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Loader Google BigQuery

Google BigQuery is a prominent platform in the data analytics and storage domain, offering businesses a robust solution for managing and analyzing large datasets. With an emphasis on practical data processing, Google BigQuery enables businesses to efficiently handle vast amounts of data, supporting informed decision-making and analysis.

Business Value in CFP

Integrating CDP and BigQuery allows businesses to leverage their customer data effectively, facilitating seamless data transfer and analysis. By utilizing Google BigQuery within a CDP ecosystem, businesses can optimize their data management processes and gain valuable insights without relying on complex marketing language.

Setting up the loader in MI

For setting BigQuery as a destination within Meiro Integration, use the  Google BigQuery loader component.

Data In/Data Out

Data In Upload all files into /data/in/files 
Data Out N/A

Learn more: about the folder structure here.

Parameters

dataset.png

All parameters are set on Google:

Project (required) Google project name.
Dataset (required)  Google dataset.

Learn more: Datasets are top-level containers that are used to organize and control access to your tables and views. Learn more about datasets here.

Table

loader bigquery.png

Input Table (required) Name of the table you want to load to the database.
DB Table Name (required) Database table name.
Incremental Signifies if you want to load the data by overwriting whatever is in the database or incrementally.
DB Column Name (required) Database column name.
Column Name (required) The name of the column in the input table you want to load to the database.
Data Type (required) The data type can be string, int64, float64, boolean or timestamp. 

FAQ

My loading did not complete, with the following error message. How do I fix it?

 

Знімок екрана 2023-08-23 о 10.43.17.png

The loader requires a manifest file with the columns parameter, and it should be a list of column names of the CSV you are loading.


Create a manifest file based on the code template below. Remember to change the file names, destination names, and fields accordingly based on the CSV that you are trying to load:

import json

with open("/data/out/tables/<file_name>.csv.manifest", "w") as outf:
    outf.write(
        json.dumps(
            {
                "destination": "<destination_name>", 
                "incremental": False,
                "columns": [
                    "<field01>",
                    "<field02>",
                    "<field03>"
                ]
            }
        )
    )
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