dbt Platform
dbt LabsExternal reviews
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Great experience with dbt
What do you like best about the product?
dbt is the best Transformation tool out there in the industry and I love dbt for its testing capabilities and modeling and semantic layer. Ease of use and how easily you could maintain
It is easy to integrate with other tools like integration.
It is easy to integrate with other tools like integration.
What do you dislike about the product?
dbt should add more AI apacilities faster
What problems is the product solving and how is that benefiting you?
dbt is solving all the data integration, integrity and data quality problems for our company while serving as a great transfomration tool
DBT - easy transformation tool
What do you like best about the product?
Can perform transformation using SQL statement. Very easy to perform
What do you dislike about the product?
Nothing much......simple and easy to use
What problems is the product solving and how is that benefiting you?
ELT tool which helps to perform transformation using SQL and create pipelines.
Excellent tool for data transformation
What do you like best about the product?
It has helped us transform our data and structure it better and its easy use
What do you dislike about the product?
It should provide a tool to better enable model documentation.
What problems is the product solving and how is that benefiting you?
We can have the data structured through code; This helps us when migrating data to any type of DWH.
Making data transformations easier
What do you like best about the product?
Its simplicity and focus on transforming data in a reproducible and maintainable way.
What do you dislike about the product?
Sometimes,its learing curve can be steep for beginners and managing comples transformation might require advanced knowledge.
What problems is the product solving and how is that benefiting you?
dbt simplifies data transformation making analytics pipelines easier to manage and more reliable,which benefifits users by stramling workflows and improving efficiency.
A great environment and a powerful daily tool for Data Analysts and Engineers.
What do you like best about the product?
dbt Cloud - I recommend it to every org to get Data Analysts & Analytics engineers up and running quickly without having difficulty setting up during the onboarding.
It's easier to adopt new teammates when they get to dive into the models immediately and add value sooner and solidify their grasp early.
It's easier to adopt new teammates when they get to dive into the models immediately and add value sooner and solidify their grasp early.
What do you dislike about the product?
I dislike navigating the logs in the Job Runs tab.
The titles don't seem intuitive and the content could be more streamlined for finding faults.
The titles don't seem intuitive and the content could be more streamlined for finding faults.
What problems is the product solving and how is that benefiting you?
Easy onboarding - streamlined development - the guided point-and-click adventure for github saves a ton of time and is probably the best in class solution I have seen for managing state. Please dont ever change this.
dbt data modeling and test building is a fun experience on dbt cloud, my day to day work is fun because of dbt.
Testing is super easy for pro-active data quality checks.
I wish there was more visible ways to incorporate REACTIVE testing, like Metaplane's monitors, into dbt.
DBT support was a bit slow here in Africa when the Github outage took place last year - some frustration around how slow responses were, how unclear processing was but I have personally learnt how to navigate these issues outside of dbt env.
dbt data modeling and test building is a fun experience on dbt cloud, my day to day work is fun because of dbt.
Testing is super easy for pro-active data quality checks.
I wish there was more visible ways to incorporate REACTIVE testing, like Metaplane's monitors, into dbt.
DBT support was a bit slow here in Africa when the Github outage took place last year - some frustration around how slow responses were, how unclear processing was but I have personally learnt how to navigate these issues outside of dbt env.
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
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