BigQuery vs. Azure Synapse: A Comprehensive Analysis of Data Warehousing Solutions

Data warehousing has evolved from traditional on-premises systems designed to store and analyze large volumes of structured data to highly scalable cloud-based solutions. Early data warehouses required significant infrastructure investments and were limited by storage capacity and processing speed. As businesses generate more and more data from diverse sources, the need for flexible, scalable, and cost-effective solutions has driven the adoption of cloud-based data warehouses like Google BigQuery and Azure Synapse.

These platforms offer on-demand scaling, faster query performance, and integration with modern analytics and AI tools, so it’s easier for organizations to handle vast datasets. This shift has empowered businesses to make real-time data-driven decisions with less overhead and greater agility.

What is Google BigQuery?

BigQuery is a fully managed, cost-effective, serverless cloud data warehouse and analytics platform designed to handle and analyze large-scale datasets efficiently. It supports ANSI SQL, which allows users to run fast queries across vast amounts of data, leveraging the power of Google’s infrastructure for high-speed processing. BigQuery is known for its scalability, pay-as-you-go pricing, and seamless integration with other Google Cloud services, which make it ideal for real-time data analysis, machine learning, and business intelligence.

In addition, BigQuery automates the resource allocation process. Its columnar storage structure allows for easy querying and aggregation tasks. The platform also provides data security, so you can easily verify the identity and access status of clients.

What is Azure Synapse?

Azure Synapse is a Microsoft cloud-based analytics service that integrates big data and data warehousing capabilities into a unified platform. It combines data integration, data warehousing, and big data analytics into a single service, which enables users to analyze large volumes of data across multiple sources using SQL, Spark, and other tools.

Businesses can use Azure Synapse to build end-to-end analytics solutions, perform real-time data processing, and gain insights using a single, scalable environment. It provides T-SQL (Transact-Queue Sequential Query Language) analytics, including dedicated and serverless SQL pools for complete analytics and data storage. The pool of dedicated SQL Servers provides the infrastructure required to implement and operate data warehouses, while serverless SQL pools allow for ad-hoc or unplanned workloads.

Azure Synapse also supports seamless integration with other Azure services and provides advanced security and data governance features.

BigQuery vs. Azure Synapse: 8 Differences

Both BigQuery and Azure Synapse are powerful cloud-based data warehousing solutions, but they differ significantly in key areas. These differences can influence which platform is better suited for specific business needs, depending on factors such as architecture, scalability, pricing, integration options, and more. You need to understand the unique strengths and capabilities of each solution so you can make an informed decision when choosing one for your business.

The following sections explore some of the major distinctions between BigQuery and Azure Synapse, highlighting areas like performance, data integration, security, and ease of use.

Architecture

BigQuery and Azure Synapse differ fundamentally in their architecture, which shapes how they handle data warehousing and analytics. BigQuery is a fully managed, serverless platform where users don’t need to manage infrastructure. It operates on a columnar storage model with automatic scalability and distributed query execution, so it’s highly efficient for large-scale analytics without manual resource management.

In contrast, Azure Synapse offers a hybrid approach of provisioned and serverless compute options, which lets users control resource allocation through Data Warehouse Units (DWUs) or opt for on-demand, serverless querying. Azure Synapse also integrates deeply with big data environments, supporting both SQL-based analytics and Apache Spark for more complex data processing.

Scalability and performance

BigQuery and Azure Synapse offer distinct approaches to performance and scalability, tailored to different user needs. With its serverless architecture, BigQuery is designed for seamless scalability. It automatically adjusts compute and storage resources as data volumes increase, ensuring consistent performance without manual intervention. BigQuery excels in handling large-scale queries, offering fast, distributed processing across vast datasets.

On the other hand, Azure Synapse provides more control over performance through its provisioned model, which allows users to manually scale resources by adjusting DWUs. While this offers flexibility for tuning performance based on workload needs, this approach requires more active management.

Pricing model

The BigQuery and Azure Synapse pricing models also differ significantly, reflecting their distinct approaches to data warehousing. BigQuery follows a pay-as-you-go model where users are charged based on the amount of data processed by queries and the storage used, so it’s highly cost-effective for organizations with unpredictable workloads. But it also offers flat-rate pricing for businesses with consistent, high-volume queries, so costs become more predictable.

Azure Synapse uses a combination of provisioned DWU resources and serverless options. With the provisioned model, users pay for their allocated compute resources, whether they are fully utilized or not, but the serverless model charges based on the data processed by each query. Overall, BigQuery’s straightforward pricing suits dynamic workloads, while Azure Synapse offers more customization but requires careful cost management.

Data loading and ETL

BigQuery and Azure Synapse cater to different use cases in their approaches to data loading and ETL (Extract, Transform, Load) processes. BigQuery is optimized for seamless and high-speed data ingestion, supporting both batch and streaming data through tools like Google Cloud Dataflow, Pub/Sub, and direct uploads from Google Cloud Storage. It excels in real-time data processing, so businesses can perform analytics on streaming data with minimal latency.

In contrast, Azure Synapse integrates with Azure Data Factory for comprehensive ETL pipelines, which enables complex data transformations and orchestrations across diverse sources. Azure Synapse’s support for both batch loading and real-time ingestion via services like Azure Stream Analytics provides more flexibility in handling mixed data processing needs. Its built-in data flows also make it easier to perform detailed transformations without requiring separate tools.

In general, BigQuery focuses on simplifying data loading for analytics, particularly in real-time scenarios, while Azure Synapse provides a robust ETL environment with extensive transformation and orchestration capabilities.

Machine learning (ML) integration

BigQuery and Azure Synapse approach machine learning integration in ways that reflect their distinct focuses and target users. BigQuery features built-in machine learning capabilities through BigQuery ML, which lets users create, train, and deploy machine learning models directly using SQL. This is designed to be user-friendly for data analysts who are familiar with SQL but might not have extensive knowledge of advanced machine learning frameworks. BigQuery ML supports common tasks like regression, classification, and deep learning, without requiring users to move data to external tools.

In contrast, Azure Synapse integrates with Azure Machine Learning, which offers an extensive range of machine learning options that cater to data scientists and engineers. Azure Synapse also supports Python and Spark for building more complex models, enabling end-to-end AI solutions within the Azure ecosystem.

Compliance

BigQuery and Azure Synapse both offer strong compliance features, but they align with different sets of standards and certifications. BigQuery benefits from Google Cloud's extensive compliance certifications, including GDPR, HIPAA, and CCPA, as well as industry-specific standards like SOC 1, SOC 2, and ISO 27001. Google Cloud provides tools like Data Loss Prevention (DLP) and Identity and Access Management (IAM) to help ensure data protection and regulatory adherence.

Azure Synapse, on the other hand, is integrated within the broader Azure ecosystem, so it benefits from Microsoft's compliance framework, which includes certifications such as GDPR, HIPAA, and ISO 27001, alongside industry-specific standards like SOC 1, SOC 2, and PCI DSS. Azure Synapse also leverages Azure Purview for data governance, offering advanced capabilities for data cataloging and lineage tracking, both of which are crucial for meeting compliance requirements.

Data storage

Because of their underlying architectures and intended use cases, BigQuery and Azure Synapse differ significantly in how they handle data storage. BigQuery’s proprietary, columnar storage format is optimized for analytical queries, which allows for high compression and performance when scanning large datasets. Data is automatically partitioned and clustered to enhance query performance, and BigQuery supports both native storage and external tables for querying data stored in Google Cloud Storage and other sources like Google Sheets.

Azure Synapse relies on Azure Data Lake Storage Gen2 for its primary storage, which enables a unified solution for both structured and unstructured data. It also offers flexibility with PolyBase, which allows users to query external data sources such as Hadoop, SQL Server, or Cosmos DB without moving data. This makes Azure Synapse a versatile choice for organizations dealing with diverse data formats and large-scale data lakes.

User experience and ease of use

The BigQuery and Azure Synapse user experiences are aligned with the preferences and needs of their typical users. BigQuery is known for its simplicity and ease of use because it offers a straightforward, serverless interface where users can focus on writing SQL queries without worrying about managing infrastructure. Its clean, intuitive UI, in conjunction with features like automatic scaling and query optimization, make it accessible to both technical and non-technical users, especially those focused on quick data analysis and reporting.

On the other hand, Azure Synapse provides a more comprehensive and integrated workspace that combines SQL, Spark, and data integration tools, which offers flexibility but comes with a steeper learning curve. Its unified environment is powerful for users who require end-to-end data management, from ingestion to advanced analytics, but this complexity can be overwhelming for those unfamiliar with multi-service platforms.

All these differences reflect how BigQuery and Azure Synapse are tailored for different types of organizations and workloads. In general, BigQuery is ideal for users looking for straightforward, fully-managed analytics, while Azure Synapse is more suitable for enterprises that need a comprehensive platform that spans both data warehousing and big data analytics.

The CData difference

Whether you're using Google BigQuery or Azure Synapse for your data warehousing initiatives, you still need to integrate your data with your warehouse. CData Sync provides an easy-to-use interface for replicating all of your data into your warehouse. With self-hosted and SaaS offerings, Sync fits into any data strategy.