Unleashing the Power of AI with a BigQuery Data Warehouse

ByJordan Brown

In the age of artificial intelligence (AI) and machine learning (ML), data is the new oil that fuels innovation. However, deriving insights from massive, complex datasets requires a scalable and intelligent data warehousing solution. Enter Google BigQuery – a serverless, highly-scalable, and cost-effective cloud data warehouse that leverages AI to super-charge analytics workloads.

As an AI/ML expert who has built data platforms for multiple Global 2000 companies, I‘ve seen firsthand how BigQuery can democratize machine learning and accelerate time-to-value. In this guide, I‘ll share best practices for designing a modern BigQuery data warehouse that can power your organization‘s AI initiatives.

BigQuery: The Brains Behind Smart Analytics

BigQuery is not your average data warehouse. Under the hood, it utilizes cutting-edge AI and ML technologies to optimize every aspect of storing, processing, and analyzing data.

Built-in Machine Learning with BigQuery ML

What if you could build ML models directly where your data lives without moving it? That‘s the idea behind BigQuery ML, which enables data analysts and data scientists to create and execute ML models using SQL, right inside BigQuery.

BigQuery ML supports several types of models, including:

  • Linear regression for forecasting sales and revenue
  • Binary and multi-class logistic regression for classification problems
  • K-means clustering for customer segmentation
  • Matrix factorization for product recommendations
  • Time series analysis for demand forecasting
  • TensorFlow for importing deep learning models
  • AutoML tables for automatically training models

Here‘s an example of building a customer churn model to predict the likelihood of a customer cancelling their subscription:

CREATE MODEL `churn_model`
OPTIONS(model_type=‘logistic_reg‘) AS
SELECT
  IF(number_of_days_since_last_trip > 30, 1, 0) AS churn,
  customer_id,
  total_trips,
  number_of_days_since_signup,
  number_of_days_since_last_trip
FROM `customers.trips_data`

This model can then be used to make predictions on new data:

SELECT
  customer_id,
  number_of_days_since_last_trip,
  predicted_churn_probs[ORDINAL(1)] AS churn_prob
FROM
  ML.PREDICT(MODEL `churn_model`,
    (
    SELECT
      customer_id,
      number_of_days_since_last_trip
    FROM `customers.trips_data`
    )
  )

By enabling ML in the database, BigQuery accelerates model development, increases model accuracy, and reduces infrastructure complexity. (source)

Integration with Vertex AI

For more advanced AI/ML use cases, BigQuery integrates with Vertex AI, Google Cloud‘s unified platform for building and deploying ML models. Data scientists can seamlessly access BigQuery data in Vertex AI Workbench notebooks to explore data, engineer features, train models, and evaluate results. (source)

Vertex AI also supports BigQuery as a batch prediction source and destination for deploying models into production. You can store predictions in BigQuery for further analysis or serve them to applications via an API endpoint. This enables an end-to-end, closed-loop ML workflow powered by BigQuery.

Intelligent Performance Optimization

BigQuery employs several AI techniques under-the-hood to optimize query performance and cost. For instance, BigQuery uses ML to automatically provision and scale slots (units of computational capacity) based on workload patterns. It also caches results of frequently-accessed queries in BI Engine memory to reduce latency and cost.

BigQuery‘s query optimizer uses statistics, data skew, and workload history to determine the most efficient execution plan for a SQL query. It can push down predicates, prune partitions, and rearrange joins to minimize the data scanned. (source)

Designing an AI-Ready Data Warehouse

To unleash BigQuery‘s full potential for AI/ML, you need to design your data warehouse strategically. Here are some best practices:

Ensure High-Quality, Trusted Data

ML models are only as good as the data they are trained on. Inconsistent, duplicate, or missing data can negatively impact model accuracy. That‘s why it‘s critical to establish a strong data governance program that ensures data quality, security, and discoverability.

BigQuery can help by providing built-in data cleansing functions, data retention policies, and column-level security. However, you‘ll also need an enterprise data catalog like Dataplex to curate data, track lineage, and apply consistent taxonomy and metadata across all your data assets. (source)

Create Reusable Feature Sets

Feature engineering is the process of transforming raw data into meaningful signals that can be used as inputs to ML models. This requires domain expertise to identify relevant attributes and create derived features.

Rather than doing this work repeatedly for each model, consider building reusable feature sets in BigQuery. Materialize feature sets as precomputed tables or views that can be shared across teams and models. Document your feature sets in a feature store like Vertex AI Feature Store for discovery and serving. (source)

Embrace a Unified Star Schema

To make data easy to find and query, structure it in a denormalized star schema. This involves organizing data into fact and dimension tables centered around business processes.

For example, a retail sales schema may contain these fact tables:

  • Web sales
  • Store sales
  • Mobile sales

And these dimension tables:

  • Customer
  • Product
  • Location
  • Promotion

Fact tables contain measurements (e.g. quantity sold, revenue) and foreign keys to dimension tables. Dimension tables contain attributes (e.g. customer demographics, product category) that can be used to filter and group facts.

Standardizing on a unified schema allows any BI tool to query data without complex joins. It fosters reuse of common dimensions across facts for consistent reporting. (source)

Leverage External Tables and Partitioning

BigQuery can query external data sources like CSV files, Avro, ORC, or Parquet in Cloud Storage or Bigtable databases directly without ingestion. This is useful for accessing raw files or data in external systems. (source)

To optimize query performance and cost, partition and cluster fact tables. Partitioning organizes table data into granular chunks based on a date or timestamp column. Clustering co-locates related rows based on one or more columns. This allows the query engine to scan only relevant data. (source)

Here‘s an example of creating an ingestion-time partitioned and clustered table:

CREATE TABLE retail.sales
(
  date DATE,
  store_id INT64,
  product_id INT64,
  quantity INT64,
  revenue DECIMAL
)
PARTITION BY DATE(date)
CLUSTER BY store_id, product_id

Now when you query this table with a filter on date and store_id, BigQuery can eliminate scanning unnecessary partitions and clusters.

Aggregate Data into Tiers

Not all data is accessed equally. Users may frequently query recent, granular data for operational reporting but query historical, summary data for strategic analysis.

To optimize performance and cost, aggregate data into tiers based on usage patterns. For example:

  • Landing tier contains raw, unprocessed data
  • Silver tier contains cleansed, conformed data
  • Gold tier contains aggregated, business-level metrics

You can materialize these tiers as separate datasets in BigQuery. Then automate the flow of data between tiers using orchestration tools like Cloud Composer or Dataform. (source)

Real-World AI + BigQuery Use Cases

Organizations of all sizes and industries are using BigQuery to power AI use cases. Here are some examples:

Predictive Maintenance at Airbus

Airbus Defense and Space uses BigQuery to predict when aircraft components will fail before it happens. They stream real-time sensor data from helicopters to BigQuery. Then they apply ML models to identify anomalies and estimate remaining useful life. This reduces unplanned maintenance events and extends asset life. (source)

Demand Forecasting at Albertsons

Albertsons, a $60B grocery chain, uses BigQuery to predict consumer demand and optimize supply chain logistics. They built an ensemble of neural networks in BigQuery ML to forecast sales at the store-item-day level. This improved forecast accuracy by 30% and reduced inventory by 10%. (source)

Fraud Detection at HSBC

HSBC, one of the world‘s largest banks, uses BigQuery to detect payment fraud in real-time. They stream 6 TB of transaction data per day to BigQuery and run deep learning models to score transactions as they occur. This identifies suspicious activity within milliseconds so the bank can block fraudulent payments. (source)

Recommendation Engines at The Home Depot

The Home Depot uses BigQuery to power personalized product recommendations for 2 billion customer visits per year. They join clickstream, purchase history, and product catalog data to create customer profiles. Then they train collaborative filtering models to suggest relevant products to customers based on similar users‘ behavior. This has increased conversion rates and customer loyalty. (source)

Comparing BigQuery to Alternatives for AI/ML

While BigQuery is a leader in cloud data warehousing, let‘s see how it stacks up to alternatives for AI and ML:

Capability BigQuery Snowflake Redshift Synapse
In-database ML Y N N Y
AutoML Y N N Y
Notebooks Y Y Y Y
Distributed DL N N N Y
ML Ops Y Y Y Y
Autonomous optimization Y Y N N
Serverless Y Y N N
Real-time predictions Y N N Y

As you can see, BigQuery offers unique in-database ML and AutoML capabilities that enable data analysts to build models using only SQL. However, it lacks the distributed deep learning capabilities of Synapse Spark pools. (source)

In terms of pricing, BigQuery charges for storage and data scanned, while Snowflake charges for storage and compute time. Redshift charges for instance size, while Synapse charges for serverless queries and provisioned clusters. (source)

To illustrate BigQuery‘s performance at scale, here is a comparison of scan speeds:

Rows Scanned BigQuery Redshift Snowflake
1B 2.2 sec 5.76 sec 2.15 sec
10B 16.35 sec 27.54 sec 14.29 sec
100B 94.65 sec 230.48 sec 123.94 sec

As you can see, BigQuery outperforms Redshift as data volumes grow, and is comparable to Snowflake at 100B rows. (source)

Ultimately, the choice of data warehouse depends on your specific needs, but BigQuery is a strong contender for AI/ML workloads.

Getting Started with BigQuery AI

To experience BigQuery‘s AI capabilities firsthand, you can start a free trial of Google Cloud and use the $300 credit to experiment with BigQuery and Vertex AI.

Here are some resources to guide your learning journey:

You can also leverage partner solutions like Informatica, Fivetran, Qlik, and Tableau to accelerate your data warehouse modernization journey.

The Future is AI-Ready

As Debanjan Saha, GM and VP of Data Analytics at Google Cloud, puts it:

"The future of data warehousing is machine learning. Just as SQL changed the game for business intelligence, ML will fundamentally transform the way we extract insights from data. BigQuery is at the forefront of this revolution, making ML accessible to every data practitioner." (source)

By building your AI-powered data warehouse with BigQuery today, you‘ll be ready to capitalize on the business opportunities of tomorrow. The future is AI-ready, are you?

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