Databricks SQL Warehouse - Upload File to Table

Overview

Quickly upload a file from Workflows to a SQL table in Databricks. __Recommended Setup__ This should be used immediately after downloading data from another source. Although they are not required in order to connect, it is recommended that you provide the `Catalog` and the `Schema` that you will query. By not doing so, the connection will resort to the defaults and the uploaded table will reside there. The recommended approach is to provide the volume where the uploaded file will be staged, then copied into the target table. Workflows will remove the staged file after successfully copying into the target. It is also recommended to use one volume per schema, though this is not strictly enforced. If the volume provided does not exist, Workflows will create it. Additionally the [match type](https://www.shipyardapp.com/docs/reference/blueprint-library/match-type/) selected greatly affects how this Blueprint works. **Note** This blueprint cannot upload a file from your local machine.

Variables

Name	Reference	Type	Required	Default	Options	Description
Access Token	DATABRICKS_SQL_ACCESS_TOKEN	Password	✅	-	-	The access token generated in Databricks for programatic access
Databricks Server Host	DATABRICKS_SQL_SERVER_HOST	Alphanumeric	✅	-	-	The URL address of the SQL warehouse
Warehouse HTTP Path	DATABRICKS_SQL_HTTP_PATH	Alphanumeric	✅	-	-	The extended path for the SQL warehouse
Catalog	DATABRICKS_SQL_CATALOG	Alphanumeric	➖	-	-	The optional catalog to connect to. If none is provided, this will default to Hive Metastore
Schema	DATABRICKS_SQL_SCHEMA	Alphanumeric	➖	-	-	The optional schema to connect to. If none is provided, the blueprint will connect to the `default` schema
Volume	DATABRICKS_SQL_VOLUME	Alphanumeric	➖	-	-	The name of the volume to stage the file
Table Name	DATABRICKS_SQL_TABLE	Alphanumeric	✅	-	-	The table in Databricks to write to
Data Types	DATABRICKS_SQL_DATATYPES	Alphanumeric	➖	-	-	The optional Spark datatypes to use in Databricks. These should be in JSON format, and if none are provided then the datatypes will be inferred.
Insert Method	DATABRICKS_SQL_INSERT_METHOD	Select	✅	append	Append: append Create or Replace: replace	This decides whether to append to an existing table or overwrite an exiting table.
File Type	DATABRICKS_SQL_FILE_TYPE	Select	✅	csv	CSV: csv Parquet: parquet	The file type to load
Workflows File Match Type	DATABRICKS_SQL_MATCH_TYPE	Select	✅	exact_match	Exact Match: exact_match Glob Match: glob_match	Determines if the text in "Workflows File Name" will look for one file with exact match, or multiple files using regex.
Workflows Folder Name	DATABRICKS_SQL_FOLDER_NAME	Alphanumeric	➖	-	-	The optional name of the folder where the file in Workflows is located
Workflows File Name	DATABRICKS_SQL_FILE_NAME	Alphanumeric	✅	-	-	The name of the file in Workflows to load to Databricks

YAML

Below is the YAML template

YAML


source:
  template: Databricks SQL Warehouse - Upload File to Table
  inputs:
    DATABRICKS_SQL_ACCESS_TOKEN:
    DATABRICKS_SQL_SERVER_HOST:
    DATABRICKS_SQL_HTTP_PATH:
    DATABRICKS_SQL_CATALOG:
    DATABRICKS_SQL_SCHEMA:
    DATABRICKS_SQL_VOLUME:
    DATABRICKS_SQL_TABLE:
    DATABRICKS_SQL_DATATYPES:
    DATABRICKS_SQL_INSERT_METHOD: append
    DATABRICKS_SQL_FILE_TYPE: csv
    DATABRICKS_SQL_MATCH_TYPE: exact_match
    DATABRICKS_SQL_FOLDER_NAME:
    DATABRICKS_SQL_FILE_NAME:
  type: TEMPLATE
guardrails:
  retry_count: 1
  retry_wait: 0h0m0s
  runtime_cutoff: 1h0m0s
  exclude_exit_code_ranges:
    - 200
    - 202
    - 203
    - 204
    - 205
    - 206
    - 207
    - 208
    - 209
    - 210
    - 211
    - 249