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Amazon RDS for Aurora vs Google BigQuery: What are the differences?
Introduction
In this article, we will compare the key differences between Amazon RDS for Aurora and Google BigQuery. Both of these services are popular options for managing and analyzing data, but they have distinct features that differentiate them. Let's explore the differences below.
Data Warehousing vs Relational Database Management System (RDBMS): Amazon RDS for Aurora is a fully-managed relational database management system, whereas Google BigQuery is a cloud-based data warehousing solution. RDS for Aurora is designed for traditional applications that require structured data storage, while BigQuery is optimized for running complex analytical queries on large datasets.
Storage Architecture: RDS for Aurora uses a distributed storage architecture based on Amazon Aurora Storage, which replicates data across multiple Availability Zones for high availability and durability. On the other hand, BigQuery uses its proprietary columnar storage format called Capacitor, which provides automatic data compression and high performance for analytical workloads.
Query Processing and Scalability: RDS for Aurora supports traditional SQL queries and provides seamless compatibility with MySQL and PostgreSQL. It offers read replicas for horizontal scalability and can handle tens of thousands of transactions per second. In contrast, BigQuery offers a serverless data analytics platform that enables SQL-like queries on large datasets. It automatically parallelizes and distributes query execution across multiple nodes to achieve fast query response times.
Pricing Model: The pricing models for RDS for Aurora and BigQuery differ significantly. RDS for Aurora follows a pay-as-you-go pricing model based on the instance size and usage. It also offers different pricing tiers for different database engines and deployment options. On the other hand, BigQuery has a consumption-based pricing model that charges based on the amount of data processed by the queries and the storage volume. It provides flexible pricing options, including flat-rate and on-demand pricing.
Data Formats and Integration: RDS for Aurora supports a wide range of data formats, including JSON, XML, and Geospatial data, making it suitable for various types of applications. It also integrates well with other AWS services, allowing seamless data transfer and integration within the AWS ecosystem. In comparison, BigQuery supports a native JSON data type and works well with multiple data formats like CSV, Avro, Parquet, and more. It also provides built-in connectors to popular data sources such as Google Cloud Storage, Google Sheets, and Google Cloud Datastore.
Data Replication and Backup: RDS for Aurora offers automated data replication across multiple Availability Zones for increased durability and fault tolerance. It also provides automated backups and point-in-time recovery. In contrast, BigQuery automatically replicates data across multiple locations to ensure availability and durability. It stores data in a highly redundant manner and provides table snapshots for backup and recovery purposes.
In summary, Amazon RDS for Aurora is a powerful RDBMS that excels at traditional data storage and management. It offers compatibility with MySQL and PostgreSQL, high scalability, and seamless integration with other AWS services. On the other hand, Google BigQuery is a cloud-based data warehousing solution optimized for running analytical queries on large datasets. It offers a serverless platform, automatic parallel query execution, and flexible pricing based on data processing and storage volume.
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
Pros of Amazon Aurora
- MySQL compatibility14
- Better performance12
- Easy read scalability10
- Speed9
- Low latency read replica7
- High IOPS cost2
- Good cost performance1
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Amazon Aurora
- Vendor locking2
- Rigid schema1
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
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What is Amazon Aurora?
What is Google BigQuery?
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