Data Transformation and Feature Engineering in BigQuery

Google Big Query @

BigQuery, Google Cloud’s fully-managed data warehouse, provides powerful tools for data transformation and feature engineering on large datasets. In this comprehensive guide, we’ll explore various techniques and functions available in BigQuery to manipulate and engineer features, enabling you to derive valuable insights from your data.

1. Data Transformation Techniques

a. Data Filtering

Filtering data based on specific conditions is a fundamental step in data transformation.

FROM `project.dataset.table`
WHERE column_name = value;

b. Data Aggregation

Aggregating data allows you to summarize information based on certain criteria.

SELECT category, SUM(revenue) AS total_revenue
FROM `project.dataset.sales`
GROUP BY category;

2. Feature Engineering

a. Creating New Features

Deriving new features from existing ones can enhance the predictive power of your models.

       DATE_DIFF(CAST(birth_date AS DATE), CURRENT_DATE(), YEAR) AS age
FROM `project.dataset.customers`;

b. Feature Scaling

Scaling features ensures that they have a comparable range, which is crucial for certain machine learning algorithms.

       (age - AVG(age) OVER ()) / STDDEV(age) OVER () AS standardized_age
FROM `project.dataset.customers`;

3. Joining and Combining Data

a. Inner Joins

Inner joins combine rows from two or more tables based on a related column between them.

SELECT orders.*,
FROM `project.dataset.orders` AS orders
JOIN `project.dataset.customers` AS customers
ON orders.customer_id = customers.customer_id;

b. Union All

Union all combines the results of two or more SELECT statements into a single result set.

SELECT * FROM `project.dataset.table1`
SELECT * FROM `project.dataset.table2`;

4. Handling Missing Values

a. Filtering Out Missing Values

Filtering out records with missing values ensures the quality of your data.

FROM `project.dataset.table`
WHERE column_name IS NOT NULL;

b. Imputing Missing Values

Imputing missing values with appropriate substitutes can prevent loss of information.

       IFNULL(column_name, 'N/A') AS imputed_column
FROM `project.dataset.table`;

Data transformation and feature engineering are essential steps in the data analysis pipeline, enabling you to extract meaningful insights and build robust machine learning models. With BigQuery’s powerful capabilities for handling large datasets, you can efficiently perform these tasks at scale.

BigQuery import urls to refer

Author: user