Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Google BigQuery and SQL Server so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Google BigQuery and SQL Server perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Google BigQuery vs SQL Server Breakdown


 
Database Model

Data warehouse

Relational database

Architecture

BigQuery is a fully managed, serverless data warehouse provided by Google Cloud Platform. It is designed for high-performance analytics and utilizes Google’s infrastructure for data processing. BigQuery uses a columnar storage format for fast querying and supports standard SQL. Data is automatically sharded and replicated across multiple availability zones within a Google Cloud region

SQL Server can be deployed on-premises, in virtual machines, or as a managed cloud service (Azure SQL Database) on Microsoft Azure. It is available in multiple editions tailored to different use cases, such as Express, Standard, and Enterprise.

License

Closed source

Closed source

Use Cases

Business analytics, large-scale data processing, data integration

Transaction processing, business intelligence, data warehousing, analytics, web applications, enterprise applications

Scalability

Serverless, petabyte-scale data warehouse that can handle massive amounts of data with no upfront capacity planning required

Supports vertical and horizontal scaling, with features like partitioning, sharding, and replication for distributed environments

Looking for the most efficient way to get started?

Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.

Google BigQuery Overview

Google BigQuery is a fully-managed, serverless data warehouse and analytics platform developed by Google Cloud. Launched in 2011, BigQuery is designed to handle large-scale data processing and querying, enabling users to analyze massive datasets in real-time. With a focus on performance, scalability, and ease of use, BigQuery is suitable for a wide range of data analytics use cases, including business intelligence, log analysis, and machine learning.

SQL Server Overview

Microsoft SQL Server is a powerful and widely used relational database management system developed by Microsoft. Initially released in 1989, it has evolved over the years to become one of the most popular database systems for businesses of all sizes. SQL Server is known for its robust performance, security, and ease of use. It supports a variety of platforms, including Windows, Linux, and containers, providing flexibility for different deployment scenarios.


Google BigQuery for Time Series Data

BigQuery can be used for storing and analyzing time series data, although it is more focused on traditional data warehouse use cases. BigQuery may struggle for use cases where low latency response times are required

SQL Server for Time Series Data

While Microsoft SQL Server is primarily a relational database, it does offer support for time series data through various features and optimizations. Temporal tables allow for tracking changes in data over time, providing an efficient way to store and query historical data. Indexing and partitioning can be leveraged to optimize time series data storage and retrieval. However, SQL Server may not be the best choice for applications requiring high write or query throughput specifically for time series data, as specialized time series databases offer more optimized solutions as well as a variety of developer productivity features that speed up development time for applications that heavily use time series data.


Google BigQuery Key Concepts

Some important concepts related to Google BigQuery include:

  • Projects: A project in BigQuery represents a top-level container for resources such as datasets, tables, and views.
  • Datasets: A dataset is a container for tables, views, and other data resources in BigQuery.
  • Tables: Tables are the primary data storage structure in BigQuery and consist of rows and columns.
  • Schema: A schema defines the structure of a table, including column names, data types, and constraints.

SQL Server Key Concepts

  • T-SQL: Transact-SQL, an extension of SQL that adds procedural programming elements, such as loops, conditional statements, and error handling, to the standard SQL language.
  • SSMS: SQL Server Management Studio, an integrated environment for managing SQL Server instances, databases, and objects.
  • Always On: A suite of high availability and disaster recovery features in SQL Server, including Always On Availability Groups and Always On Failover Cluster Instances.


Google BigQuery Architecture

Google BigQuery’s architecture is built on top of Google’s distributed infrastructure and is designed for high performance and scalability. At its core, BigQuery uses a columnar storage format called Capacitor, which enables efficient data compression and fast query performance. Data is automatically partitioned and distributed across multiple storage nodes, providing high availability and fault tolerance. BigQuery’s serverless architecture automatically allocates resources for queries and data storage, eliminating the need for users to manage infrastructure or capacity planning.

SQL Server Architecture

Microsoft SQL Server is a relational database that uses SQL for querying and manipulating data. It follows a client-server architecture, with the database server hosting the data and processing requests from clients. SQL Server supports both on-premises and cloud-based deployment through Azure SQL Database, a managed service offering in the Microsoft Azure cloud. SQL Server’s architecture includes components such as the Database Engine, which processes data storage and retrieval, and various services for reporting, integration, and analysis.

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

Google BigQuery Features

Columnar Storage

BigQuery’s columnar storage format, Capacitor, enables efficient data compression and fast query performance, making it suitable for large-scale data analytics.

Integration with Google Cloud

BigQuery integrates seamlessly with other Google Cloud services, such as Cloud Storage, Dataflow, and Pub/Sub, making it easy to ingest, process, and analyze data from various sources.

Machine Learning Integration

BigQuery ML enables users to create and deploy machine learning models directly within BigQuery, simplifying the process of building and deploying machine learning applications.

SQL Server Features

Security

SQL Server offers advanced security features, such as Transparent Data Encryption, Always Encrypted, and row-level security, to protect sensitive data.

Scalability

SQL Server supports scaling out through features like replication, distributed partitioned views, and Always On Availability Groups.

Integration Services

SQL Server Integration Services (SSIS) is a powerful platform for building high-performance data integration and transformation solutions.


Google BigQuery Use Cases

Business Intelligence and Reporting

BigQuery is widely used for business intelligence and reporting, enabling users to analyze large volumes of data and generate insights to inform decision-making. Its fast query performance and seamless integration with popular BI tools, such as Google Data Studio and Tableau, make it an ideal solution for this use case.

Machine Learning and Predictive Analytics

BigQuery ML enables users to create and deploy machine learning models directly within BigQuery, simplifying the process of building and deploying machine learning applications. BigQuery’s fast query performance and support for large-scale data processing make it suitable for predictive analytics use cases.

Data Warehousing and ETL

BigQuery’s distributed architecture and columnar storage format make it an excellent choice for data warehousing and ETL (Extract, Transform, Load) workflows. Its seamless integration with other Google Cloud services, such as Cloud Storage and Dataflow, simplifies the process of ingesting and processing data from various sources.

SQL Server Use Cases

Enterprise Applications

SQL Server is commonly used as the backend database for enterprise applications, providing a reliable and secure data storage solution.

Data Warehousing and Business Intelligence

SQL Server’s built-in analytical features, such as Analysis Services and Reporting Services, make it suitable for data warehousing and business intelligence applications.

E-commerce Platforms

SQL Server’s performance and scalability features enable it to support the demanding workloads of e-commerce platforms, handling high volumes of transactions and user data.


Google BigQuery Pricing Model

Google BigQuery pricing is based on a pay-as-you-go model, with costs determined by data storage, query, and streaming. There are two main components to BigQuery pricing:

  • Storage Pricing: Storage costs are based on the amount of data stored in BigQuery. Users are billed for both active and long-term storage, with long-term storage offered at a discounted rate for infrequently accessed data.
  • Query Pricing: Query costs are based on the amount of data processed during a query. Users can choose between on-demand pricing, where they pay for the data processed per query, or flat-rate pricing, which provides a fixed monthly cost for a certain amount of query capacity.

SQL Server Pricing Model

Microsoft SQL Server offers a variety of licensing options, including per-core, server + CAL (Client Access License), and subscription-based models for cloud deployments. Costs depend on factors such as the edition (Standard, Enterprise, or Developer), the number of cores, and the required features. For cloud-based deployments, Azure SQL Database offers a pay-as-you-go model with various service tiers to accommodate different performance and resource requirements.