Breaking Down Data Silos with BigQuery Omni and BigLake
Imagine you’re managing data for a global retail chain. Your business has expanded its presence across the globe, and with that comes the need to adopt a multi-cloud strategy.
Here’s the setup:
Customer data is securely stored on Google Cloud Storage (GCS).
Transaction logs sit on AWS S3, closer to regional services for faster processing.
Marketing campaign data lives on Azure Blob Storage, managed by an external agency.
At first glance, this sounds like an efficient system—leveraging the best of each cloud provider. But in reality, it’s a logistical nightmare. The data is siloed, scattered across platforms that don’t naturally talk to each other.
When the marketing team asks for insights to personalize campaigns, or the finance team wants to analyze transaction trends, here’s what happens:
You spend hours building complex ETL pipelines.
Data transfer costs skyrocket as you move datasets between clouds.
Compliance teams start ringing alarms about cross-border data movement risks.
It’s like trying to cook a meal, but the ingredients are scattered across three kitchens in different countries. Exhausting, right?
Enter BigQuery Omni and BigLake
Here’s where BigQuery Omni and BigLake step in to save the day. These tools make it possible to analyze data across clouds without moving it.
Let’s break it down.
BigQuery Omni: Analytics Across Clouds
Think of BigQuery Omni as your passport to accessing data wherever it lives. With Omni, you can run queries across Google Cloud, AWS, and Azure, as if all your data were in one place.
How does it work?
BigQuery Omni uses Anthos to deploy BigQuery’s analytics engine close to your data. Whether your data resides in AWS S3 or Azure Blob Storage, it stays where it is, while BigQuery does the heavy lifting.
Why is it a game-changer?
No Data Movement: Forget about costly transfers and compliance risks. Analyze data in place.
One Query for All Clouds: Write a single SQL query and combine datasets from multiple clouds seamlessly.
Retail Example:
Let’s return to our global retail chain:
Analyze customer preferences stored in GCS.
Join transaction logs from AWS S3.
Measure marketing campaign success from Azure Blob Storage.
All of this happens in one unified query—no tedious ETL pipelines, no data duplication, no silos.
BigLake: Uniting Lakes and Warehouses
While BigQuery Omni breaks down barriers across clouds, BigLake simplifies working with diverse data formats within and outside of Google Cloud.
How does it work?
BigLake adds a metadata layer to external data formats like Parquet, ORC, and CSV. This makes them instantly queryable using BigQuery, while maintaining access control and governance.
Why is it powerful?
Unified Governance: Consistent access policies across your data lakes and warehouses.
Cost Efficiency: Query raw data directly, skipping the need to load everything into BigQuery.
Retail Example:
Imagine the retailer has raw clickstream data stored as Parquet files in GCS. With BigLake:
They can join this data with sales records in BigQuery to generate personalized recommendations.
They avoid duplicating data, slashing storage and processing costs.
Omni + BigLake: A Perfect Pair
When you bring BigQuery Omni and BigLake together, you get the best of both worlds:
1️⃣ Multi-cloud flexibility to query data wherever it resides.
2️⃣ Unified governance and compliance for structured and unstructured data.
3️⃣ Cost efficiency, thanks to reduced ETL complexity and no unnecessary data movement.
For our retailer, this means a 360-degree view of their customers, faster insights, and significantly lower costs. It’s like turning a scattered, chaotic pantry into a streamlined, world-class kitchen.