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Google BigQuery

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Sequel Pro

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Google BigQuery vs Sequel Pro: What are the differences?

Google BigQuery: 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.; Sequel Pro: MySQL database management for Mac OS X. Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

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

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, Sequel Pro provides the following key features:

  • Quickly filter and paginate table content
  • Fast, threaded UI
  • Document based connections — Save your connection and share it

"High Performance" is the primary reason why developers consider Google BigQuery over the competitors, whereas "Free" was stated as the key factor in picking Sequel Pro.

Sequel Pro is an open source tool with 6.73K GitHub stars and 589 GitHub forks. Here's a link to Sequel Pro's open source repository on GitHub.

According to the StackShare community, Google BigQuery has a broader approval, being mentioned in 160 company stacks & 41 developers stacks; compared to Sequel Pro, which is listed in 46 company stacks and 23 developer stacks.

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Pros of Google BigQuery
Pros of Sequel Pro
  • 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
  • 24
    Free
  • 18
    Simple
  • 17
    Clean UI
  • 8
    Easy

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

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    - No public GitHub repository available -

    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 Sequel Pro?

    Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

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

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

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    Blog Posts

    Aug 28 2019 at 3:10AM

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    What are some alternatives to Google BigQuery and Sequel Pro?
    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