Item: Treasure Data
Rating: 9
Author: Verified User

Use Cases and Deployment Scope

We use Treasure Data to ingest multiple data sources and once processed export them to Tableau dashboards.
One of our clients has data in more than 10 places (API, websites, local files), we use Python to gather all these sources and ingest them to Treasure Data. We also use Unity SDK and Unreal SDK to track user behavior in games.

After a processing passes we export the datasets to Tableau.

Pros and Cons

Treasure Data has a lot of connectors that allow for ingesting and exporting data easily.
Treasure Data has a Python library to easily interface with TD SQL queries to Pandas DataFrames.
Treasure Data has a very powerful workflow tool named DigDag that simplifies the multiple ETL processes we use.

The website UI and especially the searching option of some queries should be improved. The user should be able to create labels to group queries.

Return on Investment

Weekly Data ingestion used to be done in 1 full day. Now with Treasure Data it's a 3-hour process.

Innovative Uses

The Workflow Tool (DigDag) developed by Treasure Data is open source so we deployed it in our server to manage Python scripts [to] import scripts.

Alternatives Considered

Domo, Swrve and Upsight

Treasure Data is better in terms of performance, the SDK is more flexible and Treasure Data is not limited to a static format like Swrve. The competitors provide more out-of-the-box solutions but the quality we can aim for with Treasure Data is better in any case.

Likelihood to Recommend

When working in a diverse data ecosystem where multiple data sources and outputs coexist, Treasure Data is a very good option. If the data pipeline is mainly powered by Python TD can be easily leveraged to move quickly data from one source to another. The web UI can be easily used by SQL newbies but also by experienced Analyst with the workflow and Python options.

Treasure Data will Increase your productivity!

Overall Satisfaction with Treasure Data