How to Specify Schema Upload Big Query

Google BigQuery is a cloud storage service that allows you to collect all your data in i system and easily analyze it using SQL queries. For information to be convenient to work with, it should be structured correctly. In this article, we'll explain how to create tables and datasets for uploading to Google BigQuery.

Table of contents

• Datasets: What they are and how to create ane
• How to add a table to load information into Google BigQuery
• How to make changes to the table schema
• Export and import data from/to Google BigQuery
• Export and import data using an addition from OWOX BI
• Why collect data in Google BigQuery?

Best OWOX BI Marketing Cases

Download now

Datasets: What they are and how to create one

To utilise Google BigQuery, you need to create a project in Google Cloud Platform (GCP). Upon registration, y'all'll receive access to all Cloud Platform products during a free trial period and $300 to spend on these products within the next 12 months.

Later on creating a project in Google Cloud Platform, you demand to add together at least 1 dataset to Google BigQuery.

A dataset is a top-level container that'southward used to organize and control access to your data. In unproblematic terms, it'south a kind of folder in which your information is stored in the form of tables and views.

Open your projection in GCP, go to the BigQuery tab, and click Create Dataset:

In the window that opens, specify a name for the dataset and the shelf life of a tabular array. If you desire tables with data to be deleted automatically, specify when exactly. Or go out the default Perpetual choice so that tables can only be deleted manually.

The Processing site field is optional. By default, it'due south set to US multiregion. You can discover more information about regions for storing data in the assistance section.

How to add a table to load data into Google BigQuery

After creating a dataset, yous demand to add a table to which data volition be collected. A table is a set of rows. Each row consists of columns, which are likewise called fields. In that location are several ways to create a table in BigQuery depending on the data source:

• Manually create an empty table and set up a information schema for it
• Create a table using the result of a previously calculated SQL query
• Upload a file from your estimator (in CSV, AVRO, JSON, Parquet, ORC, or Google Sheets format)
• Instead of downloading or streaming information, y'all can create a tabular array that refers to an external source: Cloud Bigtable, Cloud Storage, or Google Drive.

In this article, we'll have a closer look at the commencement method: creating a table manually.

Footstep ane. Select the dataset to which you want to add together the tabular array, and so click Create Table:

Stride two. In the Source field, select Empty Tabular array, and in the Tabular array Type field, select Tabular array in the native format of the target object. Come up up with a name for the table.

Of import: The names of datasets, tables, and fields must exist in Latin characters and contain but messages, numbers, and underscores.

Stride 3. Specify the table schema. The schema consists of four components: two mandatory (column proper name and data type) and 2 optional (column fashion and description). Properly selected types and field modes will facilitate work with the data.

Example schema in BigQuery:

Column names
In the column name, you demand to specify the parameter for which each column is responsible: date, user_id, products, etc. Titles tin contain but Latin messages, numbers, and underscores (maximum 128 characters). Identical field names are not allowed, even if their example is different.

Information blazon 
When creating a tabular array in BigQuery, you lot tin can utilize the following field types:

Modes
BigQuery supports the following modes for table columns:

Note: It isn't necessary to fill in the Mode field. If no manner is specified, the default column is NULLABLE.

Cavalcade descriptions
If yous wish, you lot can add together a curt description (no more than 1024 characters) for each column in the table in order to explain what a particular parameter means.

When you create an empty table in BigQuery, you lot demand to set the schema manually. This can be done in 2 means:
1. Click the Add together field button and make full in the name, blazon, and manner for each cavalcade.

two. Enter the table schema as a JSON array using the Edit as text switch.

In add-on, Google BigQuery can use automated schema detection when loading data from CSV and JSON files.

This option works on the following principle: BigQuery selects a random file from the source y'all specify, scans up to 100 rows of information in it, and uses the results as a representative sample. It then checks each field in the uploaded file and tries to assign a information type to it based on the values ​​in the sample.

When loading Google files, BigQuery can modify the name of a column to make it compatible with its ain SQL syntax. Therefore, we recommend that you upload tables with English field names; if the names are in Russian, for example, the system will rename them automatically. For example:

If, when loading data, the names of the columns were entered incorrectly or you want to modify the names and types of the columns in an existing tabular array, you tin do this manually. We'll tell y'all how.

How to make changes to the tabular array schema

Afterward loading data into Google BigQuery, the table layout may be slightly different from the original. For case, a field name may take changed because of a grapheme that's not supported in BigQuery, or the field type may exist INTEGER instead of STRING. In this instance, you can manually adjust the schema.

How to change a column proper name

Using a SQL query, select all the columns in the table and specify a new name for the column that you want to rename. In this case, you lot can overwrite the existing table or create a new one. Request examples:

                      #legacySQL Select date, order_id, order___________ every bit order_type, -- new field proper noun product_id from [project_name:dataset_name.owoxbi_sessions_20190314]                  

                      #standardSQL Select * EXCEPT (orotp, ddat), orotp as order_id, ddat as engagement from `project_name.dataset_name.owoxbi_sessions_20190314`                  

How to brand changes to the data type in the schema

Using a SQL query, select all information from a table and convert the respective column to a unlike data type. You can apply query results to overwrite an existing table or create a new one. Asking example:

                      #standardSQL Select Bandage (order_id as Cord) equally order_id, Cast (engagement as TIMESTAMP) as date from `project_name.dataset_name.owoxbi_sessions_20190314`                  

How to change the column mode

You can modify the column mode from REQUIRED to NULLABLE as described in the help documentation. The second option is to export the data to Cloud Storage and from there return information technology to BigQuery with the correct manner for all columns.

How to remove a cavalcade from the data schema

Use the SELECT * EXCEPT query to exclude a column (or columns), then write the query results to the onetime table or create a new one. Asking case:

                      #standardSQL Select * EXCEPT (order_id) from `project_name.dataset_name.owoxbi_sessions_20190314`                  

In addition, at that place's a 2nd style to modify the schema that's suitable for all tasks described above: export information and load it into a new table. To rename a column, you lot can upload data from BigQuery to Cloud Storage, then export it from Deject Storage to BigQuery in a new tabular array or overwrite the information in the former table using the Advanced Parameters:

You tin can read well-nigh other ways to alter the table structure in the Google Cloud Platform assistance documentation.

Consign and import data from/to Google BigQuery

You can download information from and upload information to BigQuery without the assist of developers via the interface or a special addition from OWOX BI. Let's consider each method in detail.

Import information via the Google BigQuery interface

To upload the necessary information to storage – for example, information about users and offline orders – open your dataset, click Create Table, and select the data source: Cloud Storage, your computer, Google Bulldoze, or Deject Bigtable. Specify the path to the file, its format, and the proper name of the table where the data will be loaded:

Later on you click Create Table, a table volition announced in your dataset.

Export data via the Google BigQuery interface

Information technology's as well possible to upload processed data from BigQuery – for example, to create a written report through the organisation interface. To practice this, open the desired tabular array with data and click the Export button:

The system volition offering 2 options: view data in Google Information Studio or upload it to Google Cloud Storage. If you select the first option, you'll immediately go to Data Studio, where you can salve the study.

Choosing to export to Google Cloud Storage will open up a new window. In information technology, you lot need to specify where to salvage the data and in what format.

Export and import data using an add-on from OWOX BI

The costless OWOX BI BigQuery Reports addition allows y'all to quickly and conveniently transfer data directly from Google BigQuery to Google Sheets and vice versa. Thus, you don't need to prepare CSV files or use paid tertiary-party services.

For example, say you want to upload offline club information to BigQuery to build a ROPO report. For this, you'll demand to:

1. Install the BigQuery Reports add-on in your browser.
2. Open your information file in Google Sheets and in the tab Add-ons, select OWOX BI BigQuery Reports → Upload information to BigQuery.
3.  In the window that opens, select your project and dataset in BigQuery and enter the desired name for the table. Too, select the fields whose values you want to load. By default, the type of all fields is String, merely we recommend choosing the information blazon co-ordinate to the context (for case, for fields with numeric identifiers, choose INTEGER; for prices, FLOAT):
   

4. Click the Start Upload button and your data will be loaded into Google BigQuery​

Y'all can besides apply this add-on to export data from BigQuery to Google Sheets – for case, to visualize data or share information technology with colleagues who don't have access to BigQuery. For this:

1. Open up Google Sheets. In the Add together-ons tab, select OWOX BI BigQuery Reports → Add a new report:
   

2. And then enter your project in Google BigQuery and select Add new query.
3. In the new window, insert your SQL query. This could be a query that uploads data from a tabular array to BigQuery or a query that pulls and calculates the necessary data.
4. Rename the query to brand information technology like shooting fish in a barrel to find and launch it by clicking the Salve & Run button.
   

To upload data from BigQuery to Google Sheets on a regular footing, you can enable scheduled data updates:

1. In the Add together-ons tab, select OWOX BI BigQuery Reports → Schedule report:
   

2. In the window that opens, set the time and frequency for report updates and click Save:​
   

Why collect data in Google BigQuery?

If you haven't yet appreciated the benefits of Google BigQuery cloud storage, nosotros recommend trying it. With the aid of OWOX BI, you tin combine information from your website, advertising sources, and internal CRM systems into BigQuery in club to:

• Gear up end-to-end analytics and find out the real return on your marketing, taking into account offline orders, returns, and all user steps on the way to a buy;
• Create reports on consummate unsampled data with any parameters and indicators;
• Evaluate customer acquisition channels using cohort assay;
• Find out how your online advertisement impacts offline sales;
• Reduce the share of advertising costs, extend the life cycle of customers, and increase the LTV of your customer base of operations as a whole;
• Segment customers depending on their activity and personalize communication with them.

OWOX BI has a costless trial period during which you can try all the features of the service.

hodgestwon1958.blogspot.com

Source: https://www.owox.com/blog/use-cases/bigquery-schema/

Share this post