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Treasure Data has proven to be a versatile and powerful tool for a wide range of use cases. Customers have found that Treasure Data …
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What is Treasure Data?
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…
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What is Treasure Data?
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…
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- Integrations
- Competitors
- Tech Details
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What is Treasure Data?
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 utilize it for personalization at scale:
• 170+ Pre-built connectors to collect data and activate profiles
• 2M+ Events collected every second
• 10B+M Profiles activated every month
• 130T Records managed
• 1M Presto queries run daily
• 30T+ Rows of data processed daily
• 99.99% Uptime
Treasure Data Screenshots
Treasure Data Videos
Webinar: What is a Customer Data Platform?
Watch How a Customer Data Platform Achieves Over 800% Marketing ROI
Watch Webinar: Demystifying Customer Data Platforms, with Winterberry Group
Treasure Data Integrations
- Adobe Marketo Engage
- Twilio Segment
- Mixpanel
- Google Analytics
- AppsFlyer
- Adjust by AppLovin
- Amplitude Analytics
- Chartio (discontinued)
- Tableau Desktop
- Tableau Server
- Looker
- Domo
- Adobe Analytics
- Heap
- Apache Hadoop
- Amazon Redshift
- data.ai
- Microsoft Power BI
- Tableau Cloud
- Pentaho
- Metric Insights
- Microsoft Office 2016 (discontinued)
- Microsoft Azure
- Google Cloud Storage
- Google Drive
- Intercom
- Zendesk Suite
- Elasticsearch
- MongoDB
- Google BigQuery
- Microsoft SQL Server
- PostgreSQL
- Snowflake
- MySQL
- MariaDB Platform
- Mirantis Kubernetes Engine
- Heroku Platform
- Stripe Payments
- Mailchimp
- Salesforce Marketing Cloud Email Studio, on marketing cloud
- Salesforce Marketing Cloud
- HubSpot Marketing Hub
- NetSuite ERP
- Meta Business Suite
- Sizmek Ad Suite
- AdRoll
- LiveRamp
- Salesforce Marketing Cloud Intelligence, on Marketing Cloud
- Twitter Ads
- Google Tag Manager
- Branch
- Amazon Kinesis
- Unity
- iOS
- Android
- Unreal Engine
- Oracle Marketing
- TUNE
- Google Marketing Platform
- pandas
- Eyeota
- Oracle Responsys
- part of Oracle CX Marketing
- salesforce sales cloud
- kafka
- hive mall
- jupyter
- R
- mLab
- fluentd
- JDBC
- Luigi
- Python
- Rest API
- informatica
- ruby
- instagram Ads
- xbee
- MQTT
- RaspberryPi
- Serial Devices
- CSV
- FTP
- Webhooks
- mysql workbench
- razorSQL
- SQuirreL SQL
Treasure Data Competitors
Treasure Data Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
| Supported Countries | global |
| Supported Languages | English, Japanese |
Treasure Data Downloadables
Frequently Asked Questions
Reviewers rate Support Rating highest, with a score of 8.2.
The most common users of Treasure Data are from Mid-sized Companies (51-1,000 employees).
Treasure Data Customer Size Distribution
| Consumers | 0% |
|---|---|
| Small Businesses (1-50 employees) | 10% |
| Mid-Size Companies (51-500 employees) | 50% |
| Enterprises (more than 500 employees) | 40% |
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(149)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
- Cons
- Recommendations
Treasure Data has proven to be a versatile and powerful tool for a wide range of use cases. Customers have found that Treasure Data addresses the business problem of scattered data sources by providing a single platform for data collection and analysis. It serves as an ETL platform, analytical database, and data warehouse, supporting various business units across organizations. Users rely on Treasure Data as their primary storage and processing facility for large volumes of event data, eliminating the need to worry about storage and processing infrastructure.
Marketing teams utilize Treasure Data to enable a constant flow of data between databases and platforms like Salesforce, supporting email campaigns and providing refreshed data. The software helps clients understand customer behaviors, digitize the consumer base, and gain insights for business decisions, leading to increased revenue. It also serves as a central source of truth for customer data, solving the problem of fragmented information across digital and offline properties. Furthermore, Treasure Data is used for web tracking and analytics, supporting marketing teams in tracking website usage and campaign performance, ultimately improving the user experience. Overall, Treasure Data has helped companies store and secure their data while generating valuable insights to support data-driven decision-making.
Flexibility in Data Management: Many users have highly appreciated the flexibility of Treasure Data in ingesting different data sets and creating a data lake. This feature has been praised by numerous reviewers, allowing them to easily manage their data and streamline the data management process.
Extensive Native Integrations: The extensive native integrations provided by Treasure Data have received high praise from users. They have mentioned that these integrations cover the majority of systems they use, saving them time and effort in connecting different systems. Several reviewers have expressed their satisfaction with this feature.
Reliability and Uptime: Users consistently mention that Treasure Data is highly reliable, boasting a 99.99% uptime rate. This level of reliability has given them confidence in the platform and allowed them to trust that their data and jobs will always be accessible. Numerous reviewers have highlighted this aspect as one of the strengths of Treasure Data.
Cons:
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Frequent Request Failed Errors: Many users have experienced frequent "Request failed with status code 429" errors while using Treasure Data. These errors can be frustrating and disrupt the workflow, leading to delays in completing tasks.
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Lack of Error Resolution Guidance: Some reviewers have mentioned that they faced difficulties in resolving the "Request failed with status code 429" errors. They felt that there was a lack of clear guidance or documentation on how to troubleshoot and fix these issues, which made it challenging for them to resolve the errors independently.
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Impact on Productivity: The recurring "Request failed with status code 429" errors negatively impact productivity for some customers. Users expressed frustration at having to constantly deal with these errors, as it interrupts their work and requires them to spend additional time trying to resolve the issue or contacting support for assistance.
Users commonly recommend several actions for optimal use of Treasure Data. First, they suggest thoroughly testing the software before making a purchase decision. This allows customers to assess if the platform meets their specific needs and requirements. Second, users recommend having a skilled technical team in place to make the most of Treasure Data's capabilities. This ensures proper configuration and utilization of the platform's features. Lastly, users suggest engaging key personnel within the organization to maximize the benefits of using Treasure Data. This collaboration helps leverage the platform's flexibility and robustness for effective data management and analysis.
Additionally, users highly recommend Treasure Data for its comprehensive toolset, data connectors, analytical database, and excellent support. They emphasize that it is well-suited for managing massive amounts of data and allows analysts to work independently without relying heavily on engineers. With its powerful big data and business intelligence capabilities, Treasure Data can serve as an efficient ETL pipeline and facilitate seamless exporting of results to a warehousing database for scheduled queries. It is advised to ensure a comprehensive list of data sources and validate aggregation levels for accurate analysis. Reading through documentation and relying on customer success representatives are also recommended for efficient support.
Overall, users find Treasure Data to be a flexible and robust Customer Data Platform (CDP) with a powerful set of features and excellent support, making it a valuable asset for businesses dealing with large datasets.
Attribute Ratings
Reviews
(76-90 of 90)Companies can't remove reviews or game the system. Here's why
September 22, 2017
Driving a Data Driven Culture via Product Analytics
Today, we use Treasure Data to manage the ingestion of our product data across the organization. It allows us to unify event data, metadata around our customers, and external data we may collect via surveys, etc. Treasure Data powers daily reports that are used internally by 200+ people throughout our organization to get a pulse of how our products are being used and tracking towards success.
- Workflows is a fantastic product that is configurable to bypass the need for specific extraction APIs, but flexible enough to chain together and manage jobs.
- The GUI allows you to easily switch between Presto/Hive, share queries with other analysts and maintain an audit trail of past jobs.
- The Bulk Integration library allows you to easily add on additional datasets that may further enhance the 360-view of the customer.
- Today, Treasure Data does require some technical expertise, especially as it relates to new features. I appreciate that the approach allows a quick time to value but does require coding expertise.
- I would love if Treasure Data had the ability to connect workflows with Github easily via the GUI.
- Additionally, I would love better search functionality to easily mine past jobs rather than paging through them via the console.
September 13, 2017
Deep-Dive Data
Treasure Data has allowed our team to make data-driven decisions for product enhancements. Mining data efficiently means the difference between spending time on projects we know our users will want vs. hoping we deliver. Our students want products to help them be more successful and it is our job to deliver.
- Parallel processing
- Machine learning options
- Easy SQL
- Easier quick tips
- Q&A
- On-the-fly help
We are using Treasure Data as our main data analysis platform and its main users are Analytics team and Engineering team. We are actively using it for large data analysis such as traffic log analysis of our web site. It helps us to understand customers' needs based on the data in order to provide a better user experience.
- Treasure Data provides a comprehensive tool set necessary for the big data analysis such as a bulk data loader, data import/export connectors and a workflow engine along with powerful SQL based query engine (Presto).
- Treasure Data's support is amazing. They provided us the initial setup support as well as conducted the user training sessions. Also, their support engineers are almost always online and super responsive for any type of questions.
- Since Treasure Data is a managed service, we don't have to worry about the maintenance much. They keep their system up-to-date and make it accessible 24/7 for you. So we can focus on the analysis.
- Since Treasure Data's product is mainly open source based, it's updated very frequently. So, Treasure Data's documentation was outdated from time to time. It would be great if they can keep their documentation fresh and accurate.
- This is also related to Treasure Data's frequent update, but they are not providing a release note for each new release. They only provide a release note about the high-level updates happened in the past month. Most of the time, it's not a big issue. But when their update breaks our existing functionalities, it's pretty annoying to find out the root cause of the issue because we cannot know which modules are updated.
- Treasure Data has a bunch of functionalities, but not all of them have many users. So, when you use their unpopular functionalities, you might face some weird bugs or performance issues and need to work with their support engineers to find out solutions. Basically, they are addressing the issues very quickly, though.
September 13, 2017
A Data Tool For Every Team
We use Treasure Data to store logs from all areas of the company. It allows us to collect data on everything and analyze after the fact without having to worry about scalability. The ease of use makes it available to engineers, data scientists and product managers with little training required. It's great to not have to think about the data storage and focus on just what matters to the business.
- Easy web UI
- Scalable data solution with little setup time
- Responsive support
- Better communication when there are backend issues
- Faster web UI when inspecting tables with very large data sets
September 12, 2017
Easy interface to use Hive
We use it mainly as a logging tool and to take snapshots of important collections. It is also our gateway between the production database and Redshift, which is used by less technical users. Any task that requires use of big data goes through the Treasure Data pipeline. Currently, it is only used by the Engineering team.
- It uses Hive, which allows you to analyze TBs of data in a reasonable amount of time.
- Since some tables may have "duplicate" records with respect to some columns, TD provides functions that allow you to pick essential data from the different records that represent "the same event".
- When you need faster queries, there's also Presto. Presto does not have the overhead of Hive.
- When exploring a table, there should be a fast way to query it. E.g., a button that says "Query Table" and upon clicking on it, it opens the query page with boilerplate SQL prewritten
- When a database has too many tables, the Query page becomes unresponsive while it loads a lot of data from all the tables. There should be a way to opt out of that behavior.
- Bad error messages when a query fails. (For example, I've received errors about a parenthesis when the real issue was I didn't assign an alias to a subquery.)
- I often use the same tables. There should be a tab with "My Most Used Tables" or something like that, so I can get faster access to what I need in order to do work.
- API throws 404s in some instances where it should be returning 403s or 401s. It becomes hard to debug when new team members haven't received the same level of access as older team members.
September 11, 2017
Treasure your data
Treasure Data solves the most challenging connectivity problems between data acquisition and data formatting to relational databases. It is not just limited to cloud analytics but even more. Some of the best features that are really helpful for a data engineer like me is to reuse the results rather than re-execute. Treasure Data provides an option of a persist job_id for each query and retrieve the results within the 90 days using a unique job_id. It is used all over the organization including the developers, analysts and business users. Storage of data in Treasure Data is flexible which has a metadata to define. This helps to scale and accommodate the changing needs.
- Unique Job_id to persist results for a duration of 90 days.
- Metadata driven data structure to store importing data.
- Multiple Analytical engines to process the query based on the performance and need.
- Automated deletion of unwanted data by using an expiration configuration. This reduces the maintenance and monitoring of tables.
- Persist required job_id for more than 90 days
- More client APIs to ease the developers
- Good in computing but lot of scope for improvements in performance tuning
September 11, 2017
How Treasure Data help our business grow
It is being used across the whole organization. It helps us find the data that we need.
- Their system is reliable most of the time
- Easy to query using the web tools
- Good communication from Treasure Data support staff
- I'd like to see querying results faster if possible.
- The tables still take a long time to loan when there are a lot of them.
- There's no easy way to view the results if one cell contains too much data when it can't fit.
September 08, 2017
TD review
We use it for running queries for metrics purposes.
- Solid support
- Great for a technical audience
- Difficult to use for non tech folks
- Not cheap
September 08, 2017
User-Level Data and how it can change your business.
We use Treasure Data to track data about in-game events in our games. Our monetization and data teams analyze the data we record and use it to make business decisions for future growth of the products. We also are using it as a replacement to Flurry Analytics, because it allows us to analyze data at the user level, rather than an aggregate.
- User Level data tracking gives us the ability to figure out what the users do in our app, including their playstyle flows.
- Treasure Data allows us to track users across multiple games, to see if our Cross Promotion between our game is successful.
- Treasure Data queries have allowed our QA team to be a part of the data verification process, much more so than when we used Flurry Analytics.
- Website navigation is a little difficult for users who check on it once a month, because it seems like the website UI keeps changing very rapidly.
- Some of the search features on the portal are less than ideal and should be improved.
- Maybe additional support for queueing queries.
September 05, 2017
There's Treasure in your Data
We leverage Treasure Data for storage of unstructured data at scale. By using a tool which does not enforce a schema to write to, we are able to capture all data of interest. As our storage needs increase exponentially, Treasure Data has the speed to respond to our needs.
- The Treasure Data team was quick to provision additional infrastructure as needed so we did not experience any downtime when we recently experienced a tremendous amount of growth in our data storage needs.
- The documentation for implementing API calls is accurate, detailed and intuitive to navigate. This leads to quicker implementation and reduced Q & A sessions as new clients are onboarded.
- Stickers... They provided us a ton of Treasure Data stickers (and not cheap ones, the nice vinyl ones). Never underestimate the importance of stickers to a developer.
- I'm personally not a fan of AWS, which is the backend provider of the hardware leveraged. I find it is less consistent in delivering the tier of performance paid for, and this then requires selecting a higher level of package to ensure minimum requirements are met. I would like to see Treasure Data provide the OPTION of choosing Google Compute Engine.
- It would be a very nice feature to have some business rule filtering applied prior to ingestion of data, with invalid records placed in a type of quarantine storage. Nit picking, I know.
- It doesn't happen often, but when multiple queries are queued, it would be nice to set a priority level for these requests so our ad-hoc requests do not hold up the regularly scheduled infrastructure requests. Perhaps UI requests should default to a level of priority less than API initiated requests.
September 01, 2017
Built end-to-end Big Data pipeline in few months without dedicated engineering team
Treasure Data has allowed us to build an end-to-end Big Data pipeline in only few months, without a dedicated data engineering team. The main business problem we were able to address was how to unlock value from our 150+ million records MySQL Database, updated at a rate of 1 billion events per month. The Data team needed a quick and flexible way to explore, interrogate and train models with minimum impact on the company's development team.
In addition to this, we have used Treasure Data as an in-house web tracking and analytics solution by leveraging their Google Tag Manager integration. This has allowed the us to support Product Marketing teams in providing an independent and flexible way to track website usage and marketing campaigns performance.
In addition to this, we have used Treasure Data as an in-house web tracking and analytics solution by leveraging their Google Tag Manager integration. This has allowed the us to support Product Marketing teams in providing an independent and flexible way to track website usage and marketing campaigns performance.
- Flexible, easy and really schema free data landing/ingestion part. No need to worry about changes in the data. Treasure Data storage solutions adapt very well and gives you a lot of flexibility.
- Amazing Support! The guys are really great and available to answer most of the questions. Most of the time within less than an hour from when tickets are opened.
- Support for a lot of data integration solutions and continuous development of new solutions.
- Some documentation can be improved. Would be good to have more practical examples
- Website homepage/UX are changing all the time. I would like it to be a bit more consistent simple and minimal. Also, why do I have to log in again every time I open an new tab? It wasn't like this before.
- It would be nice if it was easier to work with fields which has been saved as a Json. Would be great to have more support on this. Not sure if this is a limitation with Hive and Presto or with Treasure Data.
September 01, 2017
No more data silos!
Treasure Data is used mainly by Data Team, which is under Engineering Department. However, analysts from other team could get access to Treasure Data too, in case their use cases require data ingestion, data integration, etc. Treasure Data helps us a lot on data integration tasks, which includes data warehouse and data mart creations, also other ad-hoc analysis.
- Lots of third party connectors
- No need to configure the machine
- Responsive supports
- R connector
- More customizable Treasure Workflow configuration
- No versioning on query changes, it's hard to identify who changes it, and which lines are changed
September 01, 2017
Taking our data platform to the next level!
Treasure Data is being used as an ETL platform and analytical database across the organization at Silvercar. Treasure Data supports three different business units within the company (our B2C airport car rental business, our B2B dealership hardware/software business and our B2B2C corporate product). At Silvercar we use Treasure Data to ingest data directly from our business systems (GA, AppAnnie, Salesforce, Netsuite, JIRA, etc.) and internal databases (mySQL) to create a data lake and run all our data transformation jobs accordingly. We also use Treasure Data as an analytical database to process our largest jobs.
We choose to use Treasure Data for this process because we can create, manage and schedule these jobs without the need for a data engineer or developer to create these custom scripts for us.
We choose to use Treasure Data for this process because we can create, manage and schedule these jobs without the need for a data engineer or developer to create these custom scripts for us.
- API/Connectors to a number of different business systems
- Easy to use analytical database
- Great customer support (direct chat, etc.)
- Does not integrate directly with our BI system (Looker) yet, although we can push data to Looker indirectly.
September 01, 2017
Do it – Find the Treasure in your Data
We use Treasure Data to make wielding inconceivable amounts of data blissful. We store and query massive amounts of time series data, and mirror other data sources to build complete, near-realtime reports that deliver insights for our customers.
- Customer Service
- API and User Interface
- Technical Expertise
- Keeping Presto up to date
August 29, 2017
Andy Wu's Review
Treasure Data is basically our hadoop solution. Log files are stored on Treasure Data. Data engineers/data scientists/analysts use Treasure Data for their day-to-day analysis.
- Interaction with support.
- Add more ways [to contact support] such as [Cisco] Spark.