Azure Synapse vs Google BigQuery: What are the differences?

Introduction

Azure Synapse and Google BigQuery are two popular cloud-based data warehouse solutions that offer fast and scalable data analytics capabilities. While they serve similar purposes, there are key differences between them that make them suitable for different use cases.

1. Integration and Ecosystem: Azure Synapse is tightly integrated with the broader Azure ecosystem, providing seamless integration with other Azure services such as Azure Data Factory, Azure Machine Learning, and Power BI. On the other hand, Google BigQuery is part of the Google Cloud Platform (GCP), which offers a wide range of complementary services such as Google Dataflow and Google Cloud Storage.

2. Performance and Concurrency: Azure Synapse offers dedicated SQL pools, allowing users to provision dedicated compute and storage resources for specific workloads. This provides better performance and higher concurrency for complex queries. In contrast, Google BigQuery uses an architecture that automatically scales resources based on demand, providing a serverless experience. While this offers ease of use and scalability, it may result in slightly lower performance for complex queries.

3. Pricing Model: Azure Synapse offers a flexible pricing model that allows users to choose between provisioned resources and on-demand serverless options, providing cost optimization based on workload requirements. On the other hand, Google BigQuery uses a pricing model based on usage, where users are billed based on the amount of data processed and the storage used. This can provide cost savings for sporadic or unpredictable workloads.

4. Data Import and Export: Azure Synapse provides native integrations with various data sources and supports both batch and real-time data ingestion. It offers connectors to common data sources such as Azure Blob Storage, Azure SQL Database, and popular big data frameworks like Apache Kafka and Apache Spark. Google BigQuery also provides various data connectors and supports batch data import/export, but it lacks support for real-time streaming ingestion out of the box.

5. Data Partitioning and Clustering: Azure Synapse supports data partitioning and clustering, allowing users to optimize query performance by organizing data based on specific columns, reducing the amount of data scanned during query execution. This is especially useful for large datasets. Google BigQuery offers a similar concept called partitioned tables but does not provide native support for data clustering, requiring users to manually organize data to achieve similar optimizations.

6. Machine Learning Capabilities: Azure Synapse provides integration with Azure Machine Learning, allowing users to build, train, and deploy machine learning models directly from the platform. Additionally, it offers built-in support for automated machine learning and model explainability. Google BigQuery also offers integration with Google Cloud AI Platform, providing similar machine learning capabilities. However, it does not have built-in support for automated machine learning.

In summary, Azure Synapse and Google BigQuery differ in terms of integration with their respective cloud ecosystems, flexibility in pricing models, performance and concurrency options, support for data import/export, data optimization techniques, and machine learning capabilities. The choice between them depends on specific requirements, preferences, and existing cloud platform investments.