Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Athena
Score 8.4 out of 10
N/A
Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. With a few clicks in the AWS Management Console, customers can point Athena at their data stored in S3 and begin using standard SQL to run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to setup or manage, and customers pay only for the queries they run. You can use Athena to process logs, perform ad-hoc analysis, and run…
$5
per TB of Data Scanned
Google BigQuery
Score 8.6 out of 10
N/A
Google'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 AthenaGoogle BigQuery
Editions & Modules
Price per Query
$5.00
per TB of Data Scanned
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
Amazon AthenaGoogle BigQuery
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon AthenaGoogle BigQuery
Considered Both Products
Amazon Athena

No answer on this topic

Google BigQuery
Chose Google BigQuery
There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
Chose Google BigQuery
BigQuery has a simpler and more intuitive user experience (as is the case with most of its products) compared to AWS, which has a more technical and complex profile, so it was the first tool we used. It's still my go-to option for handling SQL queries, though it doesn't detract …
Chose Google BigQuery
We based our analysis primarily on [BigQuery vs. Redshift vs. Athena] and BigQuery proved to be the best solution for us.
Top Pros
Top Cons
Features
Amazon AthenaGoogle BigQuery
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Amazon Athena
8.5
3 Ratings
3% below category average
Google BigQuery
8.4
55 Ratings
4% below category average
Automatic software patching8.22 Ratings8.117 Ratings
Database scalability8.22 Ratings8.955 Ratings
Automated backups7.73 Ratings8.524 Ratings
Database security provisions9.22 Ratings8.748 Ratings
Monitoring and metrics8.83 Ratings8.450 Ratings
Automatic host deployment9.22 Ratings8.113 Ratings
Best Alternatives
Amazon AthenaGoogle BigQuery
Small Businesses
IBM Cloudant
IBM Cloudant
Score 8.2 out of 10
IBM Cloudant
IBM Cloudant
Score 8.2 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 8.2 out of 10
IBM Cloudant
IBM Cloudant
Score 8.2 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 8.2 out of 10
IBM Cloudant
IBM Cloudant
Score 8.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon AthenaGoogle BigQuery
Likelihood to Recommend
9.5
(3 ratings)
8.6
(55 ratings)
Likelihood to Renew
-
(0 ratings)
7.0
(1 ratings)
Usability
-
(0 ratings)
9.4
(3 ratings)
Support Rating
-
(0 ratings)
10.0
(9 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
User Testimonials
Amazon AthenaGoogle BigQuery
Likelihood to Recommend
Amazon AWS
If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.
Read full review
Google
Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Read full review
Pros
Amazon AWS
  • Nested Schemas like JSON data structure
  • Ability to adapt the data model to fit your queries better
  • Performance Improvement
Read full review
Google
  • Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
  • Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
  • Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
Read full review
Cons
Amazon AWS
  • Query manager can incoperate GUI based query designer
  • Auto-completion engine sometimes overwrite the query
  • Time range selection should be implicit
Read full review
Google
  • It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
  • The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
Read full review
Likelihood to Renew
Amazon AWS
No answers on this topic
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.
Read full review
Usability
Amazon AWS
No answers on this topic
Google
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
Read full review
Support Rating
Amazon AWS
No answers on this topic
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.
Read full review
Alternatives Considered
Amazon AWS
Amazon Athena, a product from Amazon, competes with offerings from Google and Microsoft. Overall, I think your database choice depends on some of the other applications you are running at your company. For example, if you are using Microsoft Power BI for reporting needs, you might want to consider going the Azure route.
Read full review
Google
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
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
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
  • Easy to query terabytes of data with faster response
  • Pricing model is also cheap
  • No indexing and partitioning
Read full review
Google
  • Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free.
  • Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized.
  • The visuals of ML models is very helpful to fine tune training, model building and prediction, etc.
Read full review
ScreenShots

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.