dbt Platform
dbt LabsExternal reviews
194 reviews
from
External reviews are not included in the AWS star rating for the product.
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
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.
Newbie to dbt
What do you like best about the product?
Easy to set up
SQL based
UI is intuitive
SQL based
UI is intuitive
What do you dislike about the product?
Lack of logs/metadata on job runs, the area around that isn't strong
What problems is the product solving and how is that benefiting you?
We are able to model our data for Looker outside of looker, so no need for PDTs etc
dbt adds so many features to SQL that I hadn't even realised were missing
What do you like best about the product?
There's a lot that I really like about dbt, particularly in the way it automates so much of the 'grunt work' out of transforming data. It's now tough to imagine how we handled refreshing our data warehouse before dbt!
Specifically I am a fan of how dbt:
- Works out dependencies rather than you having to stay on top of which models need to be run before others
- Automates testing, running tests on a schedule rather than when you remember to run the testing!
- Handles version control, allowing you to keep track of changes
- Provides a straightforward way of handling separate development and production environments, so you can see the impact of your changes before making them live
- Has a very active community of Slack users and package developers
Specifically I am a fan of how dbt:
- Works out dependencies rather than you having to stay on top of which models need to be run before others
- Automates testing, running tests on a schedule rather than when you remember to run the testing!
- Handles version control, allowing you to keep track of changes
- Provides a straightforward way of handling separate development and production environments, so you can see the impact of your changes before making them live
- Has a very active community of Slack users and package developers
What do you dislike about the product?
This is quite minor, but with documentation it can be a bit onerous to keep .yml files up to date, as the formatting needs to be exact and it is not always obvious when something is wrong, for example, with the amount of tab spacing.
What problems is the product solving and how is that benefiting you?
dbt has essentially "automated the boring stuff", letting us spend less time on boilerplate SQL work and more time thinking about the design of our models and how they best serve our reporting needs.
The best tool for data engineering on Snowflake!
What do you like best about the product?
I appreciate the ability to generate a DAG quickly. This capability is beneficial for developers to extend the data warehouse while understanding what may be impacted downstream. The recent improvements to the cloud IDE have been substantial.
What do you dislike about the product?
The cloud IDE is going through a complete revamp, and during that time, there have been some glitches in how it operates. There have also been a few outages over the past few months, but nothing of major consequence.
What problems is the product solving and how is that benefiting you?
We use DBT as part of our tech stack for our embedded analytics product. It has allowed us to go from no data warehouse to a stable and agile data warehouse in weeks.
Analytics Engineering revolution!
What do you like best about the product?
dbt is a potent tool with lots to explore. The data lineage is fantastic, where you can easily see if a small change brakes a model downstream.
Tests are integrated into it, which we use a lot, specially custom ones. Macros are handy and fantastic resources for controlling tests, functions, environment behaviour, etc.
Building new models is effortless because the analyst only needs knowledge of SQL (and a bit of dbt but like any tool).
Finally, I love the dbt community spirit, the excellent documentation and how dbt is continuously improved.
Tests are integrated into it, which we use a lot, specially custom ones. Macros are handy and fantastic resources for controlling tests, functions, environment behaviour, etc.
Building new models is effortless because the analyst only needs knowledge of SQL (and a bit of dbt but like any tool).
Finally, I love the dbt community spirit, the excellent documentation and how dbt is continuously improved.
What do you dislike about the product?
However, I am worried about the scalability of using one repository. There were two of us when we started using dbt, but we are +10 now and this reflects in running times, release management and CI/CD. We would love to see a bit more support on this.
Another thing is the alerting system, we can set up tests as warnings, but you need to enter dbt cloud on purpose and see the alerts inside the job. We are building alerts out of the box because this doesn't work for us.
Lastly, it was difficult for me initially to adapt from the "old" data stack, but I am 100% dbt converted now.
Another thing is the alerting system, we can set up tests as warnings, but you need to enter dbt cloud on purpose and see the alerts inside the job. We are building alerts out of the box because this doesn't work for us.
Lastly, it was difficult for me initially to adapt from the "old" data stack, but I am 100% dbt converted now.
What problems is the product solving and how is that benefiting you?
We previously had an ETL tool which was very difficult to maintain and contribute to.
This was a huge bottleneck, and dbt has allowed many people to contribute to modelling and building dashboards.
This was a huge bottleneck, and dbt has allowed many people to contribute to modelling and building dashboards.
DBT is a great out-of-the-box solution to huge problems found on data-driven organizations
What do you like best about the product?
I like how it enables analytics engineering (building pipelines) with best practices and has other really useful features (such as documentation and testing). I haven't found a product this complete in other competitors.
What do you dislike about the product?
I dislike the multiple indentation errors I get on YML, I dislike having to deal with separate "packages" for basic stuff that should be included on DBT regular release, I dislike the "feel" of the typing on DBT Cloud. I feel I can't type code with the same speed and flow I have when using VSCode.
What problems is the product solving and how is that benefiting you?
DBT is solving SQL standardization, lack of documentation, lack of testing, lack of a good optimized code across companies, which makes us lose less time debugging stuff or trying to figure out someone elses scripts and upstream/downstream tables.
Overall good developer experience and great ELT solution
What do you like best about the product?
- Git integration and software engineering best practices
- Lineage
- Metric layer
- Tests
- Incremental materialisations
- Quite quick response times for cloud support tickets
- Lineage
- Metric layer
- Tests
- Incremental materialisations
- Quite quick response times for cloud support tickets
What do you dislike about the product?
- Lack of being able to run changes on a dev environment without having to create a separate project
- Lack of explanation of best practices for how developer and deployment schemas should be set up/named
- Lack of explanation of best practices for how developer and deployment schemas should be set up/named
What problems is the product solving and how is that benefiting you?
- We needed a more scalable solution to the python scripts we were running to perform ELT on our data
- Batch processing for redshift data which we then use in our prod systsem
- Batch processing for redshift data which we then use in our prod systsem
Great tool to get more visibility into data warehouses
What do you like best about the product?
Being able to analyse dependencies between data models easily by following the lineage graph. Also, being able to embed tests while creating data models in a straightforward and fast way reduces the tendency to look at testing as an afterthought, effectively making data pipelines more robust.
What do you dislike about the product?
It is still not possible to precisely pinpoint the root cause of a certain error. dbt is able to identify the table where the error happens, but not the exact fields involved in it.
What problems is the product solving and how is that benefiting you?
I used to spend a lot of time trying to figure out the dependencies between data models. With dbt I just have to check the documentations, specifically the lineage graph, and I know in an instant how multiple different data models are related.
The era of shaky SQL templates is over
What do you like best about the product?
DAG execution when rebuilding an entire schema/mart, docs, tests (referential integrity especially), dbt utils and packages (redshift utils especially) and many more.
What do you dislike about the product?
There is little that I dislike, IDE version 2 has improved, one can still over-write default macros in order to achieve custom functionality.
What problems is the product solving and how is that benefiting you?
For instance, my first three months with tool were spent on a migration of all kinds of different sql processes which accumulated over 5 year within the company, now all simplified and unified under one repository. The main problems we solve are staging transformations (used by legacy reporting) and building dimensional schema for self-service analytics as well as embedded analytics.
showing 41 - 50