Google BigQuery vs Apache Doris
A detailed comparison
Compare Google BigQuery and Apache Doris for time series and OLAP workloads
Learn About Time Series DatabasesChoosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Google BigQuery and Apache Doris so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Google BigQuery and Apache Doris perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
Google BigQuery vs Apache Doris Breakdown
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 |
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| Database Model | Data warehouse |
Data warehouse |
| Architecture | BigQuery is a fully managed, serverless data warehouse provided by Google Cloud Platform. It is designed for high-performance analytics and utilizes Google’s infrastructure for data processing. BigQuery uses a columnar storage format for fast querying and supports standard SQL. Data is automatically sharded and replicated across multiple availability zones within a Google Cloud region |
Doris can be deployed on-premises or in the cloud and is compatible with various data formats such as Parquet, ORC, and JSON. |
| License | Closed source |
Apache 2.0 |
| Use Cases | Business analytics, large-scale data processing, data integration |
Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding |
| Scalability | Serverless, petabyte-scale data warehouse that can handle massive amounts of data with no upfront capacity planning required |
Horizontally scalable with distributed storage and compute |
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Google BigQuery Overview
Google BigQuery is a fully-managed, serverless data warehouse and analytics platform developed by Google Cloud. Launched in 2011, BigQuery is designed to handle large-scale data processing and querying, enabling users to analyze massive datasets in real-time. With a focus on performance, scalability, and ease of use, BigQuery is suitable for a wide range of data analytics use cases, including business intelligence, log analysis, and machine learning.
Apache Doris Overview
Apache Doris is an MPP-based interactive SQL data warehousing system designed for reporting and analysis. It is known for its high performance, real-time analytics capabilities, and ease of use. Apache Doris integrates technologies from Google Mesa and Apache Impala. Unlike other SQL-on-Hadoop systems, Doris is designed to be a simple and tightly coupled system that does not rely on external dependencies. It aims to provide a streamlined and efficient solution for data warehousing and analytics.
Google BigQuery for Time Series Data
BigQuery can be used for storing and analyzing time series data, although it is more focused on traditional data warehouse use cases. BigQuery may struggle for use cases where low latency response times are required
Apache Doris for Time Series Data
Apache Doris can be effectively used with time series data for real-time analytics and reporting. With its high performance and sub-second response time, Doris can handle massive amounts of time-stamped data and provide timely query results. It supports both high-concurrent point query scenarios and high-throughput complex analysis scenarios, making it suitable for analyzing time series data with varying levels of complexity.
Google BigQuery Key Concepts
Some important concepts related to Google BigQuery include:
- Projects: A project in BigQuery represents a top-level container for resources such as datasets, tables, and views.
- Datasets: A dataset is a container for tables, views, and other data resources in BigQuery.
- Tables: Tables are the primary data storage structure in BigQuery and consist of rows and columns.
- Schema: A schema defines the structure of a table, including column names, data types, and constraints.
Apache Doris Key Concepts
- MPP (Massively Parallel Processing): Apache Doris leverages MPP architecture, which allows it to distribute data processing across multiple nodes, enabling parallel execution and scalability.
- SQL: Apache Doris supports SQL as the query language, providing a familiar and powerful interface for data analysis and reporting.
- Point Query: Point query refers to retrieving a specific data point or a small subset of data from the database.
- Complex Analysis: Apache Doris can handle complex analysis scenarios that involve processing large volumes of data and performing advanced computations and aggregations.
Google BigQuery Architecture
Google BigQuery’s architecture is built on top of Google’s distributed infrastructure and is designed for high performance and scalability. At its core, BigQuery uses a columnar storage format called Capacitor, which enables efficient data compression and fast query performance. Data is automatically partitioned and distributed across multiple storage nodes, providing high availability and fault tolerance. BigQuery’s serverless architecture automatically allocates resources for queries and data storage, eliminating the need for users to manage infrastructure or capacity planning.
Apache Doris Architecture
Apache Doris is based on MPP architecture, which enables it to distribute data and processing across multiple nodes for parallel execution. It is a standalone system and does not depend on other systems or frameworks. Apache Doris combines the technology of Google Mesa and Apache Impala to provide a simple and tightly coupled system for data warehousing and analytics. It leverages SQL as the query language and supports efficient data processing and query optimization techniques to ensure high performance and scalability.
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Google BigQuery Features
Columnar Storage
BigQuery’s columnar storage format, Capacitor, enables efficient data compression and fast query performance, making it suitable for large-scale data analytics.
Integration with Google Cloud
BigQuery integrates seamlessly with other Google Cloud services, such as Cloud Storage, Dataflow, and Pub/Sub, making it easy to ingest, process, and analyze data from various sources.
Machine Learning Integration
BigQuery ML enables users to create and deploy machine learning models directly within BigQuery, simplifying the process of building and deploying machine learning applications.
Apache Doris Features
High Performance
Apache Doris is designed for high-performance data analytics, delivering sub-second query response times even with massive amounts of data.
Real-Time Analytics
Apache Doris enables real-time data analysis, allowing users to gain insights and make informed decisions based on up-to-date information.
Scalability
Apache Doris can scale horizontally by adding more nodes to the cluster, allowing for increased data storage and processing capacity.
Google BigQuery Use Cases
Business Intelligence and Reporting
BigQuery is widely used for business intelligence and reporting, enabling users to analyze large volumes of data and generate insights to inform decision-making. Its fast query performance and seamless integration with popular BI tools, such as Google Data Studio and Tableau, make it an ideal solution for this use case.
Machine Learning and Predictive Analytics
BigQuery ML enables users to create and deploy machine learning models directly within BigQuery, simplifying the process of building and deploying machine learning applications. BigQuery’s fast query performance and support for large-scale data processing make it suitable for predictive analytics use cases.
Data Warehousing and ETL
BigQuery’s distributed architecture and columnar storage format make it an excellent choice for data warehousing and ETL (Extract, Transform, Load) workflows. Its seamless integration with other Google Cloud services, such as Cloud Storage and Dataflow, simplifies the process of ingesting and processing data from various sources.
Apache Doris Use Cases
Real-Time Analytics
Apache Doris is well-suited for real-time analytics scenarios where timely insights and analysis of large volumes of data are crucial. It enables businesses to monitor and analyze real-time data streams, make data-driven decisions, and detect patterns or anomalies in real time.
Reporting and Business Intelligence
Apache Doris can be used for generating reports and conducting business intelligence activities. It supports fast and efficient querying of data, allowing users to extract meaningful insights and visualize data for reporting and analysis purposes.
Data Warehousing
Apache Doris is suitable for building data warehousing solutions that require high-performance analytics and querying capabilities. It provides a scalable and efficient platform for storing, managing, and analyzing large volumes of data for reporting and decision-making.
Google BigQuery Pricing Model
Google BigQuery pricing is based on a pay-as-you-go model, with costs determined by data storage, query, and streaming. There are two main components to BigQuery pricing:
- Storage Pricing: Storage costs are based on the amount of data stored in BigQuery. Users are billed for both active and long-term storage, with long-term storage offered at a discounted rate for infrequently accessed data.
- Query Pricing: Query costs are based on the amount of data processed during a query. Users can choose between on-demand pricing, where they pay for the data processed per query, or flat-rate pricing, which provides a fixed monthly cost for a certain amount of query capacity.
Apache Doris Pricing Model
As an open-source project, Apache Doris is freely available for usage and does not require any licensing fees. Users can download the source code and set up Apache Doris on their own infrastructure without incurring any direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
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