Google BigQuery vs. Treasure Data

Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.

Incentivized

Treasure Data is well suited to integrating multiple data sources, including online and digital sources. It is also well suited to trigger audience activations to known customers based on their online activity, integrating 3rd party data, and activating target audiences to ad platforms.

Incentivized

• 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.

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

• 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.

Incentivized

• Documentation is not always fully update --> better off reaching to support for some topics that are not covered
• Small bugs on the graphical user interface
• If 2 people are editing on the same project simultaneously, the latter that saves the workflow overwrites the changes of the former one

Incentivized

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.

Incentivized

I do think that we definitely will be renewing. We are putting major resources, time, and effort into Treasure Data becoming an extension of our organization, in many ways. We are working toward complete synergies with this product and leadership is very excited about the direction we are heading to be completely customer-centric.

Incentivized

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

It's a easy platform to use and give the user detailed logs about what is going on in the workflows, so someone that do not have a lot of experience can start to work with it. And also the master segment usability is awesome, as we can filter a lot of data the way we want.

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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.

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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.

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.

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

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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.

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

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I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.

Incentivized

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 may be cheaper options but we do not regret our decision to go with Treasure Data one bit.

Incentivized

None so far. Very satisfied with the transparency on contract terms and pricing model.

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

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.

• Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free.
• Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized.
• The visuals of ML models is very helpful to fine tune training, model building and prediction, etc.

Incentivized

• We have built and supported our source of truth data tables using Treasure. This forms the foundation of our decision making.
• Most of our Tableau data sources are created using a Treasure Data export which is executed by workflows on a daily basis which allows us to have visibility into day to day performance and communicate them to a wide variety of roles.
• We load custom data into our Salesforce instance which allows us to trigger certain workflows and build accountability - i.e. a "Sale" will only count once a certain product driven event occurs which comes from data we pipe into Treasure and then into Salesforce.

Incentivized