Google BigQuery vs Hadoop: 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, Hadoop is detailed as "Open-source software for reliable, scalable, distributed computing". 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.
Google BigQuery can be classified as a tool in the "Big Data as a Service" category, while Hadoop is grouped under "Databases".
"High Performance" is the primary reason why developers consider Google BigQuery over the competitors, whereas "Great ecosystem" was stated as the key factor in picking Hadoop.
Hadoop is an open source tool with 9.18K GitHub stars and 5.74K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 116 developers stacks; compared to Google BigQuery, which is listed in 156 company stacks and 39 developer stacks.
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