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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.
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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.
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.
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.
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