Amazon RDS vs Google BigQuery

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Amazon RDS vs Google BigQuery: What are the differences?

Introduction

This Markdown code provides a comparison between Amazon RDS and Google BigQuery. These are two widely used platforms for different purposes: Amazon RDS for managing relational databases and Google BigQuery for analyzing large datasets. Below are the key differences between these two platforms.

Architecture: Amazon RDS is a managed database service that provides support for various relational database engines such as MySQL, PostgreSQL, Oracle, and SQL Server. It offers automated backups, software patching, and seamless scaling. On the other hand, Google BigQuery is a fully-managed serverless data warehouse that is designed for handling large-scale datasets. It uses a distributed architecture that enables fast and parallelized data processing.
Queries and Analytics: While both Amazon RDS and Google BigQuery allow executing SQL queries, there are differences in their analytics capabilities. Amazon RDS is more suitable for traditional OLTP (Online Transaction Processing) workloads, providing efficient transaction processing and low-latency querying. Google BigQuery, on the other hand, is optimized for OLAP (Online Analytical Processing) workloads and excels at running complex analytical queries on large datasets with its massively parallel processing.
Scalability: Amazon RDS provides automated scaling capabilities, allowing users to vertically scale (increase or decrease the instance size) or horizontally scale (add read replicas) their database instances based on workload demands. Google BigQuery, being a serverless platform, offers automatic scalability with virtually no limits on the amount of data it can handle. It can seamlessly handle petabytes of data without requiring any upfront provisioning.
Data Storage: Amazon RDS provides storage that is tightly coupled with the database instance size and has a maximum limit depending on the database engine. In contrast, Google BigQuery decouples storage from computation and follows a columnar storage model, which enables efficient compression and storage of data. Data in BigQuery is stored in tables and can be easily queried using SQL.
Cost Model: The pricing models for Amazon RDS and Google BigQuery differ. Amazon RDS charges for the provisioned compute and storage resources, with different pricing tiers based on the database engine chosen. On the other hand, Google BigQuery has a separate pricing structure for storage, querying, and data processing. It charges based on the amount of data processed and storage used, but also offers optional flat-rate pricing for predictable workloads.
Integration with Ecosystem: Amazon RDS is tightly integrated with the broader AWS ecosystem, allowing seamless integration with other AWS services such as AWS Lambda, Amazon S3, and Amazon Redshift. Google BigQuery, being a part of the Google Cloud Platform, integrates well with other GCP services like Google Data Studio, Google Bigtable, and Google Cloud Storage. The choice of platform may depend on the broader ecosystem and services offered.

In summary, Amazon RDS is a managed database service suitable for traditional OLTP workloads, providing scalability, and easy integration within the AWS ecosystem. On the other hand, Google BigQuery is a fully-managed serverless data warehouse optimized for OLAP workloads, offering automatic scalability, efficient data storage, and integration with the Google Cloud Platform ecosystem.

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Detailed Comparison

Amazon RDS	Google BigQuery
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.	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.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	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.
Statistics
Stacks 16.2K	Stacks 1.8K
Followers 10.8K	Followers 1.5K
Votes 761	Votes 152
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	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
Integrations
No integrations available	Xplenty Fluentd Looker Chartio Treasure Data

What are some alternatives to Amazon RDS, Google BigQuery?

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 Aurora

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.

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 vs Google BigQuery: What are the differences?

Introduction

Architecture: Amazon RDS is a managed database service that provides support for various relational database engines such as MySQL, PostgreSQL, Oracle, and SQL Server. It offers automated backups, software patching, and seamless scaling. On the other hand, Google BigQuery is a fully-managed serverless data warehouse that is designed for handling large-scale datasets. It uses a distributed architecture that enables fast and parallelized data processing.
Queries and Analytics: While both Amazon RDS and Google BigQuery allow executing SQL queries, there are differences in their analytics capabilities. Amazon RDS is more suitable for traditional OLTP (Online Transaction Processing) workloads, providing efficient transaction processing and low-latency querying. Google BigQuery, on the other hand, is optimized for OLAP (Online Analytical Processing) workloads and excels at running complex analytical queries on large datasets with its massively parallel processing.
Scalability: Amazon RDS provides automated scaling capabilities, allowing users to vertically scale (increase or decrease the instance size) or horizontally scale (add read replicas) their database instances based on workload demands. Google BigQuery, being a serverless platform, offers automatic scalability with virtually no limits on the amount of data it can handle. It can seamlessly handle petabytes of data without requiring any upfront provisioning.
Data Storage: Amazon RDS provides storage that is tightly coupled with the database instance size and has a maximum limit depending on the database engine. In contrast, Google BigQuery decouples storage from computation and follows a columnar storage model, which enables efficient compression and storage of data. Data in BigQuery is stored in tables and can be easily queried using SQL.
Cost Model: The pricing models for Amazon RDS and Google BigQuery differ. Amazon RDS charges for the provisioned compute and storage resources, with different pricing tiers based on the database engine chosen. On the other hand, Google BigQuery has a separate pricing structure for storage, querying, and data processing. It charges based on the amount of data processed and storage used, but also offers optional flat-rate pricing for predictable workloads.
Integration with Ecosystem: Amazon RDS is tightly integrated with the broader AWS ecosystem, allowing seamless integration with other AWS services such as AWS Lambda, Amazon S3, and Amazon Redshift. Google BigQuery, being a part of the Google Cloud Platform, integrates well with other GCP services like Google Data Studio, Google Bigtable, and Google Cloud Storage. The choice of platform may depend on the broader ecosystem and services offered.

Amazon RDS vs Google BigQuery

Overview