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Google BigQuery vs Sequel Pro: What are the differences?
<p>Google BigQuery and Sequel Pro are both popular tools used for querying and analyzing data. While they serve similar purposes, there are key differences between the two that make each tool unique in its own way.</p>
1. **Pricing Model**: Google BigQuery operates on a pay-per-query pricing model, where users are charged based on the amount of data processed. In contrast, Sequel Pro is an open-source tool and is available for free with no additional costs for usage.
2. **Deployment**: Google BigQuery is a cloud-based service that does not require any installation or maintenance, making it easy to scale and accessible from anywhere with an internet connection. On the other hand, Sequel Pro is a desktop application that needs to be downloaded and installed locally on a machine.
3. **SQL Dialect**: Google BigQuery uses its own SQL dialect which includes proprietary functions and features specific to the platform. Sequel Pro, on the other hand, supports standard SQL syntax that is compatible with most database systems.
4. **Collaboration**: Google BigQuery offers built-in collaboration features that enable multiple users to work on the same project simultaneously, facilitating teamwork and coordination. In contrast, Sequel Pro is designed for individual use and does not have native collaboration capabilities.
5. **Scalability**: Google BigQuery is highly scalable and can handle large datasets efficiently, making it suitable for enterprise-level data processing and analysis. On the other hand, Sequel Pro may face performance issues when dealing with massive amounts of data due to its desktop-based nature.
6. **Data Source Connectivity**: Google BigQuery is optimized for connecting and querying data stored in Google Cloud Storage and Google Drive, while Sequel Pro supports connecting to various databases such as MySQL, PostgreSQL, and SQLite.</p>
In Summary, Google BigQuery and Sequel Pro differ in their pricing model, deployment method, SQL dialect, collaboration features, scalability, and data source connectivity.
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Learn MorePros of Google BigQuery
Pros of Sequel Pro
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
Pros of Sequel Pro
- Free25
- Simple18
- Clean UI17
- Easy8
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Cons of Google BigQuery
Cons of Sequel Pro
Cons of Google BigQuery
- You can't unit test changes in BQ data1
Cons of Sequel Pro
- Only available for Mac OS1
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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.
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What companies use Google BigQuery?
What companies use Sequel Pro?
What companies use Google BigQuery?
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?
What tools integrate with Sequel Pro?
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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.