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Google BigQuery

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

Developers describe Google BigQuery as "Analyze terabytes of data in seconds". 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.. On the other hand, Google Cloud SQL is detailed as "Store and manage data using a fully-managed, relational MySQL database". MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.

Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Google Cloud SQL can be primarily classified under "SQL Database as a Service".

Some of the features offered by Google BigQuery are:

  • 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.

On the other hand, Google Cloud SQL provides the following key features:

  • Familiar Infrastructure
  • Flexible Charging
  • Security, Availability, Durability

"High Performance" is the top reason why over 17 developers like Google BigQuery, while over 12 developers mention "Fully managed" as the leading cause for choosing Google Cloud SQL.

According to the StackShare community, Google BigQuery has a broader approval, being mentioned in 160 company stacks & 41 developers stacks; compared to Google Cloud SQL, which is listed in 73 company stacks and 28 developer stacks.

Decisions about Google BigQuery and Google Cloud SQL
Julien Lafont

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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Pros of Google BigQuery
Pros of Google Cloud SQL
  • 27
    High Performance
  • 24
    Easy to use
  • 21
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 11
    Full table scans in seconds, no indexes needed
  • 11
    Big Data
  • 8
    Always on, no per-hour costs
  • 5
    Good combination with fluentd
  • 4
    Machine learning
  • 13
    Fully managed
  • 10
    SQL
  • 10
    Backed by Google
  • 4
    Flexible
  • 3
    Encryption at rest and transit
  • 3
    Replication across multiple zone by default
  • 3
    Automatic Software Patching

Sign up to add or upvote prosMake informed product decisions

Cons of Google BigQuery
Cons of Google Cloud SQL
  • 1
    You can't unit test changes in BQ data
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    What is Google BigQuery?

    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.

    What is Google Cloud SQL?

    MySQL databases deployed in the cloud without a fuss. Google Cloud Platform provides you with powerful databases that run fast, don’t run out of space and give your application the redundant, reliable storage it needs.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Google BigQuery?
    What companies use Google Cloud SQL?
    See which teams inside your own company are using Google BigQuery or Google Cloud SQL.
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    What tools integrate with Google BigQuery?
    What tools integrate with Google Cloud SQL?

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    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

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    Jul 2 2019 at 9:34PM

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    What are some alternatives to Google BigQuery and Google Cloud SQL?
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    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.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    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.
    Google Analytics
    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.
    See all alternatives