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Druid vs Google BigQuery: What are the differences?
Introduction
Druid and Google BigQuery are both powerful data processing and analytics tools used for working with large volumes of data. While they share similarities in terms of their ability to handle big data, there are key differences that set them apart. In this article, we will explore the main differences between Druid and Google BigQuery.
Data Storage Architecture: Druid is designed as a column-oriented distributed data store that is optimized for time-series data. It uses a combination of in-memory and disk-based storage to provide fast querying and aggregation capabilities. On the other hand, Google BigQuery is a fully managed serverless data warehouse that stores data in a columnar format using its proprietary Dremel storage system.
Query Execution Model: Druid follows a distributed query execution model where data is partitioned and distributed across multiple nodes, allowing queries to be executed in parallel. It also supports pre-aggregated data cubes and caching mechanisms for faster query response. In contrast, Google BigQuery uses a massively parallel processing (MPP) architecture that automatically scales resources to handle large datasets and query volumes, making it ideal for ad-hoc queries and interactive analysis.
Data Ingestion: Druid supports real-time data ingestion, meaning it can handle continuous streams of data as they are generated. It provides built-in support for ingesting data from various sources such as Kafka, Hadoop, and AWS S3. On the other hand, Google BigQuery primarily focuses on batch processing, although it does offer some support for streaming data using technologies like Cloud Pub/Sub and Dataflow.
Query Language: Druid uses a SQL-like language called Druid Query Language (DSL) for querying and aggregating data. The DSL is specifically optimized for time-series analytics and provides advanced features like approximate and hyper-unique aggregators. In contrast, Google BigQuery uses a dialect of SQL known as BigQuery SQL, which is fully compliant with the ANSI SQL standard and includes additional features for working with nested and repeated data structures.
Data Governance and Security: Google BigQuery provides robust data governance and security features, including identity and access management (IAM) controls, encryption at rest and in transit, audit logs, and data access controls through VPC Service Controls. Druid, on the other hand, does not offer built-in security features and relies on integrating with external systems like Apache Kafka for secure data ingestion and access.
Cost Structure: The pricing model of Druid and Google BigQuery differs significantly. Druid is an open-source project and can be self-hosted, reducing the overall cost of infrastructure. However, managing and scaling the infrastructure requires additional effort and expertise. On the other hand, Google BigQuery follows a pay-as-you-go pricing model, where you are billed based on the amount of data processed and the compute resources used.
In summary, Druid is a column-oriented distributed data store optimized for time-series data, supporting real-time data ingestion and offering a specialized query language. Google BigQuery, on the other hand, is a fully managed serverless data warehouse that uses a massively parallel processing architecture and provides robust data governance and security features, albeit at a higher cost.
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 Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP5
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
Cons of Google BigQuery
- You can't unit test changes in BQ data1