Among modern cloud data warehouse platforms, Google BigQuery and Microsoft Azure Synapse Analytics have a lot in common, including columnar storage and massively parallel processing (MPP) architecture. But each has unique features that could make it better suited to a particular organization’s data analytics infrastructure.

Comparison criteria

Considering key differentiating factors can help you determine whether BigQuery or Azure Synapse Analytics is a better data warehouse for your business. Here we compare these two cloud data destinations along several dimensions:

Architecture and pricing

BigQuery and Azure Synapse Analytics bill for usage according to different models. Both platforms take into account both computation and storage, but in different ways.

With BigQuery, a serverless data warehouse, you don’t have to think about architecture — the platform manages all resources and automates scalability and availability, so administrators don’t have to make any decisions about necessary CPU or storage levels.

BigQuery has two pricing options. Its on-demand model uses a query-based pricing model for compute resources. Users are charged for the amount of data their queries scan at a rate of $5 per terabyte of data processed. A flat-rate option lets customers purchase dedicated resources for query processing rather than pay for individual queries. That plan starts at $8,500 a month with an annual plan with 500 “flex slots,” which are 60-second commitments of dedicated query processing capacity.

Google also charges for data storage at a rate of $20 per terabyte per month.

Azure Synapse Analytics is not a serverless data warehouse; Microsoft charges for compute nodes, which it calls data warehouse units (DWU). DWUs comprise CPU, memory, and IOPS but not storage. Microsoft offers a wide variety of DWUs at prices that range from $1.20 to $360 per hour. Unlike Google, Microsoft does not charge per query.

Data storage is charged at the rate of $122.88 per terabyte per month.

Note that cloud providers change their pricing frequently — these rates were in place when this article was written.

 

Performance

Thanks to their ability to scale up and down, both BigQuery and Azure Synapse Analytics perform well under various load levels. You should run benchmarks using your own data, but you’ll likely find that both platforms can handle most companies’ workloads with excellent performance.

Administration, management, maintenance

Each of these data warehouses lets administrators manage user roles and permissions and data security, but BigQuery imposes less of a burden on administrators’ time than Azure Synapse Analytics.

BigQuery is “serverless” — compute and storage resources can scale independently, and all scaling issues are handled automatically.

For Microsoft’s part, while other Azure services can be set up to autoscale, scaling an Azure Synapse Analytics data warehouse requires administrator attention. Administrators can also partition data structures to improve performance and do other kinds of performance optimization.

Data protection

BigQuery maintains a complete seven-day history of changes against its tables. Administrators can revert changes without having to request a recovery from backups.

Azure Synapse takes automatic snapshots of the data warehouse throughout the day to create restore points that are available for seven days. You can also manually trigger as many as 42 user-defined snapshots. Snapshot storage counts toward storage allotment for billing purposes. You can restore the data warehouse from any snapshot by issuing a restore command.

Security

Both BigQuery and Azure Synapse Analytics use AES encryption on data at rest, and support customer-managed keys. BigQuery turns on encryption by default; Azure Synapse Analytics does not. Both rely on roles for providing access to resources.

Both data warehouses also provide some measure of network security. BigQuery allows you to configure a network security perimeter with Google Cloud Platform’s Virtual Private Cloud (VPC) Service Controls. Microsoft offers a similar approach with what it calls virtual networks.

Compliance and governance

BigQuery and Azure Synapse Analytics satisfy compliance requirements for HIPAA, ISO 27001, PCI DSS, SOC 1 Type II, and SOC 2 Type II, among others.

BigQuery vs. Azure Synapse Analytics: which is better?

Overall, both BigQuery and Azure Synapse Analytics have a lot going for them. You should do testing with your own data — ingesting data, running reports — to determine which cloud data warehouse better suits your organization. Opting for one over the other involves identifying which solution makes the most sense for your data strategy. Like most modern cloud data warehouse platforms, BigQuery and Azure Synapse Analytics offer free trials and proof-of-concept support to help businesses get firsthand experience with the ways their solutions deliver value.

Stitch gets your data there fast

Successful businesses that depend on sound intelligence need a high-performing cloud data warehouse. On the road to better business intelligence, both BigQuery and Azure Synapse Analytics are prime destinations. No matter which one you select as your data warehouse, getting all of your organization’s data ingested is critical to providing the background you need for better business intelligence.

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