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Tool which provides simplified coding experience
What do you like best about the product?
I have been using dbt over more than a year now and I am using it almost every day as a data engineer. Its very simple to use and it is advantageous in many ways. It is easy to implement the code pipelines using dbt. The user interface is pretty good. Modularity and reusability is most useful feature. Also it is very useful in debugging with its code lineage feature. It is easy to integrate it with cloud data platforms such as Snowflake or Redshift. Also I have interacted with the support team from dbt for some of my doubts. I would recommend it to data architects to consider dbt in the modern data stack projects.
What do you dislike about the product?
It would be better if we have column level lineage available in dbt.
What problems is the product solving and how is that benefiting you?
dbt is solving the code lineage problem which is very difficult when we have a lots of SQL scripts. Also custom as well as user defined tests helps me in maintaining the data quality. This helps me in saving our teams precious time compared to time spent in solving defect or bug is raised by our reporting team.
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DBT
What do you like best about the product?
Cloud.dbt has proven to be an invaluable tool for our data modeling and analytics needs. Its seamless integration with cloud platforms, user-friendly interface, and robust features have significantly streamlined our workflow. The platform's automation capabilities have allowed our team to focus more on deriving insights from data rather than dealing with intricate modeling processes. The collaborative environment enhances team productivity, making it a standout solution for organizations aiming to elevate their data analytics game. I use it daily.
What do you dislike about the product?
Remember me during sign in doesn't work.
What problems is the product solving and how is that benefiting you?
Automation of jobs and syncing with database
Easy to use tool for building and managing data models!
What do you like best about the product?
Automatically created ERD diagrams and much more metadata about models built.
What do you dislike about the product?
Updating versions can be time consuming and cumbersome.
What problems is the product solving and how is that benefiting you?
It allows me to batch and process data querying elsewhere so users get to experience high performance datasets (queried on 100 million+ rows)
dbt makes it easier to onboarding analysts to good software engineering best practices
What do you like best about the product?
Its easy to use even for newcomers and dbt also uses sql that is the most democratic language for data manipulation. From the start is very quick to integrate with your data stack in just a few clicks. I use dbt for for data transformation and data modelling every day and its many features like data lineage, version control, ci and slim ci, testes, documentation, model contracts make it easy to apply DataOps in you project
What do you dislike about the product?
Its feature to model in python depends on the data plataform you are using. Its IDE still misses some of the functionalities that other IDE have that make development more easy, but new features are rolling out every new release of dbt cloud
What problems is the product solving and how is that benefiting you?
We use dbt to make a data migration from an old stack to the modern data stack and its helping us develiver quality in our data that we didnt have in the legacy pipelines
Awesome Experience with dbt Cloud App
What do you like best about the product?
The App is continuously upgrading. It's becoming more user-friendly and bettering the user experience. SQL writing becoming very quick.
What do you dislike about the product?
The customer support panel is always down. I have waited long many times for the help but it didn't arrive.
What problems is the product solving and how is that benefiting you?
I designed the ETL pipeline to process healthcare data and build BI Dashboards on top of it.
Data Trasformazione to to enable Analytics Engineering
What do you like best about the product?
Easy to write transformation in SQL language augmented by Jinja templating techniques. Support of Python models is powerful. Good library of Open source Macros makes life easy for Analytics engineers. Write test cases to test model results,. Powerful documentation capabilities. Works very well with Snowflake.
What do you dislike about the product?
To use DBT effectively, one needs to learn how to modularize SQL using CTEs. Bit advanced knowledge in SQl really helps.
What problems is the product solving and how is that benefiting you?
Historically Analytics and data engineering teams worked seperatly, where analytics teams created a business logic and the engineeres implemented them without much context of the business. This process was time consuming and data engineers were overwhelmed. DBT solved that for our use case. Using DBT analytysts can write the business logic using plain SQL (A must have skill for analysts) , then engineers just use the DBT project to scale, optimize and deplot to production, Its a huge effort and time saver and enabled quick go to market with data insights.
DBT is best data modelling tool
What do you like best about the product?
Cross Referencing of models, Macros and custom functions
What do you dislike about the product?
Error Messaging and no cross-query functionality
What problems is the product solving and how is that benefiting you?
We have built our organization's data stack using dbt and also we have connected dbt models to BQ which in turn feeds tableau dashboards
Best opensource data orchestration tool
What do you like best about the product?
- Easy to use and deploy for someone with SQL background
- Great community of support
- Easy to launch and maintain
- Can support data quality testing
- Great community of support
- Easy to launch and maintain
- Can support data quality testing
What do you dislike about the product?
- Complex transaofrmations which require python gets harder
What problems is the product solving and how is that benefiting you?
- Ability to run data quality tests at scale and minimal costs
- SQl tests easy to write and for ETL using SQL
- SQl tests easy to write and for ETL using SQL
An awesome tool for easy Data Transformations
What do you like best about the product?
One of the best things about dbt is that because it's an Sql-based platform, anyone ranging from a Data Analayst to a Data Engineer can easily implement and deploy Data Pipelines. It provides integrations with any different data sources like postgres, Snowflake, Bigquery etc along with features like CI/CD and version control.
What do you dislike about the product?
Currently dbt only focuses on the transformation aspect of a data pipeline. It can also focus on Data quality.
What problems is the product solving and how is that benefiting you?
dbt has enabled engineers to write a data pipeline in an SQL based format instead of writing huge codes using the same big data technologies, thus enabling anyone on the data team to setup and build their own pipelines. It also provides its own cloud platform where we can run those jobs and get data as per request.
One of the Best Data Transformation Tool
What do you like best about the product?
I am using DBT more than 1 year. Since the first day I started using it I like it's many things:
1) Simplicity: It is very simple. We just need to write SELECT quries to achive the whole design. So after the development,
2) Version Control: DBT used to integrate with version control tool like GIT. So we can easily track the changes of it.
3) Materialization: With the help of the materialized, we can configure our model. We can create views, tables etc using it.
4) Documentation: In DBT we can create yml files to describe each of the models, columns and their usages and all. It will create very good DBT docs.
5) Data Lineage Graph: DBT automatically creates data lineage graph. It helps a lot to analyze and debug code.
6) Schedule: DBT has its inbuild scheduler, so very easily we can schedule dbt jobs.
1) Simplicity: It is very simple. We just need to write SELECT quries to achive the whole design. So after the development,
2) Version Control: DBT used to integrate with version control tool like GIT. So we can easily track the changes of it.
3) Materialization: With the help of the materialized, we can configure our model. We can create views, tables etc using it.
4) Documentation: In DBT we can create yml files to describe each of the models, columns and their usages and all. It will create very good DBT docs.
5) Data Lineage Graph: DBT automatically creates data lineage graph. It helps a lot to analyze and debug code.
6) Schedule: DBT has its inbuild scheduler, so very easily we can schedule dbt jobs.
What do you dislike about the product?
dbt works in a batch mode. If we want to build a realtime job then it will not be possible in dbt. Although dbt is a data build tool. It do tranformation but it is not exactly a ETL tool where we can do data extraction, transformation and load.
What problems is the product solving and how is that benefiting you?
1) Simplicity: It is very simple. We just need to write SELECT quries to achive the whole design. So after the development,
2) Version Control: DBT used to integrate with version control tool like GIT. So we can easily track the changes of it.
3) Materialization: With the help of the materialized, we can configure our model. We can create views, tables etc using it.
4) Documentation: In DBT we can create yml files to describe each of the models, columns and their usages and all. It will create very good DBT docs.
5) Data Lineage Graph: DBT automatically creates data lineage graph. It helps a lot to analyze and debug code.
6) Schedule: DBT has its inbuild scheduler, so very easily we can schedule dbt jobs.
2) Version Control: DBT used to integrate with version control tool like GIT. So we can easily track the changes of it.
3) Materialization: With the help of the materialized, we can configure our model. We can create views, tables etc using it.
4) Documentation: In DBT we can create yml files to describe each of the models, columns and their usages and all. It will create very good DBT docs.
5) Data Lineage Graph: DBT automatically creates data lineage graph. It helps a lot to analyze and debug code.
6) Schedule: DBT has its inbuild scheduler, so very easily we can schedule dbt jobs.
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