Databricks Lakehouse Platform vs. Treasure Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Databricks Lakehouse Platform
Score 8.1 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Treasure Data
Score 8.3 out of 10
Mid-Size Companies (51-1,000 employees)
Treasure Data is an enterprise customer data platform (CDP) that powers the entire business to reclaim 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 that drive value and protect privacy for every customer, every time. According to the vendor, Treasure Data serves customers globally across a broad base of industries that…N/A
Pricing
Databricks Lakehouse PlatformTreasure Data
Editions & Modules
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
No answers on this topic
Offerings
Pricing Offerings
Databricks Lakehouse PlatformTreasure Data
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Community Pulse
Databricks Lakehouse PlatformTreasure Data
Top Pros
Top Cons
Best Alternatives
Databricks Lakehouse PlatformTreasure Data
Small Businesses

No answers on this topic

Klaviyo
Klaviyo
Score 8.9 out of 10
Medium-sized Companies
Snowflake
Snowflake
Score 9.0 out of 10
SALESmanago
SALESmanago
Score 8.9 out of 10
Enterprises
Snowflake
Snowflake
Score 9.0 out of 10
Tealium Customer Data Hub
Tealium Customer Data Hub
Score 8.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Databricks Lakehouse PlatformTreasure Data
Likelihood to Recommend
8.4
(17 ratings)
9.0
(90 ratings)
Likelihood to Renew
-
(0 ratings)
9.1
(5 ratings)
Usability
9.4
(3 ratings)
8.0
(4 ratings)
Availability
-
(0 ratings)
9.1
(1 ratings)
Performance
-
(0 ratings)
8.2
(1 ratings)
Support Rating
8.6
(2 ratings)
8.2
(7 ratings)
In-Person Training
-
(0 ratings)
6.4
(1 ratings)
Online Training
-
(0 ratings)
7.3
(1 ratings)
Implementation Rating
-
(0 ratings)
6.4
(2 ratings)
Configurability
-
(0 ratings)
7.3
(1 ratings)
Contract Terms and Pricing Model
8.0
(1 ratings)
-
(0 ratings)
Ease of integration
-
(0 ratings)
9.1
(1 ratings)
Product Scalability
-
(0 ratings)
9.1
(1 ratings)
Professional Services
10.0
(1 ratings)
-
(0 ratings)
Vendor post-sale
-
(0 ratings)
7.3
(2 ratings)
Vendor pre-sale
-
(0 ratings)
7.4
(2 ratings)
User Testimonials
Databricks Lakehouse PlatformTreasure Data
Likelihood to Recommend
Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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Treasure Data
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.
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Pros
Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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Treasure Data
  • 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.
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Cons
Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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Treasure Data
  • 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
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Likelihood to Renew
Databricks
No answers on this topic
Treasure Data
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.
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Usability
Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
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Treasure Data
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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Reliability and Availability
Databricks
No answers on this topic
Treasure Data
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
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Performance
Databricks
No answers on this topic
Treasure Data
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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Support Rating
Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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Treasure Data
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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In-Person Training
Databricks
No answers on this topic
Treasure Data
I was not here when treasure data was implemented to our company.
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Online Training
Databricks
No answers on this topic
Treasure Data
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 Rating
Databricks
No answers on this topic
Treasure Data
Implementation was quick and our developers had very few issues with the SDK.
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Alternatives Considered
Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
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Treasure Data
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.
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Scalability
Databricks
No answers on this topic
Treasure Data
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.
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Return on Investment
Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
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Treasure Data
  • 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.
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ScreenShots

Treasure Data Screenshots

Screenshot of Out of the box integrations across advertising, CRM, databases, eCommerce, machine learning and more.Screenshot of Powerful query toolsScreenshot of Fast and easy audience builder