Using Interzoid with Databricks

The Databricks platform provides a cloud-based environment that combines data processing, analytics, and machine learning for datasets. It allows data engineers, data scientists, and analysts to work collaboratively on data projects. The platform is designed to handle large-scale data processing tasks and provides support for various programming languages such as Python, R, Scala, and SQL.

Connection string example:

            
    // For a cluster
    "token:dapi1ab2c34defabc567890123d4efa56789@dbc-a1b2345c-d6e7.cloud.databricks.com:443/sql/protocolv1/o/1234567890123456/1234-567890-abcdefgh"

    // For a SQL warehouse
    "token:dapi1ab2c34defabc567890123d4efa56789@dbc-a1b2345c-d6e7.cloud.databricks.com:443/sql/1.0/endpoints/a1b234c5678901d2"
            

Supported optional connection parameters can be specified in param=value and include:

catalog: Sets the initial catalog name in the session.

schema: Sets the initial schema name in the session.

maxRows: Sets up the maximum number of rows fetched per request. The default is 10000.

timeout: Adds the timeout (in seconds) for the server query execution. The default is no timeout.

userAgentEntry: Used to identify partners. For more information, see your partner’s documentation.

Supported optional session parameters can be specified in param=value and include:

ansi_mode: A Boolean string. true for session statements to adhere to rules specified by the ANSI SQL specification. The system default is false.

timezone: A string, for example America/Los_Angeles. Sets the timezone of the session. The system default is UTC.

A "connection string" provides the parameters necessary to initiate the connection to a specific data source on the Cloud for analysis, enrichment, matching, or whatever data function from Interzoid is selected for use.

A connection string enables connecting to a data source using the Interzoid Cloud Data Connect product.

For additional information performing data matching, match reports, and the ability to match otherwise-inconsistent data in Databricks from within a Notebook utilizing DataFrames and a simple Python function (including the free Databricks Community Edition), see here.