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dbt Cloud is the perfect tool for our data transformations
What do you like best about the product?
We have highly complex SQL transformations. dbt allows us to engineer them properly, writing DRY, modular code and using CI/CD. Jinja in dbt allows us to work with SQL data models as objects. We use Fivetran as the E and L in ELT, and Fivetran has an extensive library of dbt packages to provide transformations for many of our data sources.
What do you dislike about the product?
dbt Cloud's IDE has been improving, but still doesn't have many of the features coders have come to expect from IDEs. And the orchestration available in dbt Cloud is limited.
What problems is the product solving and how is that benefiting you?
Without dbt to store and maintain our SQL transformation workflow, we would have a mess of SQL scripts that would be very difficult to manage. Without dbt, we would need to write something that had dbt's functionality.
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UI based Version Control and ETL Orchestration
What do you like best about the product?
DBT Cloud lets users set up transformations and orchestrate code runs from a UI-based IDE in the cloud for users who aren't as familiar with using the command line. By leaving engineers to do the coding rather than spending time digging through documentation, our team could reach functionality faster and with less fuss than expected. The ability to test in a safe environment by default before you push your changes to live makes version control easy for users not familiar with git principles.
What do you dislike about the product?
The cloud-based IDE has a long-ish startup time (30 seconds or longer) and locks you out of editing while it syncs after you save a file. This leads to a little bit of frustration when making minor edits across multiple files. Some other minor frustrations related to the Cloud IDE come to mind, like how column names aren't sensed while you're typing, leading the user to write out column names that could be easily polled during the lineage population. In addition, the testing documentation was a little confusing, leading to me trying different things to get it to work. In the end, I learned that you have to prefix the name of a test model with "test_" to get the orchestrator to see the model as a test. However, after a couple of speed bumps, most issues I have turn out to be minor and do not impact the product's overall usefulness.
What problems is the product solving and how is that benefiting you?
DBT Cloud has allowed us to migrate from one ETL partner and re-create our transformation files with version control and replicability in mind first and foremost. Additionally, for users who aren't familiar with command line interfaces, we can spin up new users and share work easily while everyone can push to the same deploy environment.
DBT is top-notch.
What do you like best about the product?
DBT makes developing a data warehouse fun. My favorite is the testing functionality. I am no longer anxious that reports are sent with incorrect data!
What do you dislike about the product?
I don't love the UI. However, I develop mostly locally.
What problems is the product solving and how is that benefiting you?
Data testing, documentation and warehousing.
dbt is a must for every organization
What do you like best about the product?
The upsides of dbt is that it makes it easy to implement software engineering best practices like versional control, modular & DRY code to your data team.
What do you dislike about the product?
I have found that the cloud IDE can be buggy at times. Hopefully with the updated IDE releasing soon this will improve the user experience.
What problems is the product solving and how is that benefiting you?
I am making core fact and dimensions tables that will be driving several reports and dashboards for organiziaton. Dbt allows me to be more productive and scale easier.
dbt - believe the hype!
What do you like best about the product?
As a development framework, it's hard to imagine something more collaborative, accessible, yet also lending itself to software development best practices.
What do you dislike about the product?
While its SQL basis makes development simple and straightforward, it can sometimes feel a bit limiting. Thankfully templating (and now python) alleviates a lot of this.
What problems is the product solving and how is that benefiting you?
dbt is empowering data teams to keep pace with rapidly evolving business logic in high growth settings, and to do so sustainably. We've been able to scale with far fewer headcount thanks to dbt.
Can't live without dbt
What do you like best about the product?
The best thing about dbt is its Customer Service team. They have the best customer support team I've encountered from any product in any industry. They are ready to support your questions. You get an expert on the first chat. I've never needed to escalate to a higher level of support, 100% questions answered every time.
Then there's the obvious product stuff we love: Organized data pipelines, easy to naviage DAG to trace back models to the source, automation, and DevOps best practices built into analytics pipelines.
Then there's the obvious product stuff we love: Organized data pipelines, easy to naviage DAG to trace back models to the source, automation, and DevOps best practices built into analytics pipelines.
What do you dislike about the product?
dbt cloud can be improved in the following ways:
- autocomplete! (Like intellisense included with apps like dbeaver)
- better way to search through files (search for text within a folder)
- multiline edit (like in VS Code with CTRL+ALT+Down)
- less simplified git actions (like stage only certain files to commit)
- autocomplete! (Like intellisense included with apps like dbeaver)
- better way to search through files (search for text within a folder)
- multiline edit (like in VS Code with CTRL+ALT+Down)
- less simplified git actions (like stage only certain files to commit)
What problems is the product solving and how is that benefiting you?
Our organization has access to a lot of tools with overlapping capability. dbt's DAG and documentation feature make the learning curve worth it. Just don't forget to document your models! :)
Easy to use, tailor-made for the the cloud, pairs well with Snowflake.
What do you like best about the product?
dbt is based primarily on SQL, which is very easy to use, and paired with databases like SnowFlake, which includes a broad and sophisticated SQL implementation, there is no end to the transformations you can do. Plus, there is a considerable open source library of code to perform common transformations based on general ETL needs, and transformations specific to many cloud applications. Companies like FiveTran, for example, produce and provide sophisticated dbt packages that make challenging tasks very easy. Finally, the user interface was obviously designed and created by people who have done this work. Once you get familiar with it, repetitive and mundane tasks are done quickly and easily. Error reporting and debugging works smoothly. I am still relatively new to this, but I am really enjoying dbt, and looking forward to being engaged with the community that seems willing and excited to contribute to the growth of the product and development of the user community. dbt transformations use the database engine to provide the resources to do ETL, and
What do you dislike about the product?
I think the surrogate key module in dbt_utils misses the mark. Maintaining meaningless integer surrogate keys is the preferred approach in my opinion. They use less space, and perform faster.
What problems is the product solving and how is that benefiting you?
Fast and efficient ETL: easy and fast to develop, fast to execute. Leaves the data in place so there is little latency and minimal egress/ingress charges. Paired with data pipelines like FiveTran, and a data warehouse engine like SnowFlake, the process of getting data from any cloud or on-prem application into a data lake, or data warehouse where it can be sued for reporting, analytics and more is considerably faster than any combination I have used in the past.
dbt is a game changer!
What do you like best about the product?
The simplicity with which we can model, test our logic, and update queries to help create systematic improvements is unparalleled. I love that you can test and run while only selecting certain parent/child models. This saves tons of time for us.
What do you dislike about the product?
I really wish we could ingest files directly to dbt without having to use a file ingestion software. If we could connect directly to things like salesforce/google drive, etc, I would love that. Dislikes are tough to come up with as we are pretty satisfied with our needs.
What problems is the product solving and how is that benefiting you?
The problems that dbt solves for us is modeling. The ability to jump into a data stack early in the flow is important, and dbt allows us to standardize definitions across all of our source types.
dbt is just awesome!
What do you like best about the product?
macros, data lineage and documentation are my favorite features
What do you dislike about the product?
I'd love to use dbt across multiple databases at the same time on the same project, and that's not possible yet
What problems is the product solving and how is that benefiting you?
dbt is focused on managing the data transformation process, and that's what we use it for. dbt is helping us with: i) keeping our data warehouse organized, ii) providing better visibility to the team about the existing data, iii) building better data models, and thus better dashboards, and better insights, iv) and on top of that, it's a tool that allows us to work in a collaborative workspace.
Great all around transformation tool
What do you like best about the product?
The model structure and process structure for building complex models in dbt is amazing. Leveraging jinga to compile sql has made complex data problems easier to solve. Integrating with git to add version control to transformations has been a great overall experience that has allowed code deployment processes to take places that normally are skipped. All of this resulting in more reliable transformations with fewer duplicated transformation/business logic.
What do you dislike about the product?
Dbt overall is a great product with a bright future but as with any software there can be room for improvement. At times the web interface can be unreliable failing to connect to git or unable to commit changes. This can cause valuable work to be lost when working online. The onboarding experience can be difficult when it comes to understanding things like Jinga and how to best leverage dbt and best practices. As the community of users grow i'd expect more content available to reduce this barrier.
What problems is the product solving and how is that benefiting you?
Making the transformation steps modules and sql based has allowed me to collaborate with many different stakeholders that normally are not comfortable diving deep into code. Jinga has removed the need to do adhawk scripts to generate sql to execute.
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