you should download the required dependencies and set them in the Zingg configuration file.
Download Assembled Exasol Spark Connector Jar
Please download the latest assembled Exasol spark-connector jar file. Go to the Assets section below, and make sure you download the jar file with -assembly suffix in the file name.
Update Zingg Configuration
After downloading the Exasol spark-connector jar file, you should update the spark.jars parameter in Zingg's runtime properties. so that it can find the Exasol dependencies.
For example:
spark.jars=spark-connector_2.12-1.3.0-spark-3.3.2-assembly.jar
If there are more than one jar files, please use comma as separator. Additionally, please change the version accordingly so that it matches your Zingg and Spark versions.
Connector Settings
Finally, create a configuration JSON file for Zingg, and update the data or output settings accordingly.
For example:
...
 "data": [
    {
        "name": "input",
        "format": "com.exasol.spark",
        "props": {
            "host": "10.11.0.2",
            "port": "8563",
            "username": "sys",
            "password": "exasol",
            "query": "SELECT * FROM DB_SCHEMA.CUSTOMERS"
        }
    }
 ],
 ...
Similarly, for output:
...
"output": [
   {
       "name": "output",
       "format": "com.exasol.spark",
       "props": {
           "host": "10.11.0.2",
           "port": "8563",
           "username": "sys",
           "password": "exasol",
           "create_table": "true",
           "table": "DB_SCHEMA.ENTITY_RESOLUTION",
       },
       "mode": "Append"
   }
],
...
Please note that, the host parameter should be the first internal node's IPv4 address.
As Zingg uses Exasol Spark connector underneath, please also check out the user guide and configuration options for more information.

BigQuery

Working With Python

Last modified 2mo ago