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Amazing Product We Have Used for Years
What do you like best about the product?
DBT allows us to transform our data in a way that is easy to learn (with a sql background), easily adjustable, and incredibly powerful. Every dataset that is used by a business user at our company goes through DBT one way or another, some as simple as cleaning up some fields and names, and other through complex algorithms.
What do you dislike about the product?
The code first style can be a little daunting to teach to newer people who have never seen a tool like this before or do not know SQL. I think the investment in teaching SQL/DBT is well worth the value provided.
What problems is the product solving and how is that benefiting you?
We have dozens of datasets that come in in many different formats and levels of cleanliness. DBT allows us to take any dataset and convert it in a clean, scalable way into datasets that are easy to use for analystics and process automation
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The Only Analytics Engineering Toolkit You Need!
What do you like best about the product?
dbt makes it very simple to plan, construct, and continuously transform our company data on regular intervals in order to power our decisions on product design, user experience, and much more. Moreover, its Slack community will not let you fail and is incredibly helpful.
What do you dislike about the product?
The fact that dbt is constantly expanding what you can transform and helping you to code those transformations makes it a hard software to not be married to. Marriage to a software is often the death of an operating budget over time, but the value is incredible with dbt.
What problems is the product solving and how is that benefiting you?
dbt provides a cloud-based job-running environment that I can set and forget, an integrated development environment to code my SQL and Python transformations in, and a way to easily deploy automated testing of my code to ensure data quality and reliability. Moreover, dbt makes it easy to write your code with native format assistance (SQLFMT), active syntax recommendations, shortcuts, and a suite of learning videos and docs.
My experience with dbt has been transformational, and has democratized data engineering at the org
What do you like best about the product?
- The fact that is it sql-based
- Cloud UI is ver intuitive
- Easy to learn
- Cloud UI is ver intuitive
- Easy to learn
What do you dislike about the product?
- Cloud IDE is kind of slow
- Rebuilding data models is sometimes slow
- No resources on how to optimize jobs
- Rebuilding data models is sometimes slow
- No resources on how to optimize jobs
What problems is the product solving and how is that benefiting you?
- Democratizing data eng across organizations
- Establishing a universal source of truth downstream for all orgs
- Ability to stand up data infrastructure quickly
- Establishing a universal source of truth downstream for all orgs
- Ability to stand up data infrastructure quickly
dbt is an essential component of our tech stack
What do you like best about the product?
easy to use
those w/ SQL skillsets are locked & loaded to leverage dbt
those w/ SQL skillsets are locked & loaded to leverage dbt
What do you dislike about the product?
nothing that I can think of!
the product is great!
the product is great!
What problems is the product solving and how is that benefiting you?
it helps us process & summarize customer data, working well w/ our dwh
If you're doing analytics, you need dbt
What do you like best about the product?
dbt was easy to integrate into our stack, and immediately began adding value. dbt has levelled up our data practice and helps to ensure that we're shipping reliable, consistent and accurate tables to our stakeholders. It allow for repeatability so that stakeholders don't need to know sql to use a useful table.
What do you dislike about the product?
If you have multiple projects in your warehouse (BigQuery) in our case, you have to set up dbt for each. It would be ideal if, within BQ, we could consolidate projects somehow - although given security permissions, potentially not possible or practical.
What problems is the product solving and how is that benefiting you?
Previously were modelling tables in our warehouse or BI layer leading to a lot of confusion and breaks in data. Using dbt, we can easily (i) enforce PRs for any new tables, (ii) enforce tests, (iii) leverage macros and (iv) define documentation.
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.
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.
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