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Amazon Relational Database Service (RDS) vs. Google BigQuery

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Amazon RDS

    Score8.1 out of 10
    N/AAmazon Relational Database Service (Amazon RDS) is a database-as-a-service (DBaaS) from Amazon Web Services.N/A

    Google BigQuery

    Score8.8 out of 10
    N/AGoogle's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

    $6.25

    per TiB (after the 1st 1 TiB per month, which is free)

    Pricing
    Amazon RDSGoogle BigQuery
    Editions & Modules
    Amazon RDS for PostgreSQL
    $0.24 ($0.48)
    per hour, R5 Large (R5 Extra Large)
    Amazon RDS for MariaDB
    $0.25 ($0.50)
    per hour, R5 Large (R5 Extra Large)
    Amazon RDS for MySQL
    $0.29 ($0.58)
    per hour, R5 Large (R5 Extra Large)
    Amazon RDS for Oracle
    $0.482 ($0.964)
    per hour, R5 Large (R5 Extra Large)
    Amazon RDS for SQL Server
    $1.02 ($1.52)
    per hour, R5 Large (R5 Extra Large)
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Offerings
    Pricing Offerings
    Amazon RDSGoogle BigQuery
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeOptionalNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    Amazon RDSGoogle BigQuery
    Considered Both Products
    Amazon AWS
    Chose Amazon RDS
    Amazon RDS provides more configuration ability, and also it's scalable and highly available for real-time response to the complex query.
    Incentivized
    Chose Amazon RDS
    I try not to compare services, as I know that every project has specific requirements, and every service is slightly different. However, if you have chosen AWS, and you are setting up a LAMP, and have no plans for rapid growth, then RDS is a must. If you have not chosen a …
    Incentivized
    Google
    No answer on this topic
    Key User Insights
    Would buy again
    99%
    Would buy again
    102 Answers
    98%
    Would buy again
    64 Answers
    Delivers good value for the price
    98%
    Delivers good value for the price
    98 Answers
    97%
    Delivers good value for the price
    56 Answers
    Happy with the feature set
    99%
    Happy with the feature set
    102 Answers
    97%
    Happy with the feature set
    63 Answers
    Lived up to sales and marketing promises
    99%
    Lived up to sales and marketing promises
    83 Answers
    100%
    Lived up to sales and marketing promises
    42 Answers
    Implementation went as expected
    96%
    Implementation went as expected
    96 Answers
    100%
    Implementation went as expected
    59 Answers
    Features
    Amazon RDSGoogle BigQuery
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Amazon Relational Database Service (RDS) and Google BigQuery
    Feature
    Amazon Relational Database Service (RDS)
    -
    Ratings
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Automatic software patching00 Ratings8.017 Ratings
    Database scalability00 Ratings9.079 Ratings
    Automated backups00 Ratings8.524 Ratings
    Database security provisions00 Ratings8.873 Ratings
    Monitoring and metrics00 Ratings8.675 Ratings
    Automatic host deployment00 Ratings8.013 Ratings
    Best Alternatives
    Amazon RDSGoogle BigQuery
    Small Businesses
    Microsoft Access
    Score7.5 out of 10
    MongoDB Atlas
    Score7.8 out of 10
    Medium-sized Companies
    SingleStore
    Score8.2 out of 10
    Azure Database
    Score8.8 out of 10
    Enterprises
    SAP IQ
    Score5.5 out of 10
    Google Cloud SQL
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Amazon RDSGoogle BigQuery
    Likelihood to Recommend
    8.9
    (131 ratings)
    9.0
    (79 ratings)
    Likelihood to Renew
    8.4
    (5 ratings)
    8.1
    (5 ratings)
    Usability
    8.8
    (8 ratings)
    6.6
    (6 ratings)
    Availability
    9.0
    (1 ratings)
    7.3
    (1 ratings)
    Performance
    7.0
    (1 ratings)
    6.4
    (1 ratings)
    Support Rating
    9.6
    (13 ratings)
    4.8
    (11 ratings)
    Online Training
    10.0
    (1 ratings)
    -
    (0 ratings)
    Configurability
    -
    (0 ratings)
    6.4
    (1 ratings)
    Contract Terms and Pricing Model
    -
    (0 ratings)
    10.0
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    7.3
    (1 ratings)
    Product Scalability
    9.0
    (1 ratings)
    7.3
    (1 ratings)
    Professional Services
    -
    (0 ratings)
    8.2
    (2 ratings)
    User Testimonials
    Amazon RDSGoogle BigQuery
    Likelihood to Recommend
    Amazon AWS
    If your application needs a relational data store and uses other AWS services, AWS RDS is a no-brainer. It offers all the traditional database features, makes it a snap to set up, creates cross-region replication, has advanced security, built-in monitoring, and much more at a very good price. You can also set up streaming to a data lake using various other AWS services on your RDS.
    Incentivized
    Read full review
    Google
    Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
    Incentivized
    Read full review
    Pros
    Amazon AWS
    • Automated Database Management: We use it for streamlining routine tasks like software patching and database backups.
    • Scalability on Demand: we use it to handle traffic spikes, scaling both vertically and horizontally.
    • Database Engine Compatibility: It works amazingly with multiple database engines used by different departments within our organization including MySQL, PostgreSQL, SQL Server, and Oracle.
    • Monitoring: It covers our extensive monitoring and logging, and also has great compatibility with Amazon CloudWatch
    Incentivized
    Read full review
    Google
    • Realtime integration with Google Sheets.
    • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
    • Seamless integration with other GCP products.
    • A simple pipeline might look like this:-
    • GForms -> GSheets -> BigQuery -> Looker
    • It all links up really well and with ease.
    • One instance holds many projects.
    • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
    Incentivized
    Read full review
    Cons
    Amazon AWS
    • It is a little difficult to configure and connect to an RDS instance. The integration with ECS can be made more seamless.
    • Exploring features within RDS is not very easy and intuitive. Either a human friendly documentation should be added or the User Interface be made intuitive so that people can explore and find features on their own.
    • There should be tools to analyze cost and minimize it according to the usage.
    Incentivized
    Read full review
    Google
    • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
    • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
    • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
    Incentivized
    Read full review
    Likelihood to Renew
    Amazon AWS
    We do renew our use of Amazon Relational Database Service. We don't have any problems faced with RDS in place. RDS has taken away lot of overhead of hosting database, managing the database and keeping a team just to manage database. Even the backup, security and recovery another overhead that has been taken away by RDS. So, we will keep on using RDS.
    Incentivized
    Read full review
    Google
    We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
    Incentivized
    Read full review
    Usability
    Amazon AWS
    I've been using AWS Relational Database Services in several projects in different environments and from the AWS products, maybe this one together to EC2 are my favourite. They deliver what they promise. Reliable, fast, easy and with a fair price (in comparison to commercial products which have obscure license agreements).
    Incentivized
    Read full review
    Google
    I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
    Incentivized
    Read full review
    Reliability and Availability
    Amazon AWS
    No answers on this topic
    Google
    I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
    Incentivized
    Read full review
    Performance
    Amazon AWS
    No answers on this topic
    Google
    I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
    Incentivized
    Read full review
    Support Rating
    Amazon AWS
    I have only had good experiences in working with AWS support. I will admit that my experience comes from the benefit of having a premium tier of support but even working with free-tier accounts I have not had problems getting help with AWS products when needed. And most often, the docs do a pretty good job of explaining how to operate a service so a quick spin through the docs has been useful in solving problems.
    Incentivized
    Read full review
    Google
    BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
    Incentivized
    Read full review
    Online Training
    Amazon AWS
    the online training & digital content available on the web from AWS was having sufficient information to deploy and run the service
    Incentivized
    Read full review
    Google
    No answers on this topic
    Alternatives Considered
    Amazon AWS
    Amazon Relational Database Service (RDS) stands out among similar products due to its seamless integration with other AWS services, automated backups, and multi-AZ deployments for high availability. Its support for various database engines, such as MySQL, PostgreSQL, and Oracle, provides flexibility. Additionally, RDS offers managed security features, including encryption and IAM integration, enhancing data protection. The pay-as-you-go pricing model makes it cost-effective. Overall, Amazon RDS excels in ease of use, scalability, and a comprehensive feature set, making it a top choice for organizations seeking a reliable and scalable managed relational database service in the cloud.
    Read full review
    Google
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
    Read full review
    Contract Terms and Pricing Model
    Amazon AWS
    No answers on this topic
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Scalability
    Amazon AWS
    No answers on this topic
    Google
    We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
    Incentivized
    Read full review
    Professional Services
    Amazon AWS
    No answers on this topic
    Google
    Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
    Read full review
    Return on Investment
    Amazon AWS
    • The overall cost increases, but we spect this and we can mitigate other risks.
    • Is easy to work from the cloud. Is reliable, but we keep our local solution as well where RDS works quite good.
    • RDS allow us to focurs on owr objetives instead of the other matters regarding databases.
    Incentivized
    Read full review
    Google
    • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
    • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
    • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
    Incentivized
    Read full review
    ScreenShots

    Amazon RDS Screenshots

    Screenshot of A look inside the RDS console.

    Google BigQuery Screenshots

    Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.