Google BigQuery vs Apache Cassandra
A detailed comparison
Compare Google BigQuery and Apache Cassandra 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 Cassandra 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 Cassandra 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 Cassandra Breakdown
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 |
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| Database Model | Data warehouse |
Distributed wide-column database |
| 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 |
Apache Cassandra follows a masterless, peer-to-peer architecture, where each node in the cluster is functionally the same and communicates with other nodes using a gossip protocol. Data is distributed across nodes in the cluster using consistent hashing, and Cassandra supports tunable consistency levels for read and write operations. It can be deployed on-premises, in the cloud, or as a managed service |
| License | Closed source |
Apache 2.0 |
| Use Cases | Business analytics, large-scale data processing, data integration |
High write throughput applications, time series data, messaging systems, recommendation engines, IoT |
| Scalability | Serverless, petabyte-scale data warehouse that can handle massive amounts of data with no upfront capacity planning required |
Horizontally scalable with support for data partitioning, replication, and linear scalability as nodes are added |
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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 Cassandra Overview
Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.
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 Cassandra for Time Series Data
Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.
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 Cassandra Key Concepts
- Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
- Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
- Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
- Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.
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 Cassandra Architecture
Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.
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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 Cassandra Features
Linear Scalability
Cassandra can scale horizontally, adding nodes to the cluster to accommodate growing workloads and maintain consistent performance.
High Availability
With no single point of failure and support for data replication, Cassandra ensures data is always accessible, even in the event of node failures.
Tunable Consistency
Users can balance between data consistency and performance by adjusting consistency levels based on their application’s requirements.
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 Cassandra Use Cases
Messaging and Social Media Platforms
Cassandra’s high availability and low-latency make it suitable for messaging and social media applications that require fast, consistent access to user data.
IoT and Distributed Systems
With its ability to handle large amounts of data across distributed nodes, Cassandra is an excellent choice for IoT applications and other distributed systems that generate massive data streams.
E-commerce
Cassandra is a good fit for E-commerce use cases because it has the ability to support things like real-time inventory status and it’s architecture also allows for reduced latency by allowing region specific data to be closer to users.
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 Cassandra Pricing Model
Apache Cassandra is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Cassandra cluster. Additionally, several managed Cassandra services, such as DataStax Astra and Amazon Keyspaces, offer different pricing models based on factors like data storage, request throughput, and support.
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