5 reasons why Google Cloud BigQuery is perfect for building a data platform

🌟 Why is Google BigQuery the Ideal Solution for Data Analytics?

Choosing a technology stack is one of the first steps in any new data project. Datumo launched its first product seven years ago. Our product, Storyteller, was a cloud-based analytics platform built on open-source technologies. You can read more about our story in Datumo: A Journey From a Product Company to Experts in Data Analytics.

One of our first major decisions in Datumo was to choose a cloud provider to build our data analytics product. After evaluating various options, Google Cloud Platform (GCP) stood out, primarily because of BigQuery. Let’s explore why the Google Cloud BigQuery ecosystem might be the perfect fit for your next data analytics project.

1️⃣ BigQuery: A Fully Serverless Data Warehouse

✅ Low entry level for less technical users

✅ Reduced maintenance costs

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Unlike other data warehousing solutions, BigQuery operates on a fully serverless architecture. This eliminates the need to set up and manage clusters of computing resources. There’s no need to configure or optimize infrastructure to match query workloads - BigQuery’s serverless architecture automatically provisions and allocates resources in the background. By leveraging a serverless architecture and removing infrastructure management complexities, BigQuery allows users to focus on extracting insights rather than dealing with operational overhead.

At Datumo, we have observed many organizations struggling to set up Apache Spark clusters, often leading to significant overspending. BigQuery supports seamless querying for data analysts and business users without requiring deep engineering expertise.

🔍 How Does Google BigQuery Architecture Work?

Under the hood, Google BigQuery architecture leverages distributed computing architecture that parallelizes query execution across multiple servers. It dynamically allocates compute resources based on query complexity and dataset size. Queries consume processing power in units called slots. Slots are allocated at query submission and released upon query completion.

2️⃣ Cost Efficiency: How Does Google BigQuery Pricing Work?

✅ You pay for actual consumption, not idle resources 

✅ No changes to table partition for 90 days?! Storage costs are significantly reduced

✅ Free batch ingestion from Cloud Storage

💰 Google BigQuery Pricing Models

BigQuery offers two compute pricing models:

• On-demand pricing (pay per TB) - Charges are based on the number of bytes processed by each query.
• Capacity pricing (pay per slot-hour) - Charges are based on compute capacity measured in slots over time. This model benefits from BigQuery editions and allows for autoscaling and purchase slot commitments which provide dedicated capacity always available for your workloads at a lower price.

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Organizations can mix these pricing models. Some analytic projects may use on-demand pricing, while others may benefit from slot-based pricing for cost predictability. The user experience in both models is the same.  The on-demand model is best for compute-intensive workloads that take minutes to complete on small or medium sized datasets while slot pricing is ideal for data-intensive workloads that perform lightweight operations on large datasets or when the organisation requires predictable billing. You can choose the optimal pricing model based on BigQuery monitoring.

In the on-demand model, Google Cloud allocates 2,000 slots per project, the equivalent of 2,000 independent processing units! This makes it highly efficient for large-scale use cases. In Datumo we have built hundreds of data pipelines which process hundreds of terabytes daily, benefiting from the efficiency of BigQuery data warehouse cost structures. In this model, you only pay for bytes processed by query, not idle compute resources like clusters. In contrast, we have seen organizations spend large amounts on massive analytic clusters in other data warehouse solutions, with utilization rates below 10%.

📦 BigQuery Data Storage Pricing

Similar to compute, BigQuery offers two storage pricing models:

• Logical Storage – based on uncompressed data size.
• Physical Storage – based on compressed data size.

The storage billing model you choose only impacts your pricing, it doesn't impact BigQuery performance. The data is always compressed. We observed 2 - 8 times size reduction when using a physical model. 

So what’s the catch? In the physical model, you have to pay extra for time-travel and fail-safe storage that is already included in the logical model. In most cases the physical model is more cost efficient than the logical one. The Logical model is better when the table is updated regularly. Similar to the compute pricing model you can mix these two pricing models by selecting the appropriate model at the BigQuery dataset level.

Both models have long-term storage optimization. If a table or table partition is not modified for 90 consecutive days, the price automatically drops by approximately 50% with no difference in performance, durability, or availability.

🧰 Data ingestion

How about data ingestion? The BigQuery ingestion process is fully serverless. You’re not charged for loading data from Cloud Storage, and there’s no need to provision compute resources. In practice, you can ingest terabytes of data from GCS at no cost.

3️⃣ Instant query execution with BigQuery

✅ Don’t waste your precious time on cluster creation

BigQuery delivers near-instantaneous query execution times. Your queries typically start running within one to three seconds, thanks to the shared compute resource pool maintained by Google Cloud. This drastically improves user experience, there's no need to wait several minutes for your cluster recreation after your meeting. Additionally, you don’t incur unnecessary costs related to cluster creation times. This makes BigQuery an ideal choice as a data warehouse for the most popular BI tools, such as Looker, PowerBI, and Tableau.

4️⃣ Real-time data warehouse

✅ Real-time support by design

BigQuery natively supports near-real-time data analytics thanks to Vortex, a stream-oriented storage engine. The BigQuery Storage Write API enables the ingestion of thousands of events per second with minimal latency. How about pricing? The first 2 TiB per month are free.

This makes BigQuery an ideal solution for building systems that rely on real-time data, such as Customer Data Platforms. The Datumo team utilized BigQuery and Dataflow for one of Poland’s largest retailers, to implement an ingestion job from Apache Kafka to BigQuery capable of handling over 30,000 events per second in real-time.

5️⃣ BigQuery is more than just a data warehouse

✅ Comprehensive solution

✅ One license fee provided by one provider equals cost savings

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During the last few years Google Cloud has been consolidating data services under the BigQuery umbrella. Through BigQuery Studio, users can now:

• Run Apache Spark jobs.
• Use LLMs for content generation.
• Build machine learning models using SQL with BigQuery ML.
• Execute real-time processing with continuous queries.

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🔗 Seamless Integration with Google Cloud Data Services

BigQuery’s ecosystem extends beyond its core warehousing capabilities. It seamlessly integrates with other Google Cloud services, enhancing its usability for advanced data processing and AI-driven analytics:

• Vertex AI: Seamlessly integrates with BigQuery for end-to-end machine learning workflows. Users can preprocess data in BigQuery, train ML models directly on BigQuery data, and deploy them in Vertex AI. Additionally, the Vertex AI Feature Store has native integration with BigQuery.
• Dataproc: Enables Apache Spark users to process data while treating BigQuery as a database. At Datumo, we find Spark + BigQuery an ideal combination for data pipelines where Python or Scala are used.
• Dataflow: A powerful streaming and batch processing engine. It allows you to build real-time data pipelines that ingest and process data before storing it in BigQuery.
• Looker Studio and Looker: Allows users to create interactive dashboards and reports directly connected to BigQuery. Looker Studio is a free solution with basic capabilities, while Looker offers more advanced features for enterprises.
• Dataplex: A comprehensive data governance platform that extends BigQuery’s governance capabilities. It offers automated metadata inference, data lineage tracking, data quality monitoring, and governance policy enforcement at scale.

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🔒 Built-in Security & Compliance

A key advantage of using Google Cloud’s native data services is the unified security model. BigQuery ensures robust data protection with:

• Access control with IAM
• Encryption at rest and in transit for maximum data security.
• Column and row-level access controls, ensuring granular data governance.
• Integrated compliance and governance tools through Dataplex for enterprise-grade data security.

💳 Cost Efficiency with a Unified Licensing Model

Beyond its analytical power, Google Cloud provides a significant cost advantage by offering a single cloud provider license. This streamlined pricing model eliminates the need for multiple third-party service agreements, reducing overall operational costs.

🏆 A Comprehensive Platform for All Workloads

Google Cloud Platform extends beyond Data & AI solutions. It provides a comprehensive platform for both operational and analytical workloads, enabling you to build operating systems with Google Kubernetes Engine, Cloud Functions, and managed database services. This unified approach not only simplifies infrastructure management but also enhances cost-effectiveness.

By combining data warehousing, machine learning, governance, and real-time analytics under one ecosystem, BigQuery provides an end-to-end data solution that is both scalable and cost-effective. Organizations looking to build a modern data platform can leverage these capabilities to optimize efficiency and drive innovation.

📋Conclusion – Why Choose BigQuery for Your Data Platform?

BigQuery is more than just a serverless cloud data warehouse - it is a fully managed and cost-efficient ecosystem that supports data and machine learning workloads. From streamlining data warehousing operations to empowering real-time analytics and machine learning initiatives, BigQuery provides a strong foundation for building modern data platforms. Its seamless integration with other Google Cloud services further strengthens its effectiveness, making it an ideal choice for organizations looking to maximize efficiency and innovation in their data-driven endeavors. If you're embarking on a new data project, the BigQuery ecosystem is worthy of serious consideration. 

🚀 Need Help Getting Started with BigQuery?

As a Google Cloud Partner, we specialize in data analytics, pipeline optimization, and cloud architecture. Whether you're just starting or scaling your data platform, we can help!

📩 Contact us today for expert guidance!