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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.
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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.
Invaluable tool that fosters a culture of transparency
What do you like best about the product?
Dbt frees me up to understand data logic without asking the data engineers, thus unblocking me to move faster and make better decisions
What do you dislike about the product?
Sometimes the error messages are not easy to understand and require some assistance, but this is rare
What problems is the product solving and how is that benefiting you?
Dbt makes it easier for all members of a data team to understand logic, upstream/downstream dependencies, increase the quality of critical data, and uplevels the data IQ of an entire company. By ensuring that our data is logical and well-tested, we free up our time to do less debugging and more analysis.
No brainer when working with batch processing
What do you like best about the product?
It gives you a set of tools to improve data governance and quality. It allows developers to apply many software engineering concepts to create a robust data pipeline.
What do you dislike about the product?
It can be complicated to implement and maintain for a non-technical person or someone who is not proficient in SQL. One of the main limitations for me was that you couldn't run python, which apparently was recently implemented (I haven't tried it yet).
What problems is the product solving and how is that benefiting you?
It helps you to create a robust data pipeline that holds the "source of truth" for data being used for analysis. Using dbt you can automate a lot of transformations and tests, work in collaboration with your peers and write robust documentation.
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.
Gamechanger
What do you like best about the product?
Templating SQL, enabling aspects of data engineering to be treated more like software engineering. CI/CD pipelines. Autogenerated lineage and documentation. Git intergration.
What do you dislike about the product?
I think dbt should be more opinionated on best practices around staging/intermediate/marts and the vocabulary used. Would also like to see further niche coverage (snowflake streams).
What problems is the product solving and how is that benefiting you?
SQL Model lineage and versioning. It makes it much more clearer the upstream sources and downstream impacts of changing any SQL model. Integrated version control and deployment environments also makes rollbacks and testing much easier.
Really helpful for cleaning up data
What do you like best about the product?
The product is really good, but the support documentation and community surrounding the product are really helpful in implementing the product.
What do you dislike about the product?
It takes a lot of training to get competent with the product - not unexpected.
What problems is the product solving and how is that benefiting you?
Orchestrating our data into usable models
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