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BigQuery vs Druid

A detailed comparison

Compare BigQuery vs Druid by the following set of categories:

BigQuery was one of the first decoupled storage and compute architectures. It is a unique piece of engineering and not a typical data warehouse in part because it started as an on-demand serverless query engine. It runs in multi-tenancy with shared resources, allocated as “slots” which represent a virtual CPU that executes SQL. BigQuery determines how many slots a query requires, without the ability of the user to control it.

Druid is an OLAP engine designed to provide fast real time analytics. Druid adopts a clustered architecture with servers that host various role specific processes. These processes address real time and batch ingestion, indexing, querying of historical and real time data. Apache Druid can be deployed as a virtual machine or a Kubernetes based cluster. Druid does not support a decoupled compute & storage architecture. Deep storage in the form of object storage is used to replicate data to.

BigQuery vs Druid - Architecture

The biggest difference among cloud data warehouses are whether they separate storage and compute, how much they isolate data and compute, and what clouds they can run on.

BigQuery scales very well to large data volumes, and automatically assigns more compute resources when needed behind the scenes, in the form of “slots”. BigQuery works either in an “on-demand pricing model”, where slot assignment is completely in the hands of BigQuery and the state of the shared resource pool, or in “flat-rate pricing model” where slots are reserved in advance. With reserved slots there is more control over compute resources, thus making scaling more predictable. Concurrency is limited to 100 users by default.

Druid provides the ability to handle fast ingest and high concurrency. Custom sizing and cluster tuning are required to balance the compute, memory, storage needs of each process within Druid and to provide high concurrency. Druid clusters can be grown by adding nodes with automatic rebalancing of storage segments assigned to nodes.

BigQuery vs Druid - Scalability

There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.

BigQuery lines up in benchmarks in the same ballpark as other cloud data warehouses but does come in consistently last in most queries. Beyond implementing according to best practices, there is little you can do to accelerate BigQuery performance, as it determines the amount of resources (slots) the query needs for you. BigQuery can be used together with the “BigQuery BI Engine” for lower latency analytics. However, BI Engine is limited in terms of scale because it runs in memory. Its maximum capacity is 100GB.

Druid provides high performance through columnar storage format, parallel processing, bitmap indexes and roll-ups. Druid, however, recommends a denormalized data model for performance needs. Join operations in Druid are a relatively new feature with various limitations, especially if there is a need to join large datasets.

BigQuery vs Druid - Performance

Performance is the biggest challenge with most data warehouses today.
While decoupled storage and compute architectures improved scalability and simplified administration, for most data warehouses it introduced two bottlenecks; storage, and compute. Most modern cloud data warehouses fetch entire partitions over the network instead of just fetching the specific data needed for each query. While many invest in caching, most do not invest heavily in query optimization. Most vendors also have not improved continuous ingestion or semi-structured data analytics performance, both of which are needed for operational and customer-facing use cases.

BigQuery is a mature general-purpose data warehouse, which lends itself well to internal BI & reporting. The fact that it’s serverless in nature and tightly integrated with GCP, makes it very convenient for Ad-Hoc analytics and ML use cases on GCP. On the other hand, because BigQuery makes resource allocation decisions for you, it is not always the best fit for operational use cases and Data Apps where performance needs to be consistent and predictable.

Druid is designed as an OLAP engine to provide fast access to aggregations that are run against large volumes of data. Druid is typically used for customer facing analytics and streaming data processing. Druid is used as an add-on with other data warehousing products that are efficient at scaling, joining, and filtering large volumes of data. It is not a suitable option for data warehouse replacement.

BigQuery vs Druid - Use cases

There are a host of different analytics use cases that can be supported by a data warehouse. Look at your legacy technologies and their workloads, as well as the new possible use cases, and figure out which ones you will need to support in the next few years.

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