Google BigQuery聽vs聽Google Cloud SQL

Google BigQuery

875
705
+ 1
117
Google Cloud SQL

391
359
+ 1
46
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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鈥檛 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.

Advice on Google BigQuery and Google Cloud SQL

Hi Team, We already have share point custom application with below features. 1. Transaction type(Create, Update, Delete, Read) 2. Workflow 3. Report 4. Number of Columns 150 5. Max 50 K rows 6. 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

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Decisions about Google BigQuery and Google Cloud SQL
Julien Lafont
Chose
to add
Google BigQuery
and to remove
Snowflake
at
()

BigQuery is the only really Serverless Cloud Datawarehouse. Automatic scaling, automatic partitioning, 0 maintenance operations.

Perfectly integrated with most GCP products (dataflow, cloud functions, gcs).

Impressive release rate. Looking forward to discover the new features available every month

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Pros of Google BigQuery
Pros of Google Cloud SQL

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Cons of Google BigQuery
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    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鈥檛 run out of space and give your application the redundant, reliable storage it needs.
    What companies use Google BigQuery?
    What companies use Google Cloud SQL?

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Google BigQuery?
    What tools integrate with Google Cloud SQL?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Aug 28, 2019 at 3:10AM
    https://img.stackshare.io/stack/505487/default_e35b8bd5e615e01dc9b420dbd2a444fcbaeff755.png logo

    Segment

    5
    1706
    Jul 2, 2019 at 9:34PM
    https://img.stackshare.io/stack/374725/default_11e3fd22f9363f9e6b2bd10a6a73e7a93b3a221c.png logo

    Segment

    10
    4615
    42
    66603
    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鈥攊t'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)鈥攏o 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
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