Chapter 1: Introduction to BigQuery and Cloud Spanner

In a recent article, I discussed how Google is evolving BigQuery into a robust platform for big data analytics. With integrations into BigTable, BigLake, and Analytics Hubs, BigQuery has significantly enhanced its capabilities as a Software as a Service (SaaS). The latest development involves the integration of Cloud Spanner with BigQuery, which brings exciting opportunities for data professionals.

Cloud Spanner is Google's globally distributed NewSQL database, designed for use within the Google Cloud Platform. It serves as the successor to Google's BigTable and MegaStore databases.

Chapter 2: Real-Time Data Access

Google has announced that BigQuery now allows for real-time querying of data stored in Cloud Spanner without the need to copy or relocate the data. This is part of their Zero-ETL initiative, which they previously implemented with BigTable and also integrated into Google BigLake.

This approach addresses several limitations associated with traditional Extract, Transform, Load (ETL) processes:

• Enhanced data freshness, providing up-to-date insights for businesses without the delays typical of conventional methods.
• Cost efficiency, as it eliminates the need to store the same data in multiple locations, which is often the case with large datasets in BigTable.
• Reduced overhead in monitoring and maintaining ETL pipelines.

As a Data Scientist or Analyst, this means you can seamlessly access a variety of (Big) Data tools across different platforms (such as AWS and Azure) using SQL for real-time queries. Additionally, the integration with Google Data Studio facilitates business users' access to data insights.

Chapter 3: Google Analytics Hub Integration

The Google Analytics Hub, built on BigQuery, employs a publish and subscribe model for dataset management. To query data from external sources using BigQuery SQL, it is essential to designate an external data source.

So, the noteworthy advancement is that BigQuery is increasingly functioning as a cross-platform analytical tool. Given the recent developments, it will be interesting to see what further innovations Google will unveil in the coming months.

If you frequently utilize GCP and BigQuery, you may find the following articles and updates beneficial:

• BigQuery now supports Query Queues
• Utilizing the Load Data Statement in Google BigQuery
• Enhancements in Data Security within BigQuery's Data Warehouse
• Three Major Announcements from Google