Druid vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Druid

Stacks376

Followers867

Votes32

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.

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Advice on Google BigQuery, Druid

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	Druid
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.	Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
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.	-
Statistics
Stacks 1.8K	Stacks 376
Followers 1.5K	Followers 867
Votes 152	Votes 32
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 15 Real Time Aggregations 6 Batch and Real-Time Ingestion 5 OLAP 3 OLAP + OLTP 2 Combining stream and historical analytics Cons 3 Limited sql support 2 Joins are not supported well 1 Complexity
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	Zookeeper

What are some alternatives to Google BigQuery, Druid?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Related Comparisons

Druid vs Google BigQuery: What are the differences?

Introduction

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.

Druid vs Google BigQuery

Overview

Druid vs Google BigQuery: What are the differences?