Reviews from AWS Marketplace
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
External reviews are not included in the AWS star rating for the product.
An Absolute Powerhouse
What do you like best about the product?
There's a lot to like! Especially the ability to: connect dbt with GitHub for version control and documentation, add testing, work on multiple branches at one time, and visualize and manage the relationships between models. We have created documentation that is pulled from the schema files to our BI tool, which is super helpful. Being able to install packages and utilize macros is incredibly powerful as well. I'm also excited about the recent addition of Python - we plan to use it this year. dbt is also surprisingly easy to use; I started using it in my first data job and loved it immediately!
What do you dislike about the product?
Honestly, there's nothing that I dislike. Jinja has been the most challenging part since it requires thinking differently, but that's just a learning curve and nothing against dbt itself.
What problems is the product solving and how is that benefiting you?
My use case has been breaking down data silos and building data models that generate a combined view. With dbt, we've automated reports that saved teams a ton of time and gave them a unified view of metrics they've never had before! We've also been able to set up customized tests that alert us to problems within the business/our own software.
- Leave a Comment |
- Mark review as helpful
Makes data transformations easy to maintain
What do you like best about the product?
Maintaining data transformations are easy with SQL- and git-based workflow. Integrates nicely with Big Query.
What do you dislike about the product?
IDE is not perfect but improving. Git support lacks some basic functionalities.
What problems is the product solving and how is that benefiting you?
Maintaining our data pipelines is easier with dbt than other tools or techiques I've tried.
An amazing product that change the way any company handle data
What do you like best about the product?
The ability to quickly develop, test and deploy models at scale has increased our team productivity enormously and lead us to focus more on adding value to the company and less on maintaining ETL's
What do you dislike about the product?
I would love it if the python compatibility became better and faster, it will dramatically change how we build models and I can't wait to see it live. I also think the cloud IDE can be faster
What problems is the product solving and how is that benefiting you?
The problem of unifying and transforming data to better fit the visualization and analysis needs throughout the different teams in the company. We can now focus on adding value and not in maintaining ETL's
DBT is saving our time and money
What do you like best about the product?
I love that we can document destinations and sources and automatically test the sources. We recently tried making dbt package and hopefully will soon bring it to production.
What do you dislike about the product?
I miss the option to define loaders the same way we can define exposures. We are using 4 different loaders, and it would be beneficial to document the responsible person and the link to the loader.
What problems is the product solving and how is that benefiting you?
DBT is solving the automagical orchestration of our queries, testing, documentation, and much more. And it is built on top of git!! At the same time, the dbt community is fantastic.
dbt is very intuitive to use and works great in our data tool stack
What do you like best about the product?
dbt cloud's structure and the ease of transforming the data without worrying about the warehouse connection. Also being able to see the lineage easily to understand the connections.
What do you dislike about the product?
sql help could be much more improved and also showing when there is a false syntax before having to compile or run the preview because there are many times that you need to go out of the platform just to check sql syntax
What problems is the product solving and how is that benefiting you?
transformation of that in a structural way where you have control over the lineage greatly and surfacing reliable models that can be exposed to consumers directly
DBT Wow!
What do you like best about the product?
I love how powerful dbt is. I use it on a daily basis for all my ELT needs! It is compact and efficient
What do you dislike about the product?
I don't have much to say about what I dislike about dbt.. nothing!
What problems is the product solving and how is that benefiting you?
DBT solves many issues for us, including documentation, DAGs, relationship testing, data integrity constraints, performance, etc.
Best tool to handle the T of ETL
What do you like best about the product?
I've been using "traditional" graphical ETL tools for 15+ years (Informatica PowerCenter, Talend, BODS...) and they never seemed "right".
More often than not, we just ended up having to use SQL instead of the transformations of the ETL tool, as this was much more robust / performant / flexible.
With dbt, the tool finally felt right:
- No more useless graphical interfaces.
- Heavily focused on leveraging the database performance thanks to SQL.
- But still being way better than just SQL since dbt automates everything that can be automated:
* No need to create the tables, no need to specify the data types of each column: this all comes automatically from the source.
* No need to specify the order of the transformations: this is automated based on the dependencies of each model.
* Easy integration of data tests.
* Possible to use the Jinja templating language to
* Now possible to use python to transform the data.
* Good integration of the documentation.
* Possible to set up the dbt repository in git to have great version control.
* Possible to use dbt cloud with github to automate basic testing of each pull request.
* Easy deployment to production thanks to CI / CD / github integration.
* Packages available that can answer specific needs.
* Great active community behind the tool.
* And probably other things !
More often than not, we just ended up having to use SQL instead of the transformations of the ETL tool, as this was much more robust / performant / flexible.
With dbt, the tool finally felt right:
- No more useless graphical interfaces.
- Heavily focused on leveraging the database performance thanks to SQL.
- But still being way better than just SQL since dbt automates everything that can be automated:
* No need to create the tables, no need to specify the data types of each column: this all comes automatically from the source.
* No need to specify the order of the transformations: this is automated based on the dependencies of each model.
* Easy integration of data tests.
* Possible to use the Jinja templating language to
* Now possible to use python to transform the data.
* Good integration of the documentation.
* Possible to set up the dbt repository in git to have great version control.
* Possible to use dbt cloud with github to automate basic testing of each pull request.
* Easy deployment to production thanks to CI / CD / github integration.
* Packages available that can answer specific needs.
* Great active community behind the tool.
* And probably other things !
What do you dislike about the product?
The python support opens the door to a lot of possibilities, but it is still new and (at least to me) it is not super clear if we can/should use it to handle some of the "EL" part of ETL (e.g., talk with external APIs to download extra data, or push data to other services). I'm guessing this could work but it is not recommended, at least at the moment.
What problems is the product solving and how is that benefiting you?
We do all our data warehouse transformations with dbt.
The tool makes this simple and robust.
dbt helps us automate what can be automated in the development and deployment process, so we can better focus on our specific domain knowledge.
The tool makes this simple and robust.
dbt helps us automate what can be automated in the development and deployment process, so we can better focus on our specific domain knowledge.
Efficient, easy to use and reliable for development
What do you like best about the product?
Fast development times and ease of use are my two favourite qualities of dbt. It allows our team to build and test new sql models very quickly, and even less experienced team members with basic knowledge of sql are able to contribute.
What do you dislike about the product?
With large complex models tracking column lineage can be tricky. Especially as fields go through many layers of transformations, identifying where certain data quality issues are arising can be challenging. I realise this is not an easy problem to solve but column level lineage would be super useful at speeding this process up.
What problems is the product solving and how is that benefiting you?
The fast dev times allow us to efficiently accommodate business changes into the logic of our data transformations. As a relatively small data team of 8 serving an org of 600, keeping up with changes to the business is vital for us to be able to work on other high-value data projects.
Easy to use , ci/cd, versioning control, data lineage in one app
What do you like best about the product?
Data lineage and github integration for versioning control
What do you dislike about the product?
A bit steep learning curve for first timer since it use new methodology
What problems is the product solving and how is that benefiting you?
Github integration for version control. Data lineage for ELT
Changed the game
What do you like best about the product?
Extremely user-friendly, DBT allows our analysts to build, test, document and deploy datasets independently without multiple dependencies on engineering departments. It does not require significant upskilling for junior members and eases knowledge share and confidence through lineage and documentation.
What do you dislike about the product?
Not much at all! The constant improvements to dbt-core and dbt-cloud keep pace with community requests. Being able to post run results to multiple slack channels would be favorable, but this is in the planned release.
What problems is the product solving and how is that benefiting you?
Understanding data lineage was a previous problem. Knowing the impact of changes made on downstream datasets was challenging and required significant investigation time. This resulted in slower development and reduced confidence. DBT has allowed the team to onboard new members quicker and with greater confidence leveraging documentation of datasets and lineage.
showing 41 - 50