Amazon RDS for Aurora vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Amazon Aurora

Stacks807

Followers745

Votes55

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.

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Advice on Google BigQuery, Amazon Aurora

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	Amazon Aurora
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.	Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.
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.	High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
Stacks 1.8K	Stacks 807
Followers 1.5K	Followers 745
Votes 152	Votes 55
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 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	PostgreSQL MySQL

What are some alternatives to Google BigQuery, Amazon Aurora?

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

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.

Amazon EMR

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

Google Cloud SQL

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

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.

Azure SQL Database

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Related Comparisons

Amazon RDS for Aurora vs Google BigQuery: What are the differences?

Introduction

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.

Amazon RDS for Aurora vs Google BigQuery

Overview

Amazon RDS for Aurora vs Google BigQuery: What are the differences?