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so usefull
What do you like best about the product?
we can made maintainalble and scalable data infrastructure, this make user easy for working with data, transforming data become easy, that is why we use it in our projects also provides some standardies features
What do you dislike about the product?
we can not able to load the data from source , we can only able to use data present in dataware houses, new users may face difficulties while learning, support also not that good from community
What problems is the product solving and how is that benefiting you?
It provides standardize transformation process that help in less error, version control is also a good feature
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Transforming data with dbt
What do you like best about the product?
dbt is an efficient solution that is capable of transforming raw data into important insights. I've been utilizing it for data transformation and it integrates easily with most of the elt tools. It has tons of features that enhances the development experience.
What do you dislike about the product?
I've experienced issues when it comes to managing dependencies between models also realtime work isn't possible which is much needed.
What problems is the product solving and how is that benefiting you?
dbt helps us in data quality checks and preparation before making it available for everyone. It ensures data accuracy and maintains regularity of the transformed data through automation testing.
It's like seeing an old friend that you really liked but haven't seen for a while.
What do you like best about the product?
At it's core, DBT aligns three technologies to deliver knowledge better: SQL, YAML, & Jinja. You can do a lot with just SQL and YAML. Adding in Jinja makes SQL feel a lot more like traditional development. I kinda missed that. It's like seeing an old friend that you really liked but haven't seen for a while.
dbt is magic for transforming and modeling data. It's a platform that allows us to wrangle, shape, and organize the data to model the business. With the help of DBT, we can implement the principle of separation of concerns to organize and manage our transformations.
One of the key tools DBT offers is Directed Acyclic Graphs (DAGs), maps that illustrate the path our data takes from source to the final destination. These maps illustrate the data transformation arc. We start with the source data, which is often messy and unrefined. We use DBT to perform a series of transformations, taking the data on a journey from a multiverse of chaos to a world of understanding. We clean the data, apply business rules, and ensure the data conforms to our business dimensional models. These models or core business logic serve as the foundation for reporting.
As we progress along the transformation arc, our data starts to take shape. We can build data marts for specific business areas or functions. These data marts are built with our business dimensional models, ensuring that the data is structured in a way that supports efficient analysis and reporting.
Reporting on top of our business dimensional models. With the data now organized and modeled in a meaningful way, we can unlock valuable insights and empower decision-makers with actionable information . . . at scale. We can slice and dice the data, apply filters, and drill down into specific dimensions to understand trends, patterns, and outliers. The reports we develop are consistent because they come from a single source of truth, the business dimensional model.
dbt is magic for transforming and modeling data. It's a platform that allows us to wrangle, shape, and organize the data to model the business. With the help of DBT, we can implement the principle of separation of concerns to organize and manage our transformations.
One of the key tools DBT offers is Directed Acyclic Graphs (DAGs), maps that illustrate the path our data takes from source to the final destination. These maps illustrate the data transformation arc. We start with the source data, which is often messy and unrefined. We use DBT to perform a series of transformations, taking the data on a journey from a multiverse of chaos to a world of understanding. We clean the data, apply business rules, and ensure the data conforms to our business dimensional models. These models or core business logic serve as the foundation for reporting.
As we progress along the transformation arc, our data starts to take shape. We can build data marts for specific business areas or functions. These data marts are built with our business dimensional models, ensuring that the data is structured in a way that supports efficient analysis and reporting.
Reporting on top of our business dimensional models. With the data now organized and modeled in a meaningful way, we can unlock valuable insights and empower decision-makers with actionable information . . . at scale. We can slice and dice the data, apply filters, and drill down into specific dimensions to understand trends, patterns, and outliers. The reports we develop are consistent because they come from a single source of truth, the business dimensional model.
What do you dislike about the product?
dbt requires a mindset change. You have to buy into how they think about modeling. It's opinionated. dbt is method-agnostic (data vallt, mesh, kimball). But structure matters and you need to spend some time to understand dbt's mindset around stricture.
What problems is the product solving and how is that benefiting you?
Let me tell you about the state of our data. At the time, we didn’t know. That was the issue. It was a black box. Our data model was opaque with logic scattered all across the data stack. As we pick around the edges a picture starts to form. Imagine a dense, thorny briar patch, each thicket representing a tangled mess of information. That's how I see it—unruly, interlacing, and chaotic. Management has a different take. They call it "spaghetti," a swirling plate of tangled noodles. It’s actually not far from the truth. Each report fed directly from the source, the logic for each was self-contained and sometimes borrowed.
Transformation step of ETL/ELT pipelines made easy
What do you like best about the product?
Using DBT Cloud, the IDE is very intuitive, project lineage diagrams are very helpful.
The general use of Jinja referencing and CTE's within the models made the flows very easy to follow, even with very large complex datasets that require lots of transformation.
DBT integrates very easily with multiple ELT tools that we have used.
Have all transformations in SQL form just makes everything easier.
Being scheduled easily, we run multiple DBT pipelines daily.
The general use of Jinja referencing and CTE's within the models made the flows very easy to follow, even with very large complex datasets that require lots of transformation.
DBT integrates very easily with multiple ELT tools that we have used.
Have all transformations in SQL form just makes everything easier.
Being scheduled easily, we run multiple DBT pipelines daily.
What do you dislike about the product?
With DBT Cloud you can only have one project per user without paying for a payed tier of the product, which is fair but makes for harder collaboration at this level.
What problems is the product solving and how is that benefiting you?
Previous functions that were performed as adhoc scripts in python were made easy, running at a fraction of the time due to being rewritten in a significantly more efficient manner. Various functions across the business that require some sort of data transformation or manipulation, often previously manually were centralised all on one platform being DBT. Workflows and pipelines flowed more logically, and were scheduled and automated easily. Reports that are used daily by the business run quickly and very reliably. Tests and checks to validate data that was also previously done manually are all now integrated into the pipelines and automated, making multiple teams lives easier.
Dbt is all you need for your ETL processes.
What do you like best about the product?
DBT is an all in one tool. You dont have to leave the plaform to get the things done in the right way. The readibility and code structure is very nice.
What do you dislike about the product?
The documentation is not very extensive.
What problems is the product solving and how is that benefiting you?
It helps me to do the following things-
1. Visualise the lineage
2. Continous integration
3. Run tests and view documentation
4. Run job schedules with ease
5. Help transform and move data between different sources
1. Visualise the lineage
2. Continous integration
3. Run tests and view documentation
4. Run job schedules with ease
5. Help transform and move data between different sources
Using dbt at work
What do you like best about the product?
The best thing about dbt is how easy is for you to load and transform the data using some built in features. They listen to the community's problems and always updating by adding packages and new features in order to make your life easier.
What do you dislike about the product?
If there was any downside, dbt had already solved it by introducing new features and adapting to the problems that community have faced.
What problems is the product solving and how is that benefiting you?
Introducing clarity to the business world by showing them (in business terms) all the inormation that they beed about data
Dbt Cloud is exactly what a lean and mean team needs!
What do you like best about the product?
Pipeline execution management is easy!
- Task dependancies are easy to manege
- Excution logs are deatiled
- Alerting is easy
- Initial setup is super easy
Convenient dev enviroment
- The git integration enables to easily spin up development environemnts, check out a development branch and run you code in a production like environemnt
- Task dependancies are easy to manege
- Excution logs are deatiled
- Alerting is easy
- Initial setup is super easy
Convenient dev enviroment
- The git integration enables to easily spin up development environemnts, check out a development branch and run you code in a production like environemnt
What do you dislike about the product?
Would be nice to be able to set up several Snowflake connections for the same project.
What problems is the product solving and how is that benefiting you?
It reduces the hassle and the overhead around data pipeline automations. Makes it easy to keep a lean and mean team, yet have everything you need for a production data pipeline
Ease of working in a team
What do you like best about the product?
Transparency for everyone, with dbt the task of working on data as a team is made easier. We can apply different processes in our analysis thanks to the ease that dbt provides, application of tests, governance and observability. Even though it is not a tool specifically for this, it helps to apply dataops
What do you dislike about the product?
Learning time, despite being quick to learn, requires time to better understand its features to speed up the development process
What problems is the product solving and how is that benefiting you?
transparency over data transformation, managing to bring business people closer to the data
This is a versatile product that figures data-centric tasks by virtue of its robust features.
What do you like best about the product?
Having worked closely with data teams using dbt for the last year, I understand its benefits of a lightweight and version-friendly SQL work. This makes PG ideal for teams with multiple users because it simplifies code execution across different environments. Running functions in parallel leads to increased productivity, which is one of the most significant advantages. This is a versatile product that figures data-centric tasks by virtue of its robust features. The interface and usability of Dbt is commendable, making it very easy execute a complex data operation flow without any friction.
What do you dislike about the product?
On the flip side its advanced functionality can sometimes be a case of good things never used. It is quite difficult to leverage the maximum capabilities of this language, especially when you have a very limited number of examples of syntax which kind of makes it feel like there is some creativity limit on it preventing one from properly dwelling deep into exploring what its core functions are.
What problems is the product solving and how is that benefiting you?
Dbt has been a game changer in terms of how we process data. The benefit is that it eliminates manual repetition of tasks and controls process consistency in different stages. This has proven to be much more time-efficient as well as the data is simply more accurate – which directly supports improved project management goals.
DBT is a great tool that has saved our data team a lot of time.
What do you like best about the product?
Model creation and CI/CD are top features.
Thanks to DBT we have reduced the inconsistency of our data models and we reduced the number of manual tasks performed by the team.
Thanks to DBT we have reduced the inconsistency of our data models and we reduced the number of manual tasks performed by the team.
What do you dislike about the product?
The documenting process could be improved.
Since we started using DBT, the load on our data warehouse has skyrocketed. Tools that optimize queries would be greatly appreciated.
Since we started using DBT, the load on our data warehouse has skyrocketed. Tools that optimize queries would be greatly appreciated.
What problems is the product solving and how is that benefiting you?
We use DBT for data consolidation and transformation. DBT is a key piece of our data infrastructure.
Every piece of information gathered from the different sources using fivetran is processed using DBT: from marketing events to information about our car's performance (we a car leasing company)
Every piece of information gathered from the different sources using fivetran is processed using DBT: from marketing events to information about our car's performance (we a car leasing company)
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