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Easy to use, powerful tool
What do you like best about the product?
In my company, we embraced dbt cloud only a few months ago, looking for a tool to help us migrate from unserviceable legacy python scripts living on Airflow, which could be replaced with more straightforward SQL model solutions. It has fundamentally changed how the data team works, incorporating software engineering best practices like version control and documentation. It is a solution that not only a data engineer could use or understand.
What do you dislike about the product?
The Cloud IDE needs some work, and the integration with python should be faster and easier.
What problems is the product solving and how is that benefiting you?
Separation of test/development environments in the data warehouse, replacement of old and unserviceable processes, management of data transformations and load in one place.
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Game changing solution that brings software engineering best practices to data work
What do you like best about the product?
dbt brings a ton of best practices from software engineering: code versioning, automated testing, documentation, and support for deploying to multiple environments to the process of transforming data (ETL/ELT). Prior to using dbt I'd either have SQL code stuck in an IDE somewhere or obfuscated through an orchestration tool like airflow that made it difficult for non-coders to contribute effectively to a data team. Even as a senior data engineer I love the best practices and simplicity that dbt has brought to my team and I'm looking forward to its continued growth!
What do you dislike about the product?
Nothing major, dbt is still a relatively new product with active development so some minor annoying things are constantly being improved. For example, the IDE in dbt cloud wasn't super performant and auto-completion would not always work, but both things were improved drastically through the most recent release. We had a little bit of an initial learning curve for customizing our environment to suit our specific use-case, but I like that we weren't forced to do a bunch of customization out of the box so you could easily start using the product.
What problems is the product solving and how is that benefiting you?
Performing data transformation at scale using only SQL and deploying those to multiple environments while using code versioning. Our ability to create standardized reporting tables for multiple different projects has drastically improved.
Not sure we'd manage without it
What do you like best about the product?
dbt offers a graceful path for analysts and data practitioners to start leveraging long-standing Software Engineering best practices. Git, ci/cd, and the command line can seem daunting but dbt makes it easy to get started with the cloud IDE, then graduate to the command line/local IDE. The end-result is data resources that are re-usable, composable, tested, and higher quality since they benefit from git-style collaboration.
What do you dislike about the product?
Limited to transformations in the warehouse and need to rely on separate tool for jobs upstream of the warehouse and downstream delivery. Also, despite the simplicity, some entrenched analysts hesitate to adopt dbt since it's a significant paradigm shift.
What problems is the product solving and how is that benefiting you?
producing high-quality data resources for stakeholders with diverse needs
generating documentation that allow stakeholders to self-serve
a system for collaboration on data work
generating documentation that allow stakeholders to self-serve
a system for collaboration on data work
dbt is a gamechanger
What do you like best about the product?
dbt has transformed the way we work for the better. Not only does it enable modularity and re-usability across our data warehouse, but it also enabled us to easily set up development environments which resulted in much faster iteration time.
What do you dislike about the product?
The dbt cloud console is confusing and a bit difficult to use.
What problems is the product solving and how is that benefiting you?
dbt enables faster development of our data warehouse by providing dev tools and documentation out of the box.
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.
Everything we use from dbt is wonderful.
What do you like best about the product?
What we like the most, without a doubt, is the docs (lineage) feature. We were able to understand practically all of our data load flow by looking at this feature.
What do you dislike about the product?
So far I've enjoyed all the features we've used from dbt. Snapshots and tests are still missing, but they will certainly meet our needs.
What problems is the product solving and how is that benefiting you?
We had a lot of difficulty getting a complete view of our DW load. Many times forgetting to update some visual documentation that we did manually. However, with the docs (lineage) feature of dbt we no longer have this type of problem.
I can define my 6 month experience with dbt with the word Productivity
What do you like best about the product?
I believe that what I like most is the possibility of versioning the codes. This greatly facilitates the maintenance and evolution of projects, generating a lot of productivity for the team.
What do you dislike about the product?
Taking into account the maturity of the analytical engineering area, in 6 months of use we found no disadvantages.
What problems is the product solving and how is that benefiting you?
One of the main points that dbt is solving is the organization of our DW, data modeling is extremely flexible, easy application of version control, data quality testing, low learning curve and integrated documentation.
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