Do you want to find the best database software? Are you unsure whether Google BigQuery or Microsoft SQL Server is best for you? It is impossible to determine whether Google BigQuery or Microsoft SQL Server is the best database option for you based solely on ratings and the number of features they provide.
Those high-level figures are certainly useful, but the reality is dependent on whether they provide the capabilities you require, whether they integrate with your other tools, what support is provided, and pricing options.
This blog provides the key differences that can explain which software in Google BigQuery Vs SQL Server is the best. Along with the average size of the businesses that use, Performance and Pricing in Google BigQuery and Microsoft SQL Server.
What is Google BigQuery?
Google BigQuery is the product offered by Google Cloud Platform, which is serverless, cost-effective, highly scalable data warehouse capabilities along with built-in Machine Learning features. Google BigQuery supports ANSI SQL, allowing users to run SQL queries on massive datasets to manage business transactions, perform data analytics, and do a variety of other things.
Google BigQuery is becoming increasingly popular, and many companies, including Twitter, use it to forecast the exact volume of packages for its various offerings.
Streaming data sources, in addition to batch data, are supported by Google BigQuery.
Key Features of Google BigQuery
Here are some notable key features of Google BigQuery:
- Scalable Architecture: Google BigQuery has a scalable architecture and offers a petabyte scalable system that users can scale up and down as per load.
- Faster Processing: Being a scalable architecture, Google BigQuery executes petabytes of data within the stipulated time and is more rapid than many conventional systems. Google BigQuery allows users to run analysis over millions of rows without worrying about scalability.
- Fully Managed: Google BigQuery is a product of Google Cloud Platform, and thus it offers fully managed and serverless systems.
- Security: Google BigQuery has the utmost security level that protects the data at rest and in flight.
- Real-time data ingestion: Google BigQuery can perform real-time data analysis, thereby making it famous across all the IoT and Transaction platforms.
- Fault Tolerance: Google BigQuery offers replication that replicates data across multiple zones or regions. It ensures consistent data availability when the region/zones go down.
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What is SQL Server?
SQL Server is a relational database owned by Microsoft to store structured data. It is commonly known as MSSQL and is built on top of Structured Query Language (SQL) to query and perform analytics on the database object. SQL Server has extensive support for ANSI SQL and PLSQL to create stored procedures, derived variables, etc.
SQL Server was initially available for Windows, but now it supports the Linux operating system.
Key Features of SQL Server
Here are a few features of SQL Server:
- Scalable: SQL server is an on-premise database, and you can add more nodes to accommodate the increasing volume of the data.
- Performance: SQL Server provides exceptional performance in transforming and analyzing the data. It has excellent support for Windows & Linux.
- Secure: Being an on-premise installation, you can have your private network to secure the data. It also supports various authentication methods like Kerberos for safe access to data.
- Analytics Support: MS SQL has excellent data analytical functions and machine learning models. You can use Python or R to connect to SQL Server and perform modeling.
- Multi-Threading: With MS SQL, you can achieve parallelism and multi-threading to process the data.
BigQuery vs SQL Server: Key Comparison
| Factor | BigQuery | SQL Server |
| Architecture | Cloud-based, serverless, follows ANSI SQL standards. | On-premise or cloud-based, follows server-client architecture, ANSI SQL standard. |
| Scalability | Automatically scales up or down based on workload, serverless. | Manual intervention required to scale based on the data load. |
| Database Model | Relational DBMS (RDBMS), fixed schema structure. | Primarily RDBMS, also supports Document Store and Graph database. |
| Query Language Support | ANSI SQL with analytical, window, and aggregation functions. | ANSI SQL with full support for analytics, stored procedures, etc. |
| Performance | Excellent for large-scale data; auto-scaling ensures optimal performance. | Performance is dependent on manual scaling and resource allocation. |
| Replication Support | Built-in replication for fault tolerance and high availability (Hadoop-based). | Supports multiple replication methods: Merge, Peer-to-Peer, Transactional, Snapshot. |
| Pricing | Pay-as-you-go with on-demand and flat-rate pricing options on Google Cloud. | License-based, per-user pricing model, with tiered licensing costs. |
| Documentation & Support | Extensive documentation and tutorials available from Google Cloud. | Comprehensive official documentation and tutorials from Microsoft. |
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Google BigQuery Vs SQL Server Comparison
In this blog post, we will discuss the detailed comparison of Google BigQuery and SQL Server based on various factors as listed below –
1) Architecture
Google BigQuery is a cloud-based architecture that has a scalable architecture with a relational RDBMS structure. It follows ANSI SQL standard and allows users to create, delete and update data in Google BigQuery.
SQL Server is a relational DBMS that has a server-client architecture. It also follows ANSI SQL standard and allows users to create, delete, and update tables.
2) Scalability
Google BigQuery is a serverless architecture and is cloud-based. Google BigQuery automatically allocates computing resources as you need them. Google Bigquery can auto-scale up and down based on the data load.
On the other hand, SQL Server doesn’t have auto-scalability, and hence it needs manual intervention to scale up and down based on the data load.
3) Database Model
Google BigQuery is based on the RDBMS model, and it supports a fixed schema structure.
On the other hand, the SQL Server has a primary RDBMS model, whereas it also supports Document store and Graph database.
4) Query Language Support
Google BigQuery supports ANSI SQL and has all the supported functions available like analytical, window, aggregation, and many more.
SQL Server also supports ANSI SQL and has all the features of SQL available to the users to perform analytics over data.
5) Performance
Google BigQuery is a cloud-based Architecture and provides exceptional performance as it can auto-scale up and down based on the data load and performs data analysis efficiently.
On the other hand, SQL Server is based on client-server architecture and has fixed performance throughout unless the user scales it manually.
6) Replication Support
Google BigQuery is built to process a huge volume of data and follows the Hadoop concept. It supports Data Replication to provide fault tolerance and high availability of data.
SQL Server supports Replication, and there are four different types of Replication supported by SQL Server.
- Merge Replication
- Peer-to-Peer Replication
- Transactional Replication
- Snapshot Replication
7) Pricing
The Google BigQuery pricing plan is based on the Google Cloud Platform subscription plan. It also has fixed and on-demand pricing to support demand based on data load. You can find more information on the pricing here.
The SQL Server has server-client architecture and has per-user pricing. You can find more information on the pricing here.
8) Documentation and Tutorials
Google BigQuery has its official documentation and tutorial for users that provide in-depth details of the concept and examples. You can find more information here.
SQL Server also has official documentation and tutorials for users that provide in-depth details of the concept and examples. You can find more information here
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Limitations of Using SQL Server
SQL Server is a popular product and is being used widely in the industries. However, with cloud technologies, SQL Server seems to be lagging in some aspects. Let us discuss some of the limitations of SQL Server –
- Cost Inefficient – SQL Server is a costly affair, and its enterprise version can cost as much as $5434 per year. This huge pricing is causing the industries to shift towards cloud technologies.
- Poor Interface – SQL Server does not have a good interface, making it difficult to understand and operate databases.
- Compatibility – SQL Server is designed to run only on Windows-based servers, making a big blow for the industries using Unix servers for their process.
Limitations of Using Google BigQuery
- Rate of Data Insertion – The rate at which data can be Inserted/Updated/Deleted with Google BigQuery is five operations every 10 seconds per table. If the inserted frequency exceeds, then the failure will happen.
- Rate of Destination Table Update – Destination tables in a query job are limited to 1,000 updates per table per day. So whenever more than 1,000 inserts are performed on a table in less than 24 hours, an error will appear.
Final Verdict – Google BigQuery Or SQL Server
In this article, we have discussed Google BigQuery vs SQL Server and compared both of them with respect to various parameters.
Based on the above discussion and carefully examining the limitations of both, we can say that Google BigQuery will be an ideal choice when the requirement is the best performance, scalability, lower cost, and compatibility with various systems.
Conclusion
This blog post discusses the important criteria that distinguish Google BigQuery and SQL Server. We have also compared both of them against each other.
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Frequently Asked Questions
1. Is BigQuery better than SQL?
BigQuery is not inherently “better” than SQL; it’s a cloud-based data warehouse that uses SQL for querying. BigQuery excels in handling large-scale data and is optimized for analytical queries across huge datasets. Traditional SQL databases are better for transactional systems, while BigQuery is ideal for large-scale analytics.
2. When should you not use BigQuery?
You should not use BigQuery when:
-You need low-latency, transactional processing (OLTP).
-You are dealing with small datasets that don’t justify the cost.
-Your queries require real-time, row-level updates (BigQuery is more optimized for batch processing).
-You need fine-grained control over your infrastructure and storage.
3. Is BigQuery a SQL or NoSQL?
BigQuery is a SQL-based data warehouse.
Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.
