Overall Satisfaction with Treasure Data
We use Treasure Data to collect and transform data from multiple sources for the purpose of product analytics.
- Great library of connectors
- Workflow engine is simple, but in a good way
- Presto query performance is very good
- Support is very good, especially with live chat
- Presto support is missing key types making it surprisingly verbose to do this as you need to cast things back and forth
- Lack of support for deploying our own connectors
- Cannot write Ruby or Python code in workflows
- We were able to create a true product analytics capability in record time
- We we able to democratise product analytics by exposing it in our BI tools and in Salesforce thanks to Treasure Data
- Send product metrics to Salesforce
- Centralised digital analytics using Treasure Data Javascript SDK, then syndicate to other tools like Amplitude
- As a data warehouse using Presto
Best balance of price, data collection, query engine, workflow engine and especially data output to targets other than data warehouses.
Sisense is a great BI tool, and is not truly a replacement for a tool like Treasure Data, but if you want to do ETL in your BI tool and your needs are basic, Sisense could theoretically replace Treasure Data.
When we were evaluating, Astronomer was promising, but too early in its lifecycle. If you like Airflow, it could be a good fit, but Treasure Data's Digdag workflow engine is a great starting point for DAG based ETL.
Sisense is a great BI tool, and is not truly a replacement for a tool like Treasure Data, but if you want to do ETL in your BI tool and your needs are basic, Sisense could theoretically replace Treasure Data.
When we were evaluating, Astronomer was promising, but too early in its lifecycle. If you like Airflow, it could be a good fit, but Treasure Data's Digdag workflow engine is a great starting point for DAG based ETL.