How to Set Up BigQuery on Linux

How do I set up BigQuery on Linux?

Installs Google Cloud SDK, authenticates, and configures BigQuery so you can create datasets and run SQL from a Linux shell.

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Description

Example H2

Example H3

Why set up BigQuery on Linux?

Linux servers and developer machines can run the Google Cloud SDK, letting you automate BigQuery jobs, load data, and schedule queries without a GUI.

What are the prerequisites?

Install curl, Python 3.8+, and unzip. Create or pick a Google Cloud project and ensure Billing and BigQuery APIs are enabled.

How do I install the Google Cloud SDK?

Download, extract, and run the install script:

curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-441.0.0-linux-x86_64.tar.gz tar -xf google-cloud-cli-*.tar.gz ./google-cloud-sdk/install.sh

How do I initialize and authenticate?

Run gcloud init --project=<PROJECT_ID>. Follow the browser prompt to log in. Service accounts can be activated with gcloud auth activate-service-account --key-file=key.json.

How do I create an ecommerce dataset?

Create a US-region dataset named ecommerce:

bq --location=US mk --dataset <PROJECT_ID>:ecommerce

How do I load CSVs into BigQuery?

Autodetect schema or supply field definitions:

bq load --autodetect --source_format=CSV \ ecommerce.Customers ./Customers.csv \ id:INTEGER,name:STRING,email:STRING,created_at:TIMESTAMP

Repeat for Orders, Products, and OrderItems.

How do I query the data?

Use the bq query command or any SQL editor like Galaxy:

bq query --use_legacy_sql=false \ "SELECT c.name, SUM(oi.quantity*p.price) AS lifetime_value FROM `ecommerce.Customers` c JOIN `ecommerce.Orders` o ON o.customer_id = c.id JOIN `ecommerce.OrderItems` oi ON oi.order_id = o.id JOIN `ecommerce.Products` p ON p.id = oi.product_id GROUP BY c.name ORDER BY lifetime_value DESC LIMIT 10;"

What are best practices?

Store service-account keys securely, use datasets per environment, set default project with bq --project_id, and script loads in CI for repeatability.

What common mistakes should I avoid?

Skipping authentication or forgetting --location cause “Not found” errors. Always match dataset location to load jobs.

Why How to Set Up BigQuery on Linux is important

How to Set Up BigQuery on Linux Example Usage


SELECT c.name,
       SUM(oi.quantity * p.price) AS lifetime_value
FROM   `ecommerce.Customers`   c
JOIN   `ecommerce.Orders`      o  ON o.customer_id = c.id
JOIN   `ecommerce.OrderItems` oi ON oi.order_id    = o.id
JOIN   `ecommerce.Products`    p  ON p.id          = oi.product_id
GROUP  BY c.name
ORDER  BY lifetime_value DESC
LIMIT 10;

How to Set Up BigQuery on Linux Syntax


# Install Google Cloud SDK
curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-VERSION-linux-x86_64.tar.gz
tar -xf google-cloud-cli-*.tar.gz
./google-cloud-sdk/install.sh

# Initialise & authenticate
gcloud init --project=<PROJECT_ID>
# or for CI
gcloud auth activate-service-account --key-file=./service-account.json

# Create dataset in US location
bq --location=US mk --dataset <PROJECT_ID>:ecommerce

# Load Customers CSV into table
bq load --autodetect --source_format=CSV \
  ecommerce.Customers ./Customers.csv \
  id:INTEGER,name:STRING,email:STRING,created_at:TIMESTAMP

# Load Orders
bq load --autodetect --source_format=CSV \
  ecommerce.Orders ./Orders.csv \
  id:INTEGER,customer_id:INTEGER,order_date:DATE,total_amount:NUMERIC

# Load Products
bq load --autodetect --source_format=CSV \
  ecommerce.Products ./Products.csv \
  id:INTEGER,name:STRING,price:NUMERIC,stock:INTEGER

# Load OrderItems
bq load --autodetect --source_format=CSV \
  ecommerce.OrderItems ./OrderItems.csv \
  id:INTEGER,order_id:INTEGER,product_id:INTEGER,quantity:INTEGER

# Run a query
bq query --use_legacy_sql=false "SELECT * FROM `ecommerce.Customers` LIMIT 5;"

Common Mistakes

Using gcloud without activating a service account in CI. Fix: run `gcloud auth activate-service-account --key-file=key.json` before any `bq` commands.
Creating the dataset in EU but loading data with `--location=US`, causing `Not found: Dataset` errors. Fix: match all `--location` flags to the dataset’s region.