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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Treasure Data
Score 9.0 out of 10
Mid-Size Companies (51-1,000 employees)
Treasure Data is an enterprise customer data platform (CDP) that reclaims customer-centricity in the age of the digital customer. It does this by connecting all data and uniting teams and systems into one customer data platform to power purposeful engagements.
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Pricing
Snowflake
Treasure Data
Editions & Modules
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Offerings
Pricing Offerings
Snowflake
Treasure Data
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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More Pricing Information
Community Pulse
Snowflake
Treasure Data
Considered Both Products
Snowflake
Verified User
Anonymous
Chose Snowflake
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 …
Both Tealium and Evergage are mostly focused on online sources. They don't have as easy or robust data model capability to ingest CRM, e-commerce, or offline data. Bluevenn has good identity resolution like TD, but the Unify data processes/model are not exposed to the customer …
We selected Treasure Data because we felt they were the best fit for a publisher as diversified as Penske. Because we have so many lines of business and integrated systems, we needed a product that had an extensible framework and was not tied to concrete workflows.
Treasure Data is a leader in the CDP space with a very easy-to-use platform for engineers, the ability to customize, customer success and investment in our business, and the ability to provide value and return on investment.
Treasure Data seems to be more flexible and scalable compared to Lytics at that time (early 2019). Our possibilities to adapt the platform to suit better our complex business environment (global, multi country, multi brand) were also a positive point. And finally, their …
We chose Treasure Data for the supreme customer service and lack of hidden costs. We don't need to manage any infrastructure or scale anything to meet customer demand. Treasure Data handles everything and makes it easy for us to integrate and focus on the tasks at hand. There …
Treasure is a more centralized and focused platform than Salesforce. Salesforce has many solutions that they seem to piece together in order to create your desired stack. Treasure integrates very well with other 3rd party technologies and you also get a more personalized …
Customer Data Platform powered by TD provides details on customer journey & individual mapping as well. GA provided aggregate level data and not the customer details. We can focus in unknown customers as well using cookie data using CDP. Custom attribution model can be created …
This is the first big data environment that I have used for marketing purposes. But Treasure Data's infrastructure could allow any kind of business to be managed in this platform--that's why this is very interesting. It's not only a consumer data platform, it's a big data …
More flexible in terms of capability, better DEVOPS (though still not ideal), large and better out of the box features/connectors, better UI, cost, integrated audience studio and active data layer (real time access data)
I did/do think that Adobe Analytics is a good tool that helps bring in all data. I really think the big point with the tool is for metrics and de-duping across media campaigns. Treasure Data is definitely much more than that. You do get to see how all of your campaigns 'play' …
Unfortunately, I was not apart of the decision to onboard Treasure Data. I was very new to this space when I inherited this tool and initiative on my team.
Based on my experience, the most striking difference between the two platforms are the way their data models are organized. Agilone (now part of Acquia) has a very hard/strict requirement for integration with the source systems as we need to conform/adhere to their …
There is a limited amount of human resource in the market who has knowledge in CDP. Treasure Data is simple and easy to navigate so that a newbie might find it easy to grasp its working concepts and initiate performing on the same. Whereas Tealium is more suited for a person …
Treasure Data was also chosen before I arrived at the organization. Also, I'm not person who's in charge or writing the queries which means that I let someone know what I need to use the software for and they let me know if Treasure Data is best suited. However, that being …
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.
Any time you need to process and store very large volumes of data at scale, Treasure Data will aways be at the forefront of my mind. Especially if the data being handled is constantly changing or evolving, rigid schemas just wont do. Treasure Data has the ability to adapt as your product needs change over time. Having the storage and processing flexibility is a huge win.
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
CDP provides a unified view of data from all touchpoints in the customer journey until a single customer uses the service. This feature is very helpful in making service decisions and direction.
It provides a variety of extensions to bring your data together in one place and helps you do this easily.
Kits provided by Treasure Box provide basic but helpful methods for further development of services.
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.
Pricing is a bit of a black box. We are currently priced on split hour usage and some spikes come out of nowhere and leave us seeking answers (and sometimes finding unsatisfactory ones).
Some jobs will fail, causing workflows to be interrupted due to a product change or a one-time product related issue. We usually contact Support in these cases, and while they are incredibly responsive and helpful, it would be great to have more proactive communication.
Treasure's UI leaves us wanting more in terms of organization and controls, especially as we scale and grow the number of data sources, queries, and workflows.
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
Because treasure data is a great platform with a great support team behind, it's a scalable solution that deals well with huge amounts of data every day and has a huge catalog of integrations that can be easily use to download data from several platforms, like aws s3, redshift, google bigquery.
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.
If you are a data person, you will likely understand the product and how to use it well. We did find that some of our queries run into memory issues though. If you are a marketer and want to build easy audience segments, I am not sure how easy it will be for you. We are still working through this.
As treasure data has a 24 hours support, every time we has big issues that impacts the zones, we do have immediatly support from the treasure data team, so I would say that we do not have any issues with availability
Since treasure data has started having a huge amount of data, sometimes we do have problems with the workflows logs because we generate a lot of then. But with integrations I have not to complain, its really easy to integrate with other platforms.
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.
The technical team has a good hold on the nuances of the data related to our organization. I have found the online technical support on their site quite responsive including the L1 support. In cases where the L1 team isn't able to resolve, I have found they are prompt in getting the product team's input to get a quick resolution.
I wasnt here at the training in the start, but I had a few training with treasure data for a few functionalities, and they provided me god explanations and great documentations, eve if the project were in beta.
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.
Both Tealium and Evergage are mostly focused on online sources. They don't have as easy or robust data model capability to ingest CRM, e-commerce, or offline data. Bluevenn has good identity resolution like TD, but the Unify data processes/model are not exposed to the customer to modify or develop data load workflows.
When there are Treasure Data updates, there might be old functions that are deprecated or existing functions which no longer work as before --> this may have impact on existing workflows/queries
As many developers are working on the same environment, the jobs are queued because there is a limited amount of computation cores available --> if we want to increase it, our client needs to pay for more cores
As data are increasing, some workflows are too expensive and need to be rethought / made more efficient --> this means re-designing existing workflows and also requires constant support from Treasure Data which analyzes the queries and identifies points of improvement that allows client to pay less