Google BigQuery vs Treasure Data: 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, Treasure Data is detailed as "Flexible data analytics infrastructure as a service". Treasure Data's Big Data as-a-Service cloud platform enables data-driven businesses to focus their precious development resources on their applications, not on mundane, time-consuming integration and operational tasks. The Treasure Data Cloud Data Warehouse service offers an affordable, quick-to-implement and easy-to-use big data option that does not require specialized IT resources, making big data analytics available to the mass market.
Google BigQuery and Treasure Data can be categorized as "Big Data as a Service" 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, Treasure Data provides the following key features:
- Instant Integration- Using td-agent, you can start importing your data from existing log files, web and packaged applications right away.
- Streaming or Batch?- You choose! Our data collection tool, td-agent, enables you to stream or batch your data to the cloud in JSON format.
- Secure Upload- The connection between td-agent and the cloud is SSL-encrypted, ensuring secure transfer of your data.
What is Google BigQuery?
What is Treasure Data?
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