Google BigQuery vs Google Cloud SQL

Overview

Google Cloud SQL

Stacks555

Followers580

Votes46

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Google BigQuery vs Google Cloud SQL: What are the differences?

Introduction: Google BigQuery and Google Cloud SQL are both cloud-based database services offered by Google Cloud Platform. While they both provide managed database solutions, they have several key differences in terms of functionality and use cases.

Scalability Google BigQuery is designed for large-scale data analytics and can handle massive datasets efficiently. It is a serverless data warehouse that allows for seamlessly scaling compute and storage resources as needed. On the other hand, Google Cloud SQL is a fully managed relational database service that is more suitable for smaller workloads and traditional database applications. It operates as a traditional SQL database, offering horizontal autoscaling for read-intensive workloads, but vertical scaling is limited.
Data Structure BigQuery uses a columnar storage format, which is optimized for querying and analyzing structured and semi-structured data. It can handle nested and repeated fields, making it suitable for handling complex data models. In contrast, Cloud SQL is based on traditional SQL databases like MySQL and PostgreSQL, which are designed for structured data with predefined schemas.
Query Language BigQuery uses its own query language called BigQuery SQL, which is similar to standard SQL but also includes extensions for handling nested and repeated fields. It supports advanced analytics functions and can execute complex queries efficiently. On the other hand, Cloud SQL supports standard SQL for querying, as it is based on traditional SQL databases.
Pricing Model BigQuery pricing is based on a combination of storage and query execution costs. It charges for the amount of data you store in tables and the amount of data processed by queries. The pricing is optimized for analytical workloads, where storing and querying large datasets is more common. In contrast, Cloud SQL pricing is primarily based on the size of the database instance and additional costs for storage and network egress.
Availability and Durability BigQuery offers high availability and durably stores data across multiple locations, with built-in replication and backup features. It automatically handles infrastructure failures and ensures data durability. Cloud SQL also provides high availability with automatic failover, but the durability relies on the underlying storage system, which might vary depending on the database engine used.
Use Cases BigQuery is well-suited for organizations that need to process and analyze large datasets for business intelligence, data warehousing, and machine learning purposes. It is commonly used for ad hoc analytics, data exploration, and running complex analytical queries on massive datasets. Cloud SQL, on the other hand, is more suitable for traditional database applications, such as web applications, content management systems, and e-commerce platforms, where structured data and transactional processing are the primary focus.

Summary: In summary, Google BigQuery is a serverless data warehouse optimized for large-scale data analytics, while Google Cloud SQL is a managed relational database service designed for smaller workloads and traditional database applications. BigQuery is ideal for processing massive datasets and running complex analytical queries, while Cloud SQL is more suitable for structured data and transactional processing.

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Advice on Google Cloud SQL, Google BigQuery

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Comments

Gopi

Jan 28, 2020

Needs advice

Hi Team, We already have share point custom application with below features.

Transaction type(Create, Update, Delete, Read)
Workflow
Report
Number of Columns 150
Max 50 K rows
No stored procedure using. Fetching data via queries

We are planning to migrate in Google Cloud Platform. Kindly suggest us the best database with detailed explanation if possible. Also provide which will be cost effective with some sample example.

Thanks & Regards, Gopi Thakur

5.95k views5.95k

Comments

Detailed Comparison

Google Cloud SQL	Google BigQuery
Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.	Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
Familiar Infrastructure;Flexible Charging;Security, Availability, Durability;Easier Migration; No Lock-in;Fully managed	All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
Statistics
Stacks 555	Stacks 1.8K
Followers 580	Followers 1.5K
Votes 46	Votes 152
Pros & Cons
Pros 13 Fully managed 10 Backed by Google 10 SQL 4 Flexible 3 Automatic Software Patching	Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas
Integrations
No integrations available	Xplenty Fluentd Looker Chartio Treasure Data

What are some alternatives to Google Cloud SQL, Google BigQuery?

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon Aurora

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Azure SQL Database

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Related Comparisons

Google BigQuery vs Google Cloud SQL: What are the differences?

Scalability Google BigQuery is designed for large-scale data analytics and can handle massive datasets efficiently. It is a serverless data warehouse that allows for seamlessly scaling compute and storage resources as needed. On the other hand, Google Cloud SQL is a fully managed relational database service that is more suitable for smaller workloads and traditional database applications. It operates as a traditional SQL database, offering horizontal autoscaling for read-intensive workloads, but vertical scaling is limited.
Data Structure BigQuery uses a columnar storage format, which is optimized for querying and analyzing structured and semi-structured data. It can handle nested and repeated fields, making it suitable for handling complex data models. In contrast, Cloud SQL is based on traditional SQL databases like MySQL and PostgreSQL, which are designed for structured data with predefined schemas.
Query Language BigQuery uses its own query language called BigQuery SQL, which is similar to standard SQL but also includes extensions for handling nested and repeated fields. It supports advanced analytics functions and can execute complex queries efficiently. On the other hand, Cloud SQL supports standard SQL for querying, as it is based on traditional SQL databases.
Pricing Model BigQuery pricing is based on a combination of storage and query execution costs. It charges for the amount of data you store in tables and the amount of data processed by queries. The pricing is optimized for analytical workloads, where storing and querying large datasets is more common. In contrast, Cloud SQL pricing is primarily based on the size of the database instance and additional costs for storage and network egress.
Availability and Durability BigQuery offers high availability and durably stores data across multiple locations, with built-in replication and backup features. It automatically handles infrastructure failures and ensures data durability. Cloud SQL also provides high availability with automatic failover, but the durability relies on the underlying storage system, which might vary depending on the database engine used.
Use Cases BigQuery is well-suited for organizations that need to process and analyze large datasets for business intelligence, data warehousing, and machine learning purposes. It is commonly used for ad hoc analytics, data exploration, and running complex analytical queries on massive datasets. Cloud SQL, on the other hand, is more suitable for traditional database applications, such as web applications, content management systems, and e-commerce platforms, where structured data and transactional processing are the primary focus.

Google BigQuery vs Google Cloud SQL

Overview