Google BigQuery vs. ThoughtSpot

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.7 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
ThoughtSpot
Score 8.4 out of 10
N/A
ThoughtSpot is an Agentic Analytics Platform for enterprises where users ask data questions using natural language and get answers with AI. Code-first for data teams and code-free for business users, ThoughtSpot can handle large, complex cloud data at scale.
$50
per month (billed annually) per user (25-1000 users)
Pricing
Google BigQueryThoughtSpot
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Thoughtspot Analytics - Pro
$50
per month (billed annually) per user (25-1000 users)
Thoughtspot Analytics - Enterprise
Custom
Offerings
Pricing Offerings
Google BigQueryThoughtSpot
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Community Pulse
Google BigQueryThoughtSpot
Considered Both Products
Google BigQuery
Chose Google BigQuery
Fully serverless. We don’t manage clusters or warehouses. Requires us to manage virtual warehouses. BigQuery is cheaper for exploratory heavy queries; Snowflake is more predictable for sustained workloads. BigQuery is unbeatable if you’re deep in Google’s ecosystem; Snowflake …
Chose Google BigQuery
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 …
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
The architecture of ETL was influenced by Data processing component which is Dataproc and there was a need for easy Query console with Access control capabilities with lesser overhead in managing the permission. This made the decision to move with Google BigQuery compare to …
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
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.
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
Main reason is how it integrates directly with the google ecosystem which really facilitates the automatization proceses for the whole company. This ensures that sales and all the other departments have the correct information on a daily bases with a ease of use with day to day …
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 …
ThoughtSpot
Chose ThoughtSpot
Great ease of use for business users and self-service capabilites
Chose ThoughtSpot
It stacks up well against these modern BI tools. These are the most popular tools and its ability to perform well with live connections gives it an advantage. However, having more control over formatting/customization would allow it to go further. Plus, developing a stronger …
Chose ThoughtSpot
I think PBI was terribly slow and clunky. UI was outdated. Solution was expensive for what we needed. ThoughtSpot was worlds different (in a better way).
Chose ThoughtSpot
They’re for different use cases. When I want a dynamic way to quickly obtain the data I need, I’d go with ThoughtSpot.
Chose ThoughtSpot
It is more flexible and PowerBI, easier work online, and share information with many users. Also the dashboards and beautiful, and the user experience is better. Performance used to be faster.

We use Looker for different use cases but, when it comes to reporting and sharing …
Chose ThoughtSpot
ThoughtSpot's user friendly interface, faster and accurate results, low-code support for building insights and vast availability of native charts, AI powered dashboards helps in faster decision making and helps in driving better business results. The mobile APP that ThoughtSpot …
Chose ThoughtSpot
It is more cost effective when compared to Tableau and more easy to use. ThoughtSpot integrates well with modern cloud data platforms like Snowflake, Google, and Bigquery, making it easy to analyze large datasets in real time. Sync insights into cloud tools. Users can sync …
Chose ThoughtSpot
ThoughtSpot provided a twist on the typical Analytics / report builder interface. The natural language query building is just so much easier to use.
Chose ThoughtSpot
ThoughtSpot is easy to build charts and it is probably the easiest tool to build things quickly. The quality of the charts is middle if not lower pack compared to others. The dbt integration is wildly oversold and does not add any value.
Chose ThoughtSpot
ThoughtSpot can handle large datasets A LOT better.
Chose ThoughtSpot
ThoughtSpot was much easier from an end-user perspective. Looker and Tableau allowed for more customization and detailed dashboards.
Chose ThoughtSpot
Thoughtspot is a fairly new tool when compared to Tableau. I'll just list the benefits of each one of threse below -
Tableau -
1. Much more customizable the Thoughtspot
Chose ThoughtSpot
Have not used as much, tried ThoughtSpot out for the first time for evaluation and testing purposes to see if this type of software would be helpful.
Chose ThoughtSpot
we were looking for something which provides self service capabilities and something which very eay to use from users perspective.
Chose ThoughtSpot
I liked thought spot because of the ad-hoc capabilities and their search functionality.
Chose ThoughtSpot
ThoughtSpot is more powerful of a tool than something like QlikView or QlikSense with the correct training.
Chose ThoughtSpot
ThoughtSpot is my top choice from the above two as the UI is the most modern and user-friendly as well.
Chose ThoughtSpot
ThoughtSpot is the leader in embedded analytics and is much easier to work with massive volumes of data. None of the other tools listed had both of those functionalities, which were most important to us. There are other features that they have such as easier drill down, …
Chose ThoughtSpot
much more extensible and much easier to embed
Chose ThoughtSpot
Compare to Kibana, which was we used previously, ThoughtSpot is definitely better in terms of UI, visualizations, usability, and the SpotIQ/ML components. The only disadvantage for ThoughtSpot is the lack of drill-down function/click-on filters.
Chose ThoughtSpot
The main reason for selecting Thoughtspot was to share reporting with other external vendors and ease of doing business.
Chose ThoughtSpot
We have been using tableau as the enterprise reporting tool and felt sending data to external people had few restrictions hence we tried with ThoughtSpot.
Chose ThoughtSpot
ThoughtSpot can similarly generate tables, visuals, and dashboards in Tableau, but in a faster and easier way.
Chose ThoughtSpot
We liked the support we were getting in the sales process (this has continued to be fantastic), I personally knew several people in the company. In the pilot we ran, our devs enjoyed working with ThoughtSpot.
Features
Google BigQueryThoughtSpot
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
Ratings
1% above category average
ThoughtSpot
-
Ratings
Automatic software patching8.00 Ratings00 Ratings
Database scalability9.00 Ratings00 Ratings
Automated backups8.50 Ratings00 Ratings
Database security provisions8.80 Ratings00 Ratings
Monitoring and metrics8.60 Ratings00 Ratings
Automatic host deployment8.00 Ratings00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
7.3
Ratings
11% below category average
Pixel Perfect reports00 Ratings6.00 Ratings
Customizable dashboards00 Ratings8.10 Ratings
Report Formatting Templates00 Ratings7.70 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
7.5
Ratings
7% below category average
Drill-down analysis00 Ratings8.40 Ratings
Formatting capabilities00 Ratings7.10 Ratings
Integration with R or other statistical packages00 Ratings5.80 Ratings
Report sharing and collaboration00 Ratings8.70 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
8.2
Ratings
0% above category average
Publish to Web00 Ratings8.20 Ratings
Publish to PDF00 Ratings8.50 Ratings
Report Versioning00 Ratings7.90 Ratings
Report Delivery Scheduling00 Ratings8.30 Ratings
Delivery to Remote Servers00 Ratings8.00 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
7.3
Ratings
9% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings7.50 Ratings
Location Analytics / Geographic Visualization00 Ratings7.20 Ratings
Predictive Analytics00 Ratings7.50 Ratings
Pattern Recognition and Data Mining00 Ratings6.90 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
8.1
Ratings
5% below category average
Multi-User Support (named login)00 Ratings8.20 Ratings
Role-Based Security Model00 Ratings8.00 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings7.70 Ratings
Report-Level Access Control00 Ratings7.90 Ratings
Single Sign-On (SSO)00 Ratings8.70 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
7.3
Ratings
6% below category average
Responsive Design for Web Access00 Ratings7.00 Ratings
Mobile Application00 Ratings6.80 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings6.80 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Google BigQuery
-
Ratings
ThoughtSpot
7.2
Ratings
7% below category average
REST API00 Ratings7.20 Ratings
Javascript API00 Ratings6.70 Ratings
iFrames00 Ratings8.10 Ratings
Java API00 Ratings7.00 Ratings
Themeable User Interface (UI)00 Ratings7.20 Ratings
Customizable Platform (Open Source)00 Ratings7.10 Ratings
Best Alternatives
Google BigQueryThoughtSpot
Small Businesses
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Yellowfin
Yellowfin
Score 8.6 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Reveal
Reveal
Score 10.0 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Kyvos Semantic Layer
Kyvos Semantic Layer
Score 9.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryThoughtSpot
Likelihood to Recommend
9.0
(0 ratings)
8.5
(0 ratings)
Likelihood to Renew
8.1
(0 ratings)
10.0
(0 ratings)
Usability
6.7
(0 ratings)
8.2
(0 ratings)
Availability
7.3
(0 ratings)
9.0
(0 ratings)
Performance
6.4
(0 ratings)
8.0
(0 ratings)
Support Rating
5.0
(0 ratings)
8.0
(0 ratings)
In-Person Training
-
(0 ratings)
5.0
(0 ratings)
Online Training
-
(0 ratings)
4.0
(0 ratings)
Implementation Rating
-
(0 ratings)
7.0
(0 ratings)
Configurability
6.4
(0 ratings)
8.0
(0 ratings)
Ease of integration
7.3
(0 ratings)
9.0
(0 ratings)
Product Scalability
7.3
(0 ratings)
8.0
(0 ratings)
Vendor post-sale
-
(0 ratings)
8.0
(0 ratings)
Vendor pre-sale
-
(0 ratings)
9.0
(0 ratings)
User Testimonials
Google BigQueryThoughtSpot
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.
Read full review
By using the power of cloud platforms like bigquery or snowflake, ThoughtSpot can deliver performance at scale ensuring that even the largest organization can quickly analyze and visualize data. The ability to seamlessly share live dashboards fosters collaborations, ensuring everyone in the organization is working from the same data source and aligned in decision making.
Read full review
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.
Read full review
  • Beautiful visualizations. The visuals are distinct, clean, and easy to discern from one another.
  • Intelligent querying functionality. When looking to manipulate the data, the search function makes it easy to manipulate the features in the data, along with aggregating them in the way you'd like.
  • Embedding! It has been a smooth process thus far for our product & technical teams to work with ThoughtSpot and bring it into our product.
Read full review
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.
Read full review
  • It would be great if ThoughtSpot can add the feature to filter by clicking on visualizations. i.e if I click on a particular data point in the chart if the full dashboard can filter just for that particular data point.
  • Color coding the heatmap with different colors like green to orange to red.
Read full review
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.
Read full review
We have had success with the initial use cases and there are more use cases that can receive return on investment. I don't give it a 10 because other products like Tableau are building functionality that may start to compete in the coming years.
Read full review
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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The tool is easy to use if you know what you are doing and looking for. I know as they work towards improvements and simple language it will become even easier, but as of now they are doing a great job but there is room for improvement.
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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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it's available unless there is a server or system update etc. sometimes the timing of this is bad (for example during a month end close)
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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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It does what it is supposed to. Would be nice to have a bit more insights
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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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I give it this meeting because the team is not only help able to help us in the current solutions but also amazing and taking feedback and feeding it back to their development team which includes more products and features into ThoughtSpot
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In-Person Training
No answers on this topic
inhouse in-person training. Took a bit to long to get the basics.
Read full review
Online Training
No answers on this topic
poor instructions
Read full review
Implementation Rating
No answers on this topic
Understand use case and model and design accordingly
Read full review
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.
Read full review
It is more flexible and PowerBI, easier work online, and share information with many users. Also the dashboards and beautiful, and the user experience is better. Performance used to be faster. We use Looker for different use cases but, when it comes to reporting and sharing information, ThoughSpot is much better.
Read full review
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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Because it is very reliable, inside the situation, we need strong internet connection to access a lot of data but easily never had any downtime except during the upgrades
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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.
Read full review
  • Time to market ROI is massive vs hiring the full-time dedicated team to build and maintain a frontend multi-tenant SaaS data viz product.
  • It will be interesting to see over time how the advanced features play out in terms of usability and end value, such as Natural Search, which we are very excited about, and the machine learning tools.
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.

ThoughtSpot Screenshots

Screenshot of the ThoughtSpot home screenScreenshot of SpotIQ, which offers AI-driven insightsScreenshot of the Spotter AI agent that surfaces insights through natural language queriesScreenshot of AI Assist producing SQL in real timeScreenshot of SpotIQ, which offers AI-driven insightsScreenshot of the integration with dbt models and metrics