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
Snowflake
Score 8.7 out of 10
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The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
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The Trade Desk
Score 9.0 out of 10
N/A
The Trade Desk is the eponymous flagship product from the company headquartered in Ventura California, a demand-side / data management hybrid platform (DSP / DMP).
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Pricing
Google BigQuery
Snowflake
The Trade Desk
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
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No answers on this topic
Offerings
Pricing Offerings
Google BigQuery
Snowflake
The Trade Desk
Free Trial
Yes
Yes
No
Free/Freemium Version
Yes
No
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google BigQuery
Snowflake
The Trade Desk
Considered Multiple Products
Google BigQuery
Verified User
Anonymous
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 …
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 …
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 …
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
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 …
is much better as it’s easily accessible provides velvet documentation and fulfils all our needs as well as easily integrated into clients, environment
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 …
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.
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).
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.
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 …
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.
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 …
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 …
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 …
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 …
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.
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 …
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 …
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 …
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 …
We use these tools for applications they are better suited for vs a Snowflake. For e.g. MS Fabric has powerful agentic AI capabilities; Redshift is our go to choice for the TMT vertical within the organization and Databricks is the default choice for AI/ML applications.
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can …
These are comparable products that can make sense depending on the specific needs of your organization. All are certainly serviceable and have varying pros and cons. Snowflake seems to provide the greatest degree of flexibility and easy scalability as new data gets brought into …
We needed scalability and a new way of organizing our data; Snowflake allowed us to have a clearer view of our data warehouses and schemas. Snowflake is also way superior in terms of speed and quick insights from the raw data you query, which is very valuable to us.
Snowflake has an attractive pricing model with auto-suspend and auto-resume and pay per use. AWS Redshift requires higher administrative efforts to maintain and scale the platform whereas with Snowflake those admin tasks are not needed or automatically taken care of.
We had a MS SQL server with over 2 TB of ram & 51 processors that we were using, that could no longer handle our workload. Snowflake can handle 3 times that workload with ease and efficiency.
Snowflake is much faster and easier to write queries and pull data. But the visualization part of Snowflake is not as good as them. Also, Snowflake only supports SQL queries but not python or other languages. So basically Snowflake is the expert in its field but not suitable …
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
While Snowflake is more open to cloud eco system, SAP integrated well with SAP eco system products like SAP ECC or SAP S/4. So for people who have invested heavily in SAP eco system including SAP ECC or S/4, it makes sense to go with SAP DWC which is also evolving very rapidly. …
In my opinion, the other tools have similar and some different features; however, when I ran proof of technologies between Synapse and Snowflake. Snowflake did things better or just had functionality that the other tools did not. One that stuck out at the time was scale up …
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Azure and Snowflake compared very similarly, but Snowflake provided more options to integrate and connect with tools/companies that were not partners. It seemed to be a more flexible environment. The barrier for entry on Oracle and Google we just too complicated. In particular, …
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system …
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
Our initial data warehousing solution was Treasure Data. We had issues with the costly pricing model, which would be exhorbitant if we want to hold our data in memory and query using Presto. As a result, some heavy lifting was done in Hive (managed by Treasure Data); …
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Redshift compute and storage can be scaled up/down together (though they added some features recently, they don't quite add up). I haven't tried Avalanche or Firebolt but would love to in the near future, due to their pedigree or revolutionary billing methods.
- Cost was the main aspect on the decision. - Performance was in par or better compared to other tools in the market. - Snowflake in my opinion stacks better than other tools I have used in the past.
Accommodates future data types such as JSON and XML. Scalability is another advantage. Pay per use is beneficial for organizations like yours. Direct connectors with AWS help us to go with it. No limit on user creation and clone data not eating up extra disk space are a few …
Since we switch from amazon redshift to Snowflake, we found Snowflake is much better than redshift in many ways, including the data integrate and data pull. However, comparing directly pull data from amazon s3, Snowflake is quite slow in terms of data pull speed and the more …
Compared to Amazon Redshift, Snowflake is slightly easier and faster to achieve ROI but based on the user's perspective, the two tools have very little difference since both are leveraging SQL to pull data from AWS S3. Snowflake is also working with Microsoft Azure but it is …
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
compared to these three, stack adapt is a bit easier to get going plus it's less expensive. I still love Google ads and meta ads for the ability to own the moment in terms of dashboard. Feels like you have more "control" with google ads and meta ads, even if that isn't the case.
We compared The Trade Desk against a number of competing programmatic media buying platforms including Google's DV360, Centro's Basis platform, Adobe Advertising Cloud, and the Simpli.fi platform. In the end, we felt that The Trade Desk provides the most tools, options, …
The Trade Desk is a much better product than AppNexus. Even though both products showed promise awhile back, AppNexus stopped innovating and The Trade Desk became specialists on data, cross site tracking and CTV inventory thus having a better and more unique product that …
The Trade Desk provides the best overall value of all features in one. We didn't evaluate other competitors because The Trade Desk came highly recommended to us by our Board of Directors. Perhaps for other companies' evaluations, we would recommend having example campaigns and …
The Trade Desk allows you to manage multiple display platforms from one interface. Google Ads only allows you to advertise on their platform and their network. This limits your ability to find new niche opportunities with Display Advertising. Although Google Ads claims to be on …
The Trade Desk is more universal and widely known than their competition. They will be able to provide you with a lot of inventory, but the competition is more oriented towards specific objectives. Vungle specializes in video, Mopub on in app traffic and AppLovin on high impact …
The Trade Desk is not as focused on connected TV as Appnexus. Also, they are more open as Appnexus is mostly focused on building a walled garden, while The Trade Desk is focusing on making the internet more open. However, they lack the exclusive publishers that Appnexus have. …
TradeDesk is more extensive like DBM compared to DX. Offers an easier building process with templates and being able to duplicate and share libraries at the partner level.
I find TradeDesk clears at higher CPMs than the other platforms. Also tends to perform well for in-view …
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.
If you need a quick query, snowflake is the way to go. It's super simple and scalable; we were struggling before with Azure, and with Snowflake, everything runs smoothly, and we have more control over our schemas and warehouses. Snowflake, in my opinion, is the next step when you want to scale your business and manage data. If your company is still small, there may be cheaper options.
We are a large company, but The Trade Desk seems like it would also work well for small companies. In our limited deployment, we focused on the USA market and Digital Marketing, but we see this platform working for global markets, multi-language, and across all paid search engines. They provide great user engagement and dashboard reporting to help us manage each campaign in real-time. The Trade Desk seems to do all of these things well and would work for most companies. I have yet to see a campaign and/or executive initiative that The Trade Desk can't handle. We have had a favorable experience and believe it has met every objective we have.
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.
Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
Snowflake manages concurrency better in our trials than other premium competitors
Snowflake has little to no setup and ramp up time
Snowflake offers online training for various employee types
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.
Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
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.
SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
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
The interface is similar to other SQL query systems I've used and is fairly easy to use. My only complaint is the syntax issues. Another thing is that the error messages are not always the easiest thing to understand, especially when you incorporate temp tables. Some of that is to be expected with any new database.
easy to understand dashboards and the self started ability to get going on your own terms. It's great to target niche groups across the world while still being able to report at a high level to C-suite. Wish it drove more conversions, but display can be a struggle there. Overall, would use it again.
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.
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.
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.
We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
We noted no issues with our limited interaction with support. The software operated just as expected and we found that we were able to navigate needs on our own. We understand that The Trade Desk offers excellent support, so we believe it will be a good resource to us in the future.
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.
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can buy according to your needs. And the integration options with various tools like Sigma are add-ons.
We compared The Trade Desk against a number of competing programmatic media buying platforms including Google's DV360, Centro's Basis platform, Adobe Advertising Cloud, and the Simpli.fi platform. In the end, we felt that The Trade Desk provides the most tools, options, advanced technology, and access to third-party solutions than anything other DSP/DMP on the market. TTD has leaned into forming a long-term relationship with our agency, and they continue to provide great strategic service.
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.
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.
Positively impacting ability to delivery in-view KPI campaigns across ad types (display, high impact, preroll).
Slightly higher pricing structure requires a bit more conservative bidding in this platform compared to others.
The customer service has been top notch. Adding to the confidence in building a lasting partnership. Access to product betas and offering product feedback are of interest to our organization.