Google BigQuery vs. Google Cloud Storage

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.4 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.
$0.04
Google Cloud Storage
Score 8.5 out of 10
N/A
Google Cloud Storage is unified object storage for developers and enterprises.N/A
Pricing
Google BigQueryGoogle Cloud Storage
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
No answers on this topic
Offerings
Pricing Offerings
Google BigQueryGoogle Cloud Storage
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Google BigQueryGoogle Cloud Storage
Considered Both Products
Google BigQuery
Chose Google BigQuery
is much better as it’s easily accessible provides velvet documentation and fulfils all our needs as well as easily integrated into clients, environment
Chose Google BigQuery
Google BigQuery is simpler and I say it has simpler UI too.
If you have a clear long term ask , mainly business intelligence needs then Google BigQuery offers you good.
If you need too much of features under a single cloud and you are ok to be lil clumsy then you can check …
Chose Google BigQuery
I have used most of the data analytics platforms. Based on my work, I have found that the user interface of Google BigQuery is simple to navigate. I like the front view - ease of joining tables, and integration with other platforms.
Chose Google BigQuery
Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale.
The price was also much lower for our use case (internal data analysis).
Chose Google BigQuery
For our usage, Google BigQuery is cheaper and more performant. The others have their place, but in certain scenarios, Google BigQuery is a better solution.
Chose Google BigQuery
We actually use Snowflake and BigQuery in tandem because they both currently meet various needs. Redshift, however, has barely been used since our migration away from it. In the case of both Snowflake and BigQuery, they beat Redshift by a long shot. The main reasons are their …
Chose Google BigQuery
I came to use BigQuery from a traditional system like MS SQL server, the features which are available in BigQuery as a cloud service far outweigh the features from SQL server. I have not used other similar tools like Amazon Redshift but Google BigQuery serves multiple use cases …
Chose Google BigQuery
Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than snowflake. Redshift is not easy …
Chose Google BigQuery
In my opinion, Google BigQuery is custom made to be the best data lake system that is easy to use, scalas to fit any business size, has inbuilt security, as well as tools for data integrity. Although a few other tools have some of the same functionality, Google BigQuery is the …
Chose Google BigQuery
It's easier to connect data between BigQuery and looker studio instead of connecting the data between BigQuery and tableau in terms of data explore or dashboard creating. Therefore we are considering migrating dashboards from tableau to looker studio for the whole company.
On …
Chose Google BigQuery
When comparing Google BigQuery and Databricks, both platforms are powerful tools for managing and analyzing large datasets. BQ is ideal for businesses requiring large-scale analytics, reporting, and dashboarding with minimal operational overhead. It’s also great for ad-hoc …
Chose Google BigQuery
Google BigQuery's main advantage over its direct competitors (Amazon Redshift and Azure Synapse) is that it is widely supported by non-Google software, while the others rely heavily on their own cloud ecosystems.
Chose Google BigQuery
I have used other data manipulation tools like SQL Server and Google BigQuery feels more intuitive, Google provides so much documentation and tutorials that getting to know the software is not only easy but even satisfactory, so I'd say Google BigQuery is very superior to that …
Chose Google BigQuery
Amazon Redshift was a likely alternative we were considering , but it needs to be provisioned on cluster and nodes, which increases infrastructure management, whereas Google BigQuery is serverless, so no infra management :) Also, I remember when comparing them we did found out …
Chose Google BigQuery
Its same as compared to Big query. We go with big query because of clients requirements in project.
Chose Google BigQuery
Google BigQuery as a platform allows for more integrations and customizability than many other offerings. Users mostly need to understand the basics of database and SQL programming in order to get the most from the product. However, other products like Hevo do have less of a …
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
The data performance of Google BigQuery is best as per other software. Limitations on Google BigQuery's data size are superior to those of Microsoft SQL. Obtaining real-time data from several IoT devices is another benefit.
Chose Google BigQuery
I personally find it by far simpler than Amazon redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily …
Chose Google BigQuery
Compared to SingleStore, BigQuery has a big advantage of being completely serverless, and without practical limitations.

Compared to RedShift, we found the cost model to be more fitted to our needs.
Chose Google BigQuery
BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.
Chose Google BigQuery
We focused more on data volume and less on full application capabilities. All in all, we found that the two solutions complement each other. For integration, some sources were better handled in SAP HANA, particularly other SAP systems where Google Big Query was more suitable …
Chose Google BigQuery
SingleStore has a much lower query latency compared to BigQuery. Thus, we segregate faster tasks to SingleStore, and use BigQuery has our main database to store all historical data.
Chose Google BigQuery
Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of …
Chose Google BigQuery
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Google Cloud Storage
Chose Google Cloud Storage
Google Cloud Storage feels much more intuitive to use versus Amazon S3. I really prefer Google's Node SDK and web interface, and find Google Cloud Platform's access/identity management much more straightforward.
Chose Google Cloud Storage
This software is perfect for the organization where a huge amount of data are generated. The software has the perfect features by which the organization can store their information safely. The software has the features where one can share cloud with colleagues for project …
Chose Google Cloud Storage
Google had the easiest and most secure file sharing abilities, and was a more trusted name for our clients to be assured that their files would remain private.
Chose Google Cloud Storage
We have used Amazon S3 for similar needs, or for solutions that don't support Google Cloud Storage. For us, Google Cloud Storage works much better since it's integrated into Google Cloud Platform. Since we use Google Cloud Platform, Google Cloud Storage is part of our cloud …
Chose Google Cloud Storage
The two services are very comparable, but we have many different services that all run on the Google Cloud Platform and therefore Google Cloud Storage made more sense as our storage solution rather than looking to an outside service like S3. Either one of these options would …
Chose Google Cloud Storage
Google Cloud Storage has been experiencing less technical issues and is more straight forward to use, for our technical team, it saves training time by having a straight forward help and FAQ section. We needed very little training, as opposed to previously used other tools …
Chose Google Cloud Storage
Google Cloud offers Google Cloud Storage, while AWS offers Amazon Simple Storage Services. In Google Cloud services, data transmission is a fully encrypted format on the other hand, in AWS, data transmission is in the general format. Google Cloud volume size is 1 GB to 64 TB …
Chose Google Cloud Storage
The big difference against other competitors that offer a Cloud storage solution, is the fact that Google Cloud Storage and other Google Cloud Platform products are billed in the local currency. This fact saves a lot of money because in some countries we have to pay for …
Chose Google Cloud Storage
Aside from Google Cloud Storage, we've used AWS S3 and have found the two comparable. In fact, GCS is one of a large number of object storage systems that are compatible with S3 (including DigitalOcean Spaces, IBM Cloud Storage, and Azure Blog Storage). When it comes to these …
Chose Google Cloud Storage
Google Cloud Storage effectively outweighs Google Drive's limited capabilities. This software, being a paid version, allows for much room for extra storage space, faster loading times from my experience, and easy sharing features. Compared to Google Drive, Google Cloud Storage …
Chose Google Cloud Storage
I chose Google Cloud Storage due to its implementation with Google Firestore. Both worked off each other and made our connection with Google Firestore a lot simpler.
Chose Google Cloud Storage
Google Cloud Storage is the only right option for media and document files. Unless you need to work with structured information where Cloud SQL or Datastore may be better options, storing files in Cloud Storage is easy and right to the point. Also, prices are worth considering …
Chose Google Cloud Storage
Google Cloud Storage compares very similar to Dropbox. The difference of using Googe Cloud Storage is that it is part of a big bundle of products that you are probably already using. If you are a big Google user then it would make sense to get Google Cloud Storage. This way you …
Chose Google Cloud Storage
Google Cloud Storage is the only one I have tried. But when researching we looked at Google Cloud, as well as Microsoft and Amazon. Google was more expensive, but seemed to be more of what we were looking for. Again, not really my department.
Chose Google Cloud Storage
One drive only offers you 5GB free space when google gives you 15 GB. and google cloud as better performance. even with real-time sync.
Chose Google Cloud Storage
Google Cloud Storage is really a different application. It does not have the data parsing capabilities and it is simply a cold storage option in our use case. This means it is less flexible and has limited uses, but the uses it does have it, performs very well. There is a very …
Chose Google Cloud Storage
Guru and Google Cloud Storage are different in many ways. They are by no means the same product, but our team has begun to move away from Google for our documentation of processes. Guru has a superior system for organizing documents and articles.
Chose Google Cloud Storage
I have used Microsoft One Drive, Dropbox, Box, Amazon Cloud Drive, and Google Cloud Storage. All of this cloud storage software do pretty much does the same thing, but the difference for me is in ease of use— for me the easiest to use is Google Drive. I will say this, I use …
Chose Google Cloud Storage
Easier to set up and has a natural interface that is well known to users so it is easy for them to assimilate. Google Cloud Storage is faster than any other option we tried and works perfectly with our Chromebooks. From an ROI perspective, it is the best option for a medium …
Chose Google Cloud Storage
We tend to only select Google Cloud Storage when we're using other Google products as it makes integration easier -- but we tend not to choose it for situations where we can use a competing offering from Azure or AWS. We tend to implement the most Azure and AWS by far, and …
Chose Google Cloud Storage
We selected GCS vs. others because we decided to use other Google Cloud services. Since we integrated GCS into our tools, we're still using GCS today, even though we've largely transitioned away from Google Compute services. GCS is still a very solid choice, even if your server …
Chose Google Cloud Storage
We ended up with Google Cloud Storage most importantly because we found it far easier to set up, configure, and operate compared to Amazon's offerings. Amazon's many products make it difficult to find just the right one, and from there configuring is overly complicated. In …
Features
Google BigQueryGoogle Cloud Storage
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.4
Ratings
3% below category average
Google Cloud Storage
-
Ratings
Automatic software patching8.00 Ratings00 Ratings
Database scalability9.20 Ratings00 Ratings
Automated backups8.50 Ratings00 Ratings
Database security provisions8.60 Ratings00 Ratings
Monitoring and metrics8.00 Ratings00 Ratings
Automatic host deployment8.00 Ratings00 Ratings
Best Alternatives
Google BigQueryGoogle Cloud Storage
Small Businesses
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Amazon S3 Glacier
Amazon S3 Glacier
Score 9.1 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Azure Blob Storage
Azure Blob Storage
Score 9.7 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Azure Blob Storage
Azure Blob Storage
Score 9.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryGoogle Cloud Storage
Likelihood to Recommend
8.6
(0 ratings)
10.0
(0 ratings)
Likelihood to Renew
8.1
(0 ratings)
9.0
(0 ratings)
Usability
7.7
(0 ratings)
8.0
(0 ratings)
Performance
-
(0 ratings)
9.0
(0 ratings)
Support Rating
7.3
(0 ratings)
7.8
(0 ratings)
Implementation Rating
-
(0 ratings)
8.0
(0 ratings)
User Testimonials
Google BigQueryGoogle Cloud Storage
Likelihood to Recommend
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
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[Google Cloud Storage is] great for storing and playing large video files, and even sharing them securely with others, whether or not they are part of your organization. No need to download video files before watching, and can also be used to store any other kinds of files.
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Pros
  • 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.
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  • Really great, easy to use interface helps us manage files easily. Storage is fast and inexpensive, so we don't have to spin up storage instances locally
  • Great set of command-line tools to manage data and storage options via scripts and apps, as well as an SDK means we can build GCS into our orchestration and operations tools
  • Robust integration with other Google cloud tools means that we don't have to think too hard about using GCS for a variety of storage tasks as we interact with other Google services.
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Cons
  • 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.
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  • Sometimes we would find advice for how to do something that wasn't documented in the API, although this was very early on.
  • When we first started using it, Google Cloud Storage was changing a lot, and some of these changes required us to adapt our code to fit them.
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Likelihood to Renew
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.
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after all of the investment made in the tool and considering how many teams use it I think we would not be likely to move away from this tool. A lot of our information including historical is already here and we are happy with the capabilities of the tool currently
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Usability
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
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Overall I say this product is awesome and very easy to use, and would highly recommend it to other business professionals. I feel that my documents and work product are safe and secure, and that I will find them easily when needed.
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Reliability and Availability
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.
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No answers on this topic
Performance
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.
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For performance i give Google Cloud Storage 10 of 10 on performance because even though there are other softwares that do exactly the same thing as Google Drive, it still works exceptionally well. It is very fast, and and far as integration, the only software I have used with it that integrated was Google Docs, and of course it integrates perfectly.
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Support Rating
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.
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We have never used official support from Google for our Google Cloud Storage, but there is plenty of documentation in place already. With a small amount of work, anybody should be able to get started. Once needs get more complicated, there is still documentation from Google, but also plenty of community support for common use cases around the internet.
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Implementation Rating
No answers on this topic
overall I was not directly involved but hears the teams were satisfied with the implementation. the teams that used the tool did not encounter major issues, it was as expected with minor issues and bugs that were resolved later. The more significant learning curve was actually starting to use the tool
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Alternatives Considered
Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.
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Google Cloud Storage compares very similar to Dropbox. The difference of using Googe Cloud Storage is that it is part of a big bundle of products that you are probably already using. If you are a big Google user then it would make sense to get Google Cloud Storage. This way you can have all of the tools you need under one roof. I selected Google Cloud Storage because I was already using Google's other products and I was very impressed by those products so it was an easy sale.
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Scalability
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.
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No answers on this topic
Return on Investment
  • In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
  • Scalability is up-to-date and really helpful in multiple places.
  • Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
  • Also, it gives us more insights from our data, helping us make smarter decisions for our business.
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  • It allows the use of large Big Query data sets, this enables better data analysis.
  • It helps the business maintain a central storage solution for data.
  • Secure and easy to learn and use, it allows for fast adoption which improves productivity.
  • It does not enable collaboration and has limits on flexibility.
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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.