Treasure Data is great for budding data teams.
July 15, 2019
Treasure Data is great for budding data teams.
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with Treasure Data
Treasure Data is used as our production data warehouse. We also use Redshift, but are increasingly moving away from that - our cleansed, production datasets primarily exist within Treasure Data.
Treasure Data provides transparent access to data for auditors, which has been a huge business problem it's addressed. We can present specific schemas to auditors to monitor, and track all of their queries easily.
Treasure Data provides transparent access to data for auditors, which has been a huge business problem it's addressed. We can present specific schemas to auditors to monitor, and track all of their queries easily.
- Excellent console interface.
- Incredibly fast processing, and simple ability to shift between Hive and Spark.
- Simple workflows - easy to understand and use.
- Workflows could be more expansive - Python capability, for example.
- Data visualization would be incredibly powerful.
- Being able to merge directly to datasets outside of TD would be powerful.
- Treasure Data has provided the value of a full data engineer for half the cost.
- Treasure Data has improved our analysts capabilities by a multiple of two.
- Treasure Data has provided ease of data access.
- As a tool for providing greater transparency.
- As a data processing engine - we perform much of our back end processing for data applications in Treasure Data.
- As an audit tool - TD query logs are very useful for this.
Redshift does not have a simple web console interface for us to use. However, one area where Redshift shines and Treasure Data does not is in its pricing model. Auto-scaling is a great feature in Redshift, whereas the Presto-hours business model can be somewhat limiting at times. On the flip side, auto-scaling can ramp up costs if an unsophisticated user writes a bad query. So there are tradeoffs to both.