Apache Drill vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Apache Drill

Stacks74

Followers171

Votes16

Apache Drill vs Google BigQuery: What are the differences?

Introduction

Apache Drill and Google BigQuery are both powerful data analysis tools that provide developers with the ability to query and analyze large datasets. While they have similar goals, there are several key differences between Apache Drill and Google BigQuery that make each unique.

Flexibility and Data Source Support: Apache Drill offers more flexibility and supports a wider range of data sources compared to Google BigQuery. Apache Drill can efficiently query structured and semi-structured data stored in various formats such as JSON, Parquet, Avro, and more. On the other hand, Google BigQuery is primarily designed for structured data stored in Google Cloud Storage or Google Drive.
Cost Structure: The cost structure of Apache Drill and Google BigQuery differs significantly. Apache Drill is an open-source project that can be freely downloaded, installed, and used without incurring any additional charges. In contrast, Google BigQuery is part of the Google Cloud Platform and has a usage-based pricing model. Users are charged based on the amount of data processed and storage used.
Scalability: While both Apache Drill and Google BigQuery can handle large volumes of data, the underlying architecture and scalability options differ. Apache Drill leverages the distributed computing power of Apache Hadoop to scale horizontally and process data in parallel across a cluster. Google BigQuery, on the other hand, is a fully managed service that automatically scales to handle massive datasets without requiring manual configuration or infrastructure management.
Query Language Support: Apache Drill supports SQL queries, making it easy for developers familiar with SQL to interact with the data. In addition, Apache Drill also provides support for complex nested data structures through its SQL-based query language. Google BigQuery, on the other hand, uses a proprietary query language called BigQuery SQL, which is similar to SQL but has some additional syntax and features.
Integration with Ecosystem: Apache Drill integrates well with the Apache Hadoop ecosystem and can leverage other tools such as Apache Hive, Apache HBase, and more. This allows developers to easily combine the capabilities of these tools with Apache Drill for efficient data analysis. Google BigQuery, on the other hand, is tightly integrated with other Google Cloud Platform services, providing seamless integration with storage, compute, and analytics services offered by Google.
Performance Optimization: Apache Drill provides developers with fine-grained control over query execution and optimization, allowing them to tune performance according to their specific requirements. Google BigQuery, being a fully managed service, automatically optimizes query execution behind the scenes. While this may simplify query optimization for users, it limits the level of control developers have over the performance tuning process.

In summary, Apache Drill provides more flexibility in terms of data source support, offers a cost advantage as an open-source project, and has better integration with the Apache Hadoop ecosystem. On the other hand, Google BigQuery is tightly integrated with Google Cloud Platform services, automatically scales to handle large datasets, and offers a simplified query optimization process.

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Advice on Google BigQuery, Apache Drill

Julien

CTO at Hawk

Sep 19, 2020

Decided

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

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Detailed Comparison

Google BigQuery	Apache Drill
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 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.
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.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	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;Nested data support;Integration with Apache Hive (queries on Hive tables and views, support for all Hive file formats and Hive UDFs);BI/SQL tool integration using standard JDBC/ODBC drivers
Statistics
Stacks 1.8K	Stacks 74
Followers 1.5K	Followers 171
Votes 152	Votes 16
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 4 NoSQL and Hadoop 3 Free 3 Lightning speed and simplicity in face of data jungle 2 Well documented for fast install 1 SQL interface to multiple datasources
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	No integrations available

What are some alternatives to Google BigQuery, Apache Drill?

dbForge Studio for MySQL

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dbForge Studio for PostgreSQL

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Qubole

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Related Comparisons

Apache Drill vs Google BigQuery: What are the differences?

Introduction

Flexibility and Data Source Support: Apache Drill offers more flexibility and supports a wider range of data sources compared to Google BigQuery. Apache Drill can efficiently query structured and semi-structured data stored in various formats such as JSON, Parquet, Avro, and more. On the other hand, Google BigQuery is primarily designed for structured data stored in Google Cloud Storage or Google Drive.
Cost Structure: The cost structure of Apache Drill and Google BigQuery differs significantly. Apache Drill is an open-source project that can be freely downloaded, installed, and used without incurring any additional charges. In contrast, Google BigQuery is part of the Google Cloud Platform and has a usage-based pricing model. Users are charged based on the amount of data processed and storage used.
Scalability: While both Apache Drill and Google BigQuery can handle large volumes of data, the underlying architecture and scalability options differ. Apache Drill leverages the distributed computing power of Apache Hadoop to scale horizontally and process data in parallel across a cluster. Google BigQuery, on the other hand, is a fully managed service that automatically scales to handle massive datasets without requiring manual configuration or infrastructure management.
Query Language Support: Apache Drill supports SQL queries, making it easy for developers familiar with SQL to interact with the data. In addition, Apache Drill also provides support for complex nested data structures through its SQL-based query language. Google BigQuery, on the other hand, uses a proprietary query language called BigQuery SQL, which is similar to SQL but has some additional syntax and features.
Integration with Ecosystem: Apache Drill integrates well with the Apache Hadoop ecosystem and can leverage other tools such as Apache Hive, Apache HBase, and more. This allows developers to easily combine the capabilities of these tools with Apache Drill for efficient data analysis. Google BigQuery, on the other hand, is tightly integrated with other Google Cloud Platform services, providing seamless integration with storage, compute, and analytics services offered by Google.
Performance Optimization: Apache Drill provides developers with fine-grained control over query execution and optimization, allowing them to tune performance according to their specific requirements. Google BigQuery, being a fully managed service, automatically optimizes query execution behind the scenes. While this may simplify query optimization for users, it limits the level of control developers have over the performance tuning process.

Apache Drill vs Google BigQuery

Overview

Apache Drill vs Google BigQuery: What are the differences?