Apache Drill vs Google BigQuery: What are the differences?
Apache Drill: Schema-Free SQL Query Engine for Hadoop and NoSQL. Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel; 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..
Apache Drill belongs to "Database Tools" category of the tech stack, while Google BigQuery can be primarily classified under "Big Data as a Service".
Some of the features offered by Apache Drill are:
- Low-latency SQL queries
- Dynamic queries on self-describing data in files (such as JSON, Parquet, text) and MapR-DB/HBase tables, without requiring metadata definitions in the Hive metastore.
- ANSI SQL
On the other hand, Google BigQuery provides the following key features:
- 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.
"NoSQL and Hadoop" is the primary reason why developers consider Apache Drill over the competitors, whereas "High Performance" was stated as the key factor in picking Google BigQuery.
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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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