BigLake: Multi-Cloud Analytics on Google Cloud

For data engineers preparing for the Professional Data Engineer certification, understanding how to design analytics solutions that span multiple cloud platforms is increasingly important. Organizations often store data across Google Cloud, AWS, and Azure, creating silos that complicate analysis and governance. BigLake multi-cloud data analytics addresses this challenge by providing a unified layer that enables querying and analysis regardless of where data resides.

This capability is relevant for exam scenarios involving hybrid cloud architectures, data governance across platforms, and designing analytics solutions that need to access data from various sources without creating redundant copies. Understanding how BigLake integrates with BigQuery while extending its capabilities to external data sources is essential for building efficient multi-cloud data platforms.

What BigLake Is

BigLake is a storage engine within Google Cloud that enables unified analytics across data stored in multiple clouds and formats. It extends BigQuery's capabilities beyond native Google Cloud Storage and BigQuery tables to include data residing in AWS S3, Azure Blob Storage, and other external sources.

The primary purpose of BigLake is to eliminate data silos by creating a single access layer that works across different storage platforms. Instead of copying data into BigQuery or maintaining separate analytics systems for each cloud provider, BigLake allows you to query and analyze data in place while maintaining consistent security policies and performance optimizations.

BigLake achieves this through BigLake tables, which are metadata objects that reference external data sources. These tables bring BigQuery's fine-grained access controls, caching mechanisms, and query optimization to data that lives outside Google Cloud's native storage systems.

How BigLake Works

BigLake operates as a bridge between BigQuery's query engine and external data sources. When you create a BigLake table, you define metadata that points to data stored in Cloud Storage, AWS S3, or Azure Blob Storage. This metadata includes the location, format (such as Parquet, Avro, or ORC), and schema information.

When a query runs against a BigLake table, the BigQuery engine uses this metadata to access the external data through BigLake connectors. These connectors handle the communication with different cloud storage systems, retrieving only the data needed for the query. BigLake applies column-level security policies and row-level filters before returning results, ensuring that access controls are enforced regardless of the data's physical location.

The architecture includes a caching layer that stores frequently accessed data and query results. This caching improves performance for repeated queries without requiring data movement. When you run a query against a BigLake table pointing to an AWS S3 bucket, BigLake retrieves the necessary files, applies security policies, caches relevant portions, and returns results through the BigQuery interface.

Here's how you create a BigLake table over data in Cloud Storage:

CREATE EXTERNAL TABLE `my_project.my_dataset.customer_data`
WITH CONNECTION `my_project.us.my_biglake_connection`
OPTIONS (
  format = 'PARQUET',
  uris = ['gs://my-bucket/customers/*.parquet']
);

This creates a table that references Parquet files in Cloud Storage through a BigLake connection, enabling BigQuery queries while maintaining the data in its original location.

Key Features and Capabilities

BigLake provides fine-grained security controls that extend beyond traditional file-level permissions. You can define column-level and row-level access policies on BigLake tables, controlling which users see which data even when the underlying storage system only supports file-level access. A hospital network could store patient records in AWS S3 while using BigLake to ensure that billing staff only see financial columns and research teams only access anonymized demographic data.

The performance optimization features include intelligent caching and metadata management. BigLake caches query results and frequently accessed data partitions, reducing latency for subsequent queries. For a trading platform querying market data stored across multiple cloud providers, this caching can significantly improve dashboard refresh times without duplicating terabytes of historical data.

BigLake supports multiple analytics engines beyond BigQuery. Apache Spark, Presto, Trino, and other open-source tools can query BigLake tables through standardized connectors. This means a data science team comfortable with Spark notebooks in Vertex AI can access the same BigLake tables that analysts query through BigQuery, all while respecting the same security policies.

The data sharing capabilities allow you to expose BigLake tables through Analytics Hub, Google Cloud's data exchange platform. A weather data provider could maintain authoritative datasets in their own multi-cloud infrastructure while sharing curated views with subscribers who query through BigQuery.

Why BigLake Matters for Multi-Cloud Analytics

Organizations adopting multi-cloud strategies face significant challenges in maintaining consistent analytics capabilities. Without BigLake, a freight company with shipment data in GCP and legacy logistics data in Azure would need separate analytics stacks, duplicated security policies, and custom integration code to generate unified reports.

BigLake addresses this by providing a single query interface with consistent security and governance. The freight company can create BigLake tables referencing both GCP and Azure storage, define unified access policies, and enable analysts to write queries that join data across clouds without understanding the underlying complexity.

The cost benefits come from eliminating data duplication and reducing egress charges. A mobile game studio might store raw game events in AWS S3 while running analytics in BigQuery. Instead of copying petabytes to Cloud Storage and paying egress fees, they create BigLake tables over the S3 data and query it directly. BigLake's caching minimizes repeated data transfers while maintaining query performance.

For regulatory compliance, BigLake enables centralized governance without centralizing data storage. A multinational bank might be required to keep European customer data in Azure's European regions while storing Asian customer data in GCP. BigLake allows unified analytics and consistent security policies while respecting data residency requirements.

When to Use BigLake

BigLake is the right choice when you need to query data stored across multiple cloud providers or when you want BigQuery's advanced features on external data. If your organization has committed to AWS for certain workloads but wants to use Google Cloud for analytics, BigLake bridges this gap without requiring data migration.

Use BigLake when fine-grained security matters more than what the underlying storage system provides. A telehealth platform storing appointment recordings in S3 might only have bucket-level permissions in AWS, but through BigLake they can enforce column-level access controls that hide personally identifiable information from certain user groups.

BigLake works well for data lake architectures where you maintain raw data in object storage and want to provide governed analytics access. A climate research institute with petabytes of sensor data in Cloud Storage can use BigLake tables to provide structured access to researchers while maintaining the flexibility of the original file formats.

However, BigLake isn't necessary when all your data already resides in native BigQuery tables and you have no multi-cloud requirements. A startup running entirely on GCP with data in BigQuery native storage should use standard BigQuery tables, which offer better performance and simpler management.

BigLake may not be ideal for extremely latency-sensitive applications where milliseconds matter. While BigLake's caching helps performance, querying external data will always have slightly higher latency than native BigQuery storage. A high-frequency trading system requiring sub-second query responses should keep critical data in native BigQuery tables.

Implementation Considerations

Setting up BigLake requires creating connections to external cloud resources. For AWS, you need to configure IAM roles and cross-account access. For Azure, you set up service principals and grant appropriate permissions. Google Cloud manages these connections securely, storing credentials that BigLake uses to access external data.

Here's how to create a connection to AWS S3:

bq mk --connection --location=us --project_id=my-project \
  --connection_type=CLOUD_RESOURCE \
  --properties='{"accessRole":{"iamRoleId":"arn:aws:iam::123456789:role/biglake-role"}}' \
  aws_connection

After creating the connection, you reference it when defining BigLake tables. The connection handles authentication and authorization with the external cloud provider.

Performance depends heavily on data format and partitioning. Columnar formats like Parquet and ORC perform significantly better than JSON or CSV because BigQuery can read only the columns needed for a query. A solar farm monitoring system with time-series data should partition files by date and use Parquet format to enable efficient time-range queries:

CREATE EXTERNAL TABLE `energy_project.sensors.solar_readings`
WITH CONNECTION `energy_project.us.aws_connection`
OPTIONS (
  format = 'PARQUET',
  uris = ['s3://solar-data/readings/year=*/month=*/day=*/*.parquet'],
  hive_partition_uri_prefix = 's3://solar-data/readings',
  require_hive_partition_filter = true
);

This configuration enables partition pruning, where BigQuery only reads files matching the query's time range filter.

Cost management requires understanding query pricing and data transfer charges. BigLake queries are billed like standard BigQuery queries based on bytes scanned. However, querying data in other clouds may incur egress charges from those providers. Monitor your query patterns and use BigLake's caching to minimize repeated data transfers. Setting appropriate cache expiration and using materialized views can reduce costs for frequently accessed data.

Integration with Other GCP Services

BigLake integrates deeply with BigQuery, appearing as standard tables in the BigQuery UI. Analysts write SQL queries against BigLake tables using the same syntax as native tables. This integration means minimal training for teams already familiar with BigQuery.

Vertex AI can access BigLake tables for machine learning workflows. A subscription box service could use BigQuery ML to build customer churn models on data stored across multiple clouds, with BigLake providing unified access. The training data remains in its original location while Vertex AI reads it through BigLake tables.

Dataflow pipelines can write to Cloud Storage buckets that BigLake tables reference. A payment processor might use Dataflow to process transaction streams, writing results to partitioned Parquet files in Cloud Storage. BigLake tables over these files provide immediate query access without additional ETL steps.

Analytics Hub enables sharing BigLake tables as data products. A logistics company could share shipment tracking data with partners, controlling access through BigLake's security policies while keeping the authoritative data in their own multi-cloud infrastructure.

Looker and Data Studio connect to BigLake tables like any BigQuery table, enabling visualization and reporting. Business users access dashboards without knowing whether data comes from GCP, AWS, or Azure storage.

Bringing It All Together

BigLake solves the fundamental challenge of multi-cloud analytics by providing unified access to data regardless of location. It extends BigQuery's security, performance, and governance capabilities to external data sources, eliminating the need for redundant copies and separate analytics stacks. The integration with GCP services like Vertex AI and Dataflow makes BigLake a natural choice for organizations building hybrid cloud data platforms.

Understanding BigLake's architecture, capabilities, and appropriate use cases helps you design analytics solutions that balance flexibility, performance, and cost. Whether you're dealing with legacy data in other clouds, meeting data residency requirements, or building a modern data lake architecture, BigLake provides the tools to maintain unified analytics without compromising on governance or user experience.

For those preparing for the Professional Data Engineer certification, BigLake represents Google Cloud's approach to breaking down data silos in hybrid environments. The ability to design solutions using BigLake tables, connections, and security policies is valuable for exam scenarios involving multi-cloud architectures. Readers looking for comprehensive exam preparation can check out the Professional Data Engineer course.