BigQuery essentials¶

Estimated time to read: 4 minutes

When working with BigQuery, it's essential to have a good understanding of various concepts, tools, and best practices. Here's a list of key topics you should be familiar with to use BigQuery effectively:

1. BigQuery SQL Syntax

Understand the SQL syntax specific to BigQuery for querying and manipulating data. This includes knowing how to write queries using SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, and JOIN clauses.

2. Data Types

Know the various data types supported by BigQuery, such as STRING, INTEGER, FLOAT, BOOLEAN, DATE, TIME, DATETIME, TIMESTAMP, ARRAY, STRUCT, and GEOGRAPHY.

Managing Datasets and Tables: Learn how to create, update, and delete datasets and tables, as well as how to load and export data to and from BigQuery.

3. Managing Datasets and Tables Learn how to create, update, and delete datasets and tables, as well as how to load and export data to and from BigQuery.

Creating a dataset:

SQL

CREATE SCHEMA my_dataset;

Creating a table:

SQL

CREATE TABLE my_dataset.my_table (
  id INT64,
  name STRING,
  age INT64
);

4. Partitioning and Clustering Understand how to create partitioned and clustered tables to improve query performance and reduce costs.

Creating a partitioned and clustered table:

SQL

CREATE TABLE my_dataset.sales (
  transaction_id INT64,
  transaction_date DATE,
  customer_id INT64,
  product_id INT64,
  quantity INT64
)
PARTITION BY transaction_date
CLUSTER BY customer_id, product_id;

5. Access Control Learn how to manage access controls for datasets and tables, including granting and revoking permissions.

Granting access to a dataset:

SQL

GRANT SELECT ON my_dataset TO user@example.com;

6. Query Optimization Familiarise yourself with best practices for optimising query performance, such as using query cache, reducing data scanned, and minimising the amount of data transferred. Using a partitioned table to optimise a query:

SQL

SELECT *
FROM my_dataset.sales
WHERE transaction_date BETWEEN '2021-01-01' AND '2021-01-31';

7. Cost Control Be aware of BigQuery's pricing model and learn how to estimate query costs, control daily costs with custom quotas, and set up budget alerts.

Estimating query costs using the BigQuery web UI: 1. Navigate to the BigQuery Console. 2. Enter your SQL query in the Query editor. 3. Click on the "Validate" button to check the syntax and to see the estimated amount of data that will be processed.

8. BigQuery ML Understand how to create, train, evaluate, and deploy machine learning models using BigQuery ML.

Creating and training a linear regression model:

SQL

CREATE OR REPLACE MODEL my_dataset.regression_model
OPTIONS(model_type='linear_reg') AS
SELECT
  input_features,
  target_variable
FROM
  my_dataset.training_data;

9. Integrations Learn how to integrate BigQuery with other Google Cloud services like Dataflow, Pub/Sub, and Cloud Storage, as well as external tools like Data Studio, Looker, Colab and Jupyter Notebooks.

Exporting a BigQuery table to Google Cloud Storage:

Bash

bq extract --destination_format CSV my_dataset.my_table gs://my_bucket/my_table.csv

10. UDFs (User-Defined Functions) Learn how to create and use UDFs in SQL queries to perform custom data processing tasks that are not easily achievable using standard SQL functions.

Creating and using a UDF:

SQL

CREATE TEMPORARY FUNCTION my_udf(x INT64, y INT64)
RETURNS INT64
LANGUAGE js AS """
  return x + y;
""";

SELECT my_udf(1, 2) AS result;

11. Error Handling Be familiar with common errors and their solutions in BigQuery, such as resource limitations, quota issues, and syntax errors.

Handling division by zero error:

SQL

SELECT IFNULL(SAFE_DIVIDE(numerator, denominator), 0) AS result
FROM my_dataset.my_table;

12. Data Transfer Service (DTS) Understand how to use the BigQuery Data Transfer Service to automate data movement between BigQuery and other Google Cloud services or external data sources.

Using BigQuery Data Transfer Service in the Google Cloud Console: 1. Navigate to the BigQuery Console. 2. Click on "Transfers" in the left-hand menu. 3. Click on "Create Transfer" and follow the prompts to configure your data transfer.

13. Scheduled Queries Learn how to create, manage, and troubleshoot scheduled queries for recurring data processing tasks.

Creating a scheduled query: 1. Navigate to the BigQuery Console. 2. Enter your SQL query in the Query editor. 3. Click on the "Schedule Query" button, configure the scheduling options, and save.

14. Monitoring and Logging Get comfortable with BigQuery monitoring and logging features, such as using Stackdriver Logging, Monitoring, and Trace, to gain insights into query performance and resource usage.

Viewing query logs in Cloud Logging: 1. Navigate to the Google Cloud Console. 2. Go to "Logging" > "Logs Explorer". 3. Use the following query to filter BigQuery logs: resource.type="bigquery_resource"

By mastering these concepts and tools, you'll be well-equipped to use BigQuery effectively and efficiently for various data processing and analytical tasks.