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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.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
Pros of Apache Drill
- NoSQL and Hadoop4
- Free3
- Lightning speed and simplicity in face of data jungle3
- Well documented for fast install2
- SQL interface to multiple datasources1
- Nested Data support1
- Read Structured and unstructured data1
- V1.10 released - https://drill.apache.org/1
Pros of Google BigQuery
- High Performance27
- Easy to use24
- Fully managed service21
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Full table scans in seconds, no indexes needed11
- Big Data11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
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Cons of Apache Drill
Cons of Google BigQuery
- You can't unit test changes in BQ data1
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What is Apache Drill?
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