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DBT: Streamlining Data Transformations with Suave and Panache
What do you like best about the product?
Dbt has revolutionized the way we handle data transformations in our organization. It's incredibly easy to apply and use, even for non-technical team members. With its intuitive command-line interface and well-documented features, we were up and running in no time.
One of the standout features of dbt is its ability to implement software best practices to our SQL codebase. It promotes modularization, version control, and testing, allowing us to treat our data transformations as a software project. This has significantly improved our code quality, collaboration, and overall data reliability. Top it with great, constantly growing, supportive community and a number of integrations with data observability and cataloging tools, third-party modules and libraries.
Dbt is simply a great product. Its support for various data warehouses gives us the flexibility to work with our preferred platform, and the performance optimizations, such as incremental processing, have saved us valuable time and resources.
I consider dbt a must-have for every data tech stack. It has streamlined our data pipeline development and maintenance, ensuring consistency and accuracy throughout our analytics models. Whether you're a data analyst, engineer, or scientist, dbt empowers you to transform raw data into valuable insights with ease.
In fact, i was headhunted and hired for my current role, primarily because of my dbt knowledge.
Dbt has exceeded my expectations on all fronts since day one and keeps doing it as it develops, it has become a number one tool in my professional armamentarium. Its simplicity, adherence to software best practices, and overall functionality make it a standout choice for anyone working with data.
One of the standout features of dbt is its ability to implement software best practices to our SQL codebase. It promotes modularization, version control, and testing, allowing us to treat our data transformations as a software project. This has significantly improved our code quality, collaboration, and overall data reliability. Top it with great, constantly growing, supportive community and a number of integrations with data observability and cataloging tools, third-party modules and libraries.
Dbt is simply a great product. Its support for various data warehouses gives us the flexibility to work with our preferred platform, and the performance optimizations, such as incremental processing, have saved us valuable time and resources.
I consider dbt a must-have for every data tech stack. It has streamlined our data pipeline development and maintenance, ensuring consistency and accuracy throughout our analytics models. Whether you're a data analyst, engineer, or scientist, dbt empowers you to transform raw data into valuable insights with ease.
In fact, i was headhunted and hired for my current role, primarily because of my dbt knowledge.
Dbt has exceeded my expectations on all fronts since day one and keeps doing it as it develops, it has become a number one tool in my professional armamentarium. Its simplicity, adherence to software best practices, and overall functionality make it a standout choice for anyone working with data.
What do you dislike about the product?
Nothing, that is truly a great tool, created by a great team.
What problems is the product solving and how is that benefiting you?
1. Data reliability:
Dbt helps ensure the accuracy and consistency of transformed data.
It allows to define and enforce tests, perform data validation, and catch errors early in the pipeline.
2. Increased productivity:
With dbt, i can work more efficiently by leveraging its modularization (macros and jinja templating), codebase management which leads to faster iterations and shorter development cycles.
3. Foster collaboration: dbt encourages collaboration among data practitioners by providing version control support.
This enables seamless collaboration, change tracking, and simplifies the process of reviewing, merging, and rolling back changes.
4.Optimize performance: dbt incorporates performance optimization such as incremental processing.
Dbt allows me to work smarter and faster, build scalable pipelines, create high quality code that is easy to maintain.
Dbt helps ensure the accuracy and consistency of transformed data.
It allows to define and enforce tests, perform data validation, and catch errors early in the pipeline.
2. Increased productivity:
With dbt, i can work more efficiently by leveraging its modularization (macros and jinja templating), codebase management which leads to faster iterations and shorter development cycles.
3. Foster collaboration: dbt encourages collaboration among data practitioners by providing version control support.
This enables seamless collaboration, change tracking, and simplifies the process of reviewing, merging, and rolling back changes.
4.Optimize performance: dbt incorporates performance optimization such as incremental processing.
Dbt allows me to work smarter and faster, build scalable pipelines, create high quality code that is easy to maintain.
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dbt infrastructure has helped us build a data warehouse with a firm foundation
What do you like best about the product?
I love it's self sustainability and the ease of implementation and documentation
What do you dislike about the product?
upgrading dbt versions is sometimes a hassle
What problems is the product solving and how is that benefiting you?
dbt is making it possible to very specifically organize our data and make it very easily accessible
DBT, the unified codegen of downstream model heterogeneous complexity
What do you like best about the product?
DBT creates a new SQL-like syntax, use select statement and configuration file and headers to control behavior, make data model task more reliable and readable.
Easy for data analysis with existing basic SQL knowledge.
Easy for data analysis with existing basic SQL knowledge.
What do you dislike about the product?
We can rarely go through all features in dbt, since it is too much
Solutions are sometime duplicated. You can always find 2-4 ways to fulfill one purpose, and it can overwrite with each other.
DBT integration with CI/CD is not easy, even in DBT cloud since file is the first citizen in dbt world. You can only have them tested in dbt project, even for only unit tests.
Solutions are sometime duplicated. You can always find 2-4 ways to fulfill one purpose, and it can overwrite with each other.
DBT integration with CI/CD is not easy, even in DBT cloud since file is the first citizen in dbt world. You can only have them tested in dbt project, even for only unit tests.
What problems is the product solving and how is that benefiting you?
data modeling and materialization. One of a customer is migrating their data warehouse to bigQuery. After platform and data migration, they think adopting new data modeling tool on new platform, DBT fit that gap and then is selected as standard
DBT the Design Tool for the Data Engineers
What do you like best about the product?
Ease with which it integrates with Python and Postgres Database.
What do you dislike about the product?
No native connector for Spark Datawarehouse. There is connector to other warehouses like Snowflake/BigQuery/Redshift
What problems is the product solving and how is that benefiting you?
Data Modeling and Data handling are the 2 key areas that DBT helps to automate. DBT makes it a breeze to move the data between schema and stages programmatically
data scientist
What do you like best about the product?
Easy to set up a test on unique row_id and add descriptions of tables or columns.
The "Format" function is super helpful. It makes coding clear to read.
The "Format" function is super helpful. It makes coding clear to read.
What do you dislike about the product?
So far, I don't find anything I dislike. just a suggestion that adding --full-refresh on quick bottoms (ex: build, run)
What problems is the product solving and how is that benefiting you?
testing unique. running time.
dbt metadata works well with Monte Carlo
What do you like best about the product?
It allows me to add metadata upstream that can be read by downstream applications. I only have to change the metadata in one place.
What do you dislike about the product?
The way we have set up our models, there are many, many different versions of the data through the pipeline. It can be hard to figure out which one is the end user model and therefore needs to have the metadata.
What problems is the product solving and how is that benefiting you?
allows more of the BI team to be involved with the data pipeline
Good product to transform raw data and pipeline it to analytics and business needs
What do you like best about the product?
What I use the most is the documentation feature. It is very robust and complete, which helps me understand where the features I use daily come from and how they were built.
What do you dislike about the product?
I'd say there is a learning curve to start using it which could be smoother. Some basic guidelines on the basics would help a lot.
What problems is the product solving and how is that benefiting you?
Understand how features from the tables are built
I <3 dbt
What do you like best about the product?
The core product has completely changed the way I think about and build data pipelines. It's hard for me to imagine the analytics world without dbt.
What do you dislike about the product?
The metrics layer still need to do some maturing.
What problems is the product solving and how is that benefiting you?
dbt helps you convert you raw data into data tables that humans can understand and use.
An Absolute Powerhouse
What do you like best about the product?
There's a lot to like! Especially the ability to: connect dbt with GitHub for version control and documentation, add testing, work on multiple branches at one time, and visualize and manage the relationships between models. We have created documentation that is pulled from the schema files to our BI tool, which is super helpful. Being able to install packages and utilize macros is incredibly powerful as well. I'm also excited about the recent addition of Python - we plan to use it this year. dbt is also surprisingly easy to use; I started using it in my first data job and loved it immediately!
What do you dislike about the product?
Honestly, there's nothing that I dislike. Jinja has been the most challenging part since it requires thinking differently, but that's just a learning curve and nothing against dbt itself.
What problems is the product solving and how is that benefiting you?
My use case has been breaking down data silos and building data models that generate a combined view. With dbt, we've automated reports that saved teams a ton of time and gave them a unified view of metrics they've never had before! We've also been able to set up customized tests that alert us to problems within the business/our own software.
Makes data transformations easy to maintain
What do you like best about the product?
Maintaining data transformations are easy with SQL- and git-based workflow. Integrates nicely with Big Query.
What do you dislike about the product?
IDE is not perfect but improving. Git support lacks some basic functionalities.
What problems is the product solving and how is that benefiting you?
Maintaining our data pipelines is easier with dbt than other tools or techiques I've tried.
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