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dbt for data pipelines
What do you like best about the product?
dbt is excellent due to its wide use of macros and the ability to transform data using SQL, an analyst-friendly language. Given this we can train analysts to read dbt and understand the logic behind our data transformations, limiting the amount of work we need to do explaining data decisions or documenting logic.
What do you dislike about the product?
I wish that dbt was more integrated with other data tools. It seems that a lot of data tools (fivetran, monte carlo, hightouch) are designed with dbt in mind but dbt never seems designed for these tools. It would be nice to have more accessibility within dbt, allowing us to create alerting and etl processes easier.
What problems is the product solving and how is that benefiting you?
Dbt allows us to transform data using SQL and then run this data daily to provide fresh data to analysts. With this data we are able to make business decisions ultimately impacting our bottom line. We also use dbt for minor tests to make sure our data is accurate and clean.
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Data Scientist
What do you like best about the product?
DBT is an easy to use too for anyone who knows SQL. Their IDE is wonderful and you can easily spin it up in no time. As a Data Scientist, doing modelling in DBT saves me hours of work and helps me provide an opportunity to others to focus on a more self serve analytics
What do you dislike about the product?
DBT is a great tool but there are a few things missing from it. Direct connection to postgres SQL. Mixing of different sources and more convinent ways of build test and macros.
What problems is the product solving and how is that benefiting you?
Dbt helps us build models for a lot of different complex queries that get used. DBT helps us compile massive queries into tables or views and helps with the flow.
Best in class ELT
What do you like best about the product?
Best in-class SQLCentric tool providing ELT, orchestration with Lineage on the models.
make the development much easier and helps to concentrate more on the Business
make the development much easier and helps to concentrate more on the Business
What do you dislike about the product?
DBT only support sql and python models for now and being a ELT external sources reading will not be possible which make DBT to be so constrained and libraries are much limited.
What problems is the product solving and how is that benefiting you?
A powerful tool in providing powerful business solutions. We are using Databricks on DBT which supports delta formats and Macro is the best option to reduce repeated code.
Overall great data transformation tool/framework
What do you like best about the product?
It brings best software development best practices to a world that didn't have them natively several years ago.
It helps speed up the delivery of data transformation models and consumable data.
It provides more than a data transformation framework, tests and documentation are two very welcome features to it.
It helps speed up the delivery of data transformation models and consumable data.
It provides more than a data transformation framework, tests and documentation are two very welcome features to it.
What do you dislike about the product?
Some people may have a hard time getting to know the framework; for this, the courses on the dbt website are a great introduction.
For people coming from a traditional drag & drop (no code) tool, the change of mindset is even more challenging. There are no training materials for addressing the "this is how you did it with a traditional tool" and "this is how you do it with dbt", so these have to be created internally by each data team.
For people coming from a traditional drag & drop (no code) tool, the change of mindset is even more challenging. There are no training materials for addressing the "this is how you did it with a traditional tool" and "this is how you do it with dbt", so these have to be created internally by each data team.
What problems is the product solving and how is that benefiting you?
Time to deliver, data quality checks prior to making data available to users (data quality issues are detected by dbt and not by data consumers).
Data documentation directly on dbt and propagated to our data catalog.
Reusability of data models and less redundant code.
Data documentation directly on dbt and propagated to our data catalog.
Reusability of data models and less redundant code.
DBT Cloud is dreadful, do not use.
What do you like best about the product?
Open source DBT is a fantastic tool that makes building robust processing jobs utilising best practice straightforward.
What do you dislike about the product?
DBT cloud is not ready for production, do not use it.
All of our scheduled jobs had been running fine for months, DBT released a change within their infrastructure which meant these jobs could no longer connect to our warehouse due to a missing sasl library.
This took close to a week to resolve (despite the exact issue being highlighted to their support team) which meant a week of downtime. Zero compensation was offered.
All of our scheduled jobs had been running fine for months, DBT released a change within their infrastructure which meant these jobs could no longer connect to our warehouse due to a missing sasl library.
This took close to a week to resolve (despite the exact issue being highlighted to their support team) which meant a week of downtime. Zero compensation was offered.
What problems is the product solving and how is that benefiting you?
Building analytical datasets.
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.
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.
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