Google BigQuery vs. Google Cloud Storage

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.8 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)
Google Cloud Storage
Score 8.9 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
Compared to PostgreSQL and MySQL, Google BigQuery is faster and more scalable for large datasets. It’s serverless, so there’s no need to manage infrastructure. We chose Google BigQuery for its ease of use built-in analytics features
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
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
BigQuery is better at storing and handling large amounts of data than Knime. Knime is locally run and does not have the ability to handle massive databases like BigQuery and importing from multiple sources for multiple teams would be impossible, that is not really the function …
Chose Google BigQuery
We liked BQ because the cost of it is only dependent on the amount of data you store (and there are tiers of data access) and how much you search. For us, it is significantly less expensive to run BQ than an equivalent hosted RDBMS. Because most of our data pipelines are …
Chose Google BigQuery
BigQuery by far the best solution in all angles compared to other ones: Especially scalability, ease of use, performance and there is no need to manage any cluster of servers. Also it's ABSOLUTELY pay as you go! No one in market currently provide such service that can compete …
Chose Google BigQuery
Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little …
Google Cloud Storage
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
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 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
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 …
Features
Google BigQueryGoogle Cloud Storage
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
80 Ratings
0% above category average
Google Cloud Storage
-
Ratings
Automatic software patching8.017 Ratings00 Ratings
Database scalability9.179 Ratings00 Ratings
Automated backups8.524 Ratings00 Ratings
Database security provisions8.773 Ratings00 Ratings
Monitoring and metrics8.375 Ratings00 Ratings
Automatic host deployment8.013 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 9.0 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Azure Blob Storage
Azure Blob Storage
Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryGoogle Cloud Storage
Likelihood to Recommend
8.8
(77 ratings)
10.0
(35 ratings)
Likelihood to Renew
8.1
(5 ratings)
9.0
(2 ratings)
Usability
7.1
(6 ratings)
8.0
(12 ratings)
Availability
7.3
(1 ratings)
-
(0 ratings)
Performance
6.4
(1 ratings)
9.0
(9 ratings)
Support Rating
5.6
(11 ratings)
7.8
(13 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
Configurability
6.4
(1 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
-
(0 ratings)
Ease of integration
7.3
(1 ratings)
-
(0 ratings)
Product Scalability
7.3
(1 ratings)
-
(0 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryGoogle Cloud Storage
Likelihood to Recommend
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).
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Google
[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
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.
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Google
  • 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
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.
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Google
  • Currently can't delete folders which means there are cluttered folders on my cloud.
  • Easier upload options would be nice. The ability to upload and store other file types with an easier interface.
  • Managing files and storage could be improved a little for easier access and editing.
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Likelihood to Renew
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.
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Google
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
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.
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Google
Very easy to use. I love having my data backed up. I love that Google Cloud Storage provides me with the peace of mind that I no longer need to worry about my data being lost. I can now sleep better at night. Google Cloud Storage is very easy to use. Overall, you save time and have less stress by using Google Cloud Storage.
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Reliability and Availability
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.
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Google
No answers on this topic
Performance
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.
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Google
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
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.
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Google
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
Google
No answers on this topic
Google
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
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.
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Google
We prefer Google Cloud Storage over Amazon Web Services because of the tools and code integration offered by Google Cloud Storage. We found the Google Cloud Storage toolset to be highly usable and give us the fine-grained control we need for managing digital assets. Ultimately, we chose Google Cloud Storage because we found the API and suitability for code integration with our Java codebase to be impeccable and because we had excellent direct support from the Google Cloud Storage team
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Google
No answers on this topic
Scalability
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.
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Google
No answers on this topic
Professional Services
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.
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Google
No answers on this topic
Return on Investment
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.
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Google
  • It has assisted greatly with our ability to share documents/information cross functionally. Especially within our advertising team, we store a large amount of information to assist new hires and refresh current employees.
  • Something that could improve is employees' understanding of how to best utilize Google Cloud Storage. This could improve by implementing a potential training video or tutorial.
  • Overall, Google Storage has been great. I have not used a similar storage product that had the same enterprise level capabilities.
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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.