Choosing 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 AWS Redshift so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Google BigQuery and AWS Redshift 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 AWS Redshift Breakdown
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
AWS Redshift utilizes a columnar storage format for fast querying and supports standard SQL. Redshift uses a distributed, shared-nothing architecture, where data is partitioned across multiple compute nodes. Each node is further divided into slices, with each slice processing a subset of data in parallel. Redshift can be deployed in a single-node or multi-node cluster, with the latter providing better performance for large datasets.
Business analytics, large-scale data processing, data integration
Business analytics, large-scale data processing, real-time dashboards, data integration, machine learning
Serverless, petabyte-scale data warehouse that can handle massive amounts of data with no upfront capacity planning required
Supports scaling storage and compute independently, with support for adding or removing nodes as needed
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.
AWS Redshift Overview
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.
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
AWS Redshift for Time Series Data
AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.
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.
AWS Redshift Key Concepts
- Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
- Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
- Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
- Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded data.
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.
AWS Redshift Architecture
Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.
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Google BigQuery Features
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.
AWS Redshift Features
Redshift allows you to scale your cluster up or down by adding or removing compute nodes, enabling you to adjust your storage capacity and query performance based on your needs.
Redshift’s columnar storage format and MPP architecture enable it to deliver high-performance query execution for large-scale data warehousing workloads.
Redshift provides a range of security features, including encryption at rest and in transit, network isolation using Amazon Virtual Private Cloud (VPC), and integration with AWS Identity and Access Management (IAM) for access control.
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.
AWS Redshift Use Cases
Redshift is designed for large-scale data warehousing workloads, providing a scalable and high-performance solution for storing and analyzing structured data.
Business Intelligence and Reporting
Redshift integrates with various BI and reporting tools, enabling organizations to gain insights from their data and make data-driven decisions.
ETL and Data Integration
Redshift supports data loading and extraction, transformation, and loading (ETL) processes, allowing you to integrate data from various sources and prepare it for analysis.
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.
AWS Redshift Pricing Model
Amazon Redshift offers two pricing models: On-Demand and Reserved Instances. With On-Demand pricing, you pay for the capacity you use on an hourly basis, with no long-term commitments. Reserved Instances offer the option to reserve capacity for a one- or three-year term, with a lower hourly rate compared to On-Demand pricing. In addition to these pricing models, you can also choose between different node types, which offer different amounts of storage, memory, and compute resources.
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