Google BigQuery and Microsoft’s Azure Synapse are cloud-based data warehouse solutions provided as SAAS offerings. They provide advanced analytics, querying, and report-making features too. 

Both Microsoft Azure Synapse and Google BigQuery take care of the underlying infrastructure and provisioning so that their customers can focus on their data and the questions they need to be answered. Both platforms can manage huge volumes of data, support advanced analytics via AI/ML, and manage Scalability, Throughput, and Security. 

In this article, We will try to discuss certain features where these two differ, but this is in no way trying to pitch one against the other. Azure BigQuery Comparison intends to facilitate decision-making about which solution suits your current needs. 

Introduction to Google BigQuery

Google BigQuery is a Cloud-based Data Warehouse that provides a Big Data Analytic Web Service capable of handling petabytes of data. It’s intended for large-scale data analysis. It’s split into two sections: storage and query processing. It processes queries with the Dremel Query Engine and saves them to the Colossus File System. These two components are decoupled and can be scaled independently and ad hoc.

Dremel and Google BigQuery use Columnar Storage for quick data scanning, as well as a Tree Architecture for executing ANSI SQL Queries and aggregating results across massive Compute Clusters.

Standard SQL Access can be provided by a variety of Business Intelligence tools that can be integrated with Google BigQuery. Google BigQuery, which employs Distributed Computing Technology within a Serverless Architecture, can process massive amounts of data in record time by utilizing multiple parallel servers to significantly increase processing speed.

Introduction to Microsoft Azure Synapse

Azure Synapse provides an End-to-End Analytics Solution by combining Big Data Analytics, Data Lake, Data Warehousing, and Data Integration into a single unified platform. It can query relational and non-relational data at a petabyte-scale by running fault-tolerant intelligent distributed queries across backend nodes.

Synapse SQL, Spark, Synapse Pipeline, and Studio are the four components of the Azure Synapse architecture. While Synapse SQL aids in SQL query execution, Apache Spark handles batch/stream Big Data processing. Synapse Pipeline provides ETL (Extract-Transform-Loading) and Data Integration capabilities, whereas Synapse Studio is a secure collaborative cloud-based analytics platform that combines AI, machine learning, IoT, and business intelligence.

T-SQL (Transact-Queue Sequential Query Language) analytics are also available through Azure Synapse, with ‘Dedicated’ and ‘Serverless‘ SQL pools for complete analytics and data storage.

Key Features of Microsoft Azure Synapse

Some of the key features of Azure Synapse are listed below.

  • Centralized Data Management Capability: Azure Synapse is powered by Massively Parallel Processing (MPP), which enables it to process massive workloads in fractions of a second.
  • The HTAP’s implementation: This technology allows your system to seamlessly integrate Azure Synapse and Azure Databases.
  • Machine Learning Integration: Using Azure Machine Learning integration capabilities, you can predict and score ML models to generate predictions within your data scope.
  • Data Transfer: After integrating with Azure Data Share, employees can share Data Lake and Data Warehouse either internally or externally.

Azure Synapse vs BigQuery Comparison

The following factors will assist you in deciding which tool is best for your business when comparing Microsoft Azure Synapse and Google BigQuery:

Google BigQuery vs Azure Synapse, Architecture, and Pricing

On the cloud, the storage costs are minimal but the compute/data transfer costs are significant. Both Microsoft Azure and Google BigQuery store data outside of the compute cluster, clearly separating compute from storage. 

Since pricing is dependent on architecture we will discuss both of these together. Two prevalent architectural models for data warehouses support querying and reporting, namely Resource Provisioning and Serverless

In the Resource Provisioning model, you hire a cluster of nodes based on your compute requirements and can transparently increase or decrease your compute capacity, based on your workloads. This model allows you to “pause” your infrastructure when not needed and “resume” it when you want to run your analytics( compute workloads). So this is essentially capacity-based flexible pricing.  

The Serverless model relieves you of the burden of estimating you are computing infrastructure requirements and predicting/managing future usage.  The serverless model will provide an appropriate volume of resources based on your current compute needs, and release resources when you’re not running your analytics. 

Customers are charged for the volume of data processed by their queries. So this is essentially volume-based flexible pricing. 

Azure Synapse follows the advanced resource provisioning model, where their customer has to choose a particular resource mix, e.g. a cluster of compute nodes, at the start. 

One of the nodes is chosen as the control node, and its responsibility is to distribute queries amongst other nodes, to ease parallel processing. As time goes by, data comes in and analytics are run on it, the customer can decide to provision more compute resources or reduce their infrastructure resource consumption, resulting in higher or lesser costs respectively. So, Azure charges you for compute nodes, which they call Data Warehouse Units (DWU). A DWU bundles three resources, namely compute+memory+IO, and is considered as an independently chargeable unit. In other words, A DWU is an abstract, normalized measure of computing resources and performance. Your costs, therefore, will depend on the number of DWUs you provision, which is directly proportional to your computational and performance needs. 

Here are some pointers on how to estimate DWUs needed:- 

  •  Data Ingestion is a network and CPU-Intensive process.
  •  Querying and Aggregations is an I/O and CPU-Intensive process.
  •  Copying data is a CPU, IO, and network-intensive process, as it involves reading data from storage, distributing it across the nodes of the appliance, and writing to storage again. 

So, you can start with a minimum, add capacity as your compute requirements increase if you see a drop in performance. Also, you can provide additional resources for periodic periods of peak activity, as release them later. You can even automate the scale-out/scale inactivity, using Azure Functions, and specify specific times of day/week when the scaling operation can automatically take place. 

There is one more cost associated, which is Storage cost. For storage, Azure charges you $122.88 per terabyte per month. Google BigQuery on the other hand follows the DWaaS — data warehouse as a service model. It’s serverless, and it manages, expands, and reduces your infrastructure usage, based on your currently running workloads. It’s advantageous in terms of not having to think a bit about resource usage and infrastructure management. A lack of transparency here, as BigQuery manages it under its hood. 

So, the first difference between Azure BigQuery, is that they follow Resource Provisioning vs Serverless architecture, respectively.
Also, another difference between Azure BigQuery, is that with Microsoft Azure you have significant control over your billing and can plan your activities to keep things under a budget. 

Ability to Pause and Resume Infrastructure, Automatically

Microsoft Azure gives you the ability to pause your cluster when not needed, and resume it when you need to perform operations/analytics. You’re charged only for the time while your cluster is running.  So, this gives you another way to manage your billing costs. Google BigQuery on the other hand charges you only for the time when you’re running your queries. 

When no queries are running, there are no costs. Google BigQuery, being a serverless solution, does not give you the “pause and resume” facility. 

Elastic Resizing and Pricing Maneuverability

Elastic resizing is the capacity to expand(add) or contract(remove) compute resources based on currently running workloads. You can increase your compute capacity when you’re running heavier workloads, and decrease it when lighter workloads are running, to reduce costs. 

Whereas Microsoft Azure directly gives you the ability to add/remove compute nodes to your cluster, and the ability to automate this if needed; Google BigQuery does it under its hood, all by itself, and claims that its resizing is appropriate and efficient. 

Machine Learning(ML) and Artificial Intelligence(AI) Support 

Both solutions differ in the way they support advanced analytics via AI and ML. AI services enable Sentiment Analysis, Spatial Analysis, Improve Customer Service, Anomaly Detection, understanding its root cause, and extracting insights that are not easily detectable. 

Google BigQuery offers native machine learning capabilities via BigQuery ML that enables users to create and execute machine learning models. 

There is no movement of data and data engineers just need to use provided ML models.  There is no need to know ML frameworks or use complex programming, only the knowledge of SQL skills is sufficient. 

Google BigQuery ML supports models like Linear regression, Multiclass logistic regression, K-means clustering, Matrix Factorization, Deep Neural Network (DNN), etc. 

Google Bigquery ML brings ML to the data instead of the other way round. It democratizes data access and usage, ML models can be trained and accessed just by using SQL. Also, this increases speed, reduces complexity, and brings predictive analytics to aid the business decision-making process. 

The Azure platform has Azure Machine Learning for creating your ML models and Azure Cognitive Services to add cognitive capabilities to apps with AI services. Azure Synapse offers a native PREDICT statement using which users can score machine learning models trained in Azure Machine Learning, Apache Spark, or other frameworks, and derive insights within Synapse without having to perform massive data movement.  

Azure Machine Learning and Azure Cognitive Services provide REST API(s) as well as language-based SDKs for integration. Hence, Azure synapse integrates well with both these solutions to give you the best of both technologies. Data Engineers working on Azure synapse can use AI/ML models built by Data Scientists, seamlessly. 

You just need to create a linked service that collaborates between Azure Synapse and Azure Machine Learning/Azure Cognitive Services. Your data remains secure as there is no data movement, and you can use these as input to your Power BI reports. Data engineers can use the PREDICT method int-SQL, to call an AI model and generate predicted values/scores. 


SELECT data.*, p.Score
FROM PREDICT(MODEL = @myModel,DATA = myDb.myDataTable AS dat) 
WITH (Score FLOAT) AS p;

PREDICT function takes an ONNX (Open Neural Network Exchange) model and data as its inputs. It eliminates the need to move data outside the warehouse. 

Moreover, you can build AI models in Azure synapse, using code-free automated ML services provided by Azure Machine Learning

These models are then run on Apache Spark pools. Apart from creating clear roles/responsibilities between Data Engineers and Data Scientists, it also allows for easy collaboration and development of predictive analytics solutions. 

Machine Learning and AI can also help in enriching your data and improving data practices. 

Backups and Data Protection 

Google BigQuery and Microsoft Azure both provide backup and data protection, but differ here in the way they implement it. Microsoft Azure Snapshot takes automatic snapshots of your data warehouse, to create restore points, which are created frequently over 24 hours. 

Then, like in Windows, you can restore your warehouse to a previous desired state by going back to a restore point. Restore points are available for the previous 7 days. You can also create your snapshots when you think you need to put a restore point. 

Google BigQuery keeps a change history of the last 7 days of operations. Using Google Bigquery change history, you can revert your changes,  to reach the desired state in your warehouse. This backtracking method is more granular and keeps more history. 


Both Google BigQuery and Azure Synapse have a lot to offer. Conduct testing with your data, Ingesting data and Running reports to determine which cloud data warehouse is best for your organization.

We have discussed how Microsoft Azure Synapse and Google BigQuery Comparison differ. We have tried to discuss AI and ML in a bit more detail as these are the technologies for the future, and could be the major deciding factors when you choose your data warehouse.

These vendors keep adding new features/innovations and keep improving their current offerings, hence the above is based on what is true at the time of writing this post. Since both Microsoft Azure Synapse and Google BigQuery, along with some other providers like Redshift and Snowflake, are competing with each other in this domain, Consumers can be assured of the best performance and prices.  

Choosing one over the other entails determining which solution is best suited to your data strategy. Google BigQuery and Azure Synapse Analytics, like most modern cloud data warehouse platforms, offer free trials and proof-of-concept support to help businesses gain firsthand experience with how their solutions deliver value.

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Pratik Dwivedi
Technical Content Writer, Hevo Data

Pratik Dwivedi is a seasoned author specializing in data industry topics, including data analytics, machine learning, AI, big data, and business intelligence. With over 18 years of experience in system analysis, design, and implementation, including 8 years in a Techno-Managerial role, Pratik has successfully managed international clients and led small to medium-sized teams and projects. He excels in creating engaging content that informs and inspires.

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