Datatrics vs. Treasure Data

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.

Incentivized

• 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.

Incentivized

• 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.

Incentivized

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.

Incentivized

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.

Incentivized

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

Incentivized

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.

Incentivized

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.

Incentivized

I was not here when treasure data was implemented to our company.

Incentivized

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.

Incentivized

Implementation was quick and our developers had very few issues with the SDK.

Incentivized

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.

Incentivized

In abi we do have a lot of data coming every day, so treasure data always give us god solutions and options that would fix the problem.

Incentivized

• 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

Incentivized