Google BigQuery vs Impala: 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.; Impala: Real-time Query for Hadoop. Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Impala can be primarily classified under "Big Data 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, Impala provides the following key features:
- Do BI-style Queries on Hadoop
- Unify Your Infrastructure
- Implement Quickly
"High Performance" is the top reason why over 17 developers like Google BigQuery, while over 7 developers mention "Super fast" as the leading cause for choosing Impala.
Impala is an open source tool with 2.18K GitHub stars and 824 GitHub forks. Here's a link to Impala'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 Impala, which is listed in 15 company stacks and 5 developer stacks.
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I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.
What's nice too is that it has SQL-based ML tools, and it has great GIS support!
BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.
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