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Resolving sql’s shortcomings with dbt
What do you like best about the product?
Being able to easily onboard 10+ analysts, has significantly increased our speed to produce data models. This has enabled us to unblock our data teams and make sure they are producing high-value work, which would not have been possible without dbt
dbt’s integration with jinja allows the implementation of software engineering practices into the data world, this helps resolve the limitations that exist in vanilla SQL. For example, keeping SQL DRY can be a challenge, with dbt jinja repeated code is easily written once and looped in a loop, or wrapped in a macro.
dbt cloud enables an accessible front end for new developers, and automatic updates are a nice feature for administration.
The lineage graph enables quick analysis of downstream impacts of changing a model, no more searching the repo for a table and crossed fingers at release time.
dbt’s integration with jinja allows the implementation of software engineering practices into the data world, this helps resolve the limitations that exist in vanilla SQL. For example, keeping SQL DRY can be a challenge, with dbt jinja repeated code is easily written once and looped in a loop, or wrapped in a macro.
dbt cloud enables an accessible front end for new developers, and automatic updates are a nice feature for administration.
The lineage graph enables quick analysis of downstream impacts of changing a model, no more searching the repo for a table and crossed fingers at release time.
What do you dislike about the product?
dbt cloud’s ide can be slow to open and has limitations that have been acknowledged by dbt.
What problems is the product solving and how is that benefiting you?
Brighte is on a mission to make every home sustainable. Brighte’s data team is supporting this mission by building an integrated data warehouse, supporting data to analysts distributed thought the business.
dbt cloud facilitates the enablement of data analysts to contribute to developing the data warehouse, increasing throughput of development, and freeing our centralised analytics engineers to work on complex problems and develop architecture vision.
dbt cloud facilitates the enablement of data analysts to contribute to developing the data warehouse, increasing throughput of development, and freeing our centralised analytics engineers to work on complex problems and develop architecture vision.
Recommendations to others considering the product:
The rapid onboarding was a good way to get our team of 10 developers up to speed quickly.
Additionally, we have implemented a policy of developers completing the dbt fundamentals course online before taking branches of our prod repo. The fundamentals videos are a very quick way to get developers who have never seen dbt before thinking in the dbt mindset.
Additionally, we have implemented a policy of developers completing the dbt fundamentals course online before taking branches of our prod repo. The fundamentals videos are a very quick way to get developers who have never seen dbt before thinking in the dbt mindset.
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DBT will change how you think about data
What do you like best about the product?
DBT will change how you develop analytics in a good way. With a background in software engineering developing analytics with DBT has started the revolution for analytical engineering. Testing, smaller components and built-in documentation.
What do you dislike about the product?
DBT can feel at times death by YAML files for configuring your setup. DBT cloud could be better if it allowed the execution of more outside python resources like FAL or re_data.
What problems is the product solving and how is that benefiting you?
we solve almost all of our data problems inside of DBT. data tests have allowed us to speed up our ability to understand problems and the documentation engine is pushing our team to think about this and how everything flows with data lineage.
great product, helpful team and community
What do you like best about the product?
dbt itself is a great tool - it's flexible and easy to use, and the learning curve isn't too steep (as it's largely SQL-based). It's easy to transform, test, and move data around, and because dbt sits between raw data and BI tools, it's possible to experiment, add business logic, and make changes to how data is transformed even retroactively.
Also, the team is very helpful (we engaged them as consultants to set up our data stack), the documentation is clear, and the slack community is a good resource.
Also, the team is very helpful (we engaged them as consultants to set up our data stack), the documentation is clear, and the slack community is a good resource.
What do you dislike about the product?
It's just one piece of a complete stack - you'll need a warehouse, a tool like Stitch or Fivetran to get data there, and a BI tool.
I mostly use dbt with a CLI, but the cloud product can be a bit slow.
I mostly use dbt with a CLI, but the cloud product can be a bit slow.
What problems is the product solving and how is that benefiting you?
It's a core part of our data stack that centralizes how data is transformed and how business logic is applied so that everyone at our company can see the same results. It organizes and joins data from disparate sources so it's easier to understand and use.
DBT is fantastic!
What do you like best about the product?
I like that DBT allows users to write complex SQL queries using Python-style coding blocks. It makes data transformations more fluid and achieveable.
What do you dislike about the product?
I have yet to find something that I dislike about DBT.
What problems is the product solving and how is that benefiting you?
We have messy data and inefficient SQL queries supporting some of our views. DBT has been able to streamline our data capabilities to increase our performance and clean up our data.
DBT is incredible and the people who work there are awesome!
What do you like best about the product?
DBT truly feels like it was created by analysts for analysts! It is intuitive, approachable, functional and honestly really fun to use. In the past, at another company, our analytics team was very dependent on Data Engineering to build our ETLs (primarily in Java) and aggregate tables even though we already had SQL created for the table we wanted to build. With DBT, you can remove the dependency on Engineering, move quickly, iterate, and learn more about analytics engineering as its own interesting functional area. We also worked with consultants at DBT labs for two 3-month engagements to refactor our growing repository and they were extremely professional, knowledgable, and fun! Would highly recommend it!
What do you dislike about the product?
It's difficult to think of things I dislike about DBT and it just keeps getting better and better as DBT continually listens and learns from its vibrant community. One thing I can think of is I wish it was possible to integrate DBT Docs directly into existing BI Tools.
What problems is the product solving and how is that benefiting you?
We use DBT for cleaning, transforming, testing, and modeling our source of truth datasets in our data warehouse. Our BI tools then leverage these datasets for important analysis and KPI tracking. We've also built important data frameworks within DBT to enable dynamic downstream ananlysis.
Recommendations to others considering the product:
Go for it! You won't regret!
DBT is the BEST (and most fun?) transformation tool. Mind blowing.
What do you like best about the product?
The whole paradigm of representing data models as SELECT statements is so approachable and functional. Starting out as a SQL novice, I was able to model complex database schemas into a common data standard. Documentation was good, and support was helpful. DBT Cloud is an ideal environment for learning DBT in real-time. Most of all, I love that everything is version-controlled and easy to roll back. DBT is one of the most innovative tools I've encountered period.
What do you dislike about the product?
Honestly, I can't think of anything significant. The online IDE has some bugs (as all do), but after the initial learning curve, we switched to VSC and GitHub, so problem solved. Documentation for Jinja _as it relates specifically to DBT_ was a little difficult to parse, and there were fewer articles about that online. If you already know Jinja this won't be an issue, but starting cold that was the steepest part of the learning curve by far.
What problems is the product solving and how is that benefiting you?
We use DBT for radical transformations of many source schemas into a common standard. We pair it with Fivetran to create an end-to-end data loading and transformation solution. It's been a quantum leap in scalability compared to our own, custom solutions as used previously.
Recommendations to others considering the product:
The paradigm of "every model is a SELECT statement" may not be the correct fit for every situation. Understanding how to use joins, sub-queries, and window functions is necessary for advanced transformations. DBT's own examples provide great reference asn best practices for most these concepts.
dbt is easy dynamic sql on steroids
What do you like best about the product?
easy to learn, very dynamic and powerful. dbt allows teams to work together using skills they already know (sql) along with a powerful templating language. this allows you do develop dynamic sql in a very easy, maintainable fashion. Using text files, it also allows you to easily see what's changed and who made those changes via github integration. It also eliminates the tedious ddl creation, and allows you to switch between using views and tables with a single configuration argument. It has powerful testing capabilities built in.
What do you dislike about the product?
compiling sometimes when not needed - it can slow down your development if you're saving a lot of files at once. Usually performance is not a problem but in some cases (not the typical workflow) it can be slow.
What problems is the product solving and how is that benefiting you?
solving data transformations. dbt lets you create dynamic sql very easily. you can onboard new teammates easily as everyone knows sql. transformations are stored in plain text in github so you can easily identify what's changed. easy to use - you only write 'select' statements - dbt handles all the ddl for you dynamically, keeping target environments separate. Integrated documentation and testing. packages (libraries) are available with great utilities.
Recommendations to others considering the product:
dbt is great for transformation - you still need another tool like FiveTran or Stitch to get the data into your warehouse. It's very powerful, and the dbt tools and support are great. Onboarding is quick as there are many developers who know sql. Being familiar with python is an additional bonus to becoming productive rapidly, but not required. One thing I really like is that dbt is open-source, so you can keep your business logic transformations even if you decide not to use dbt cloud. In most other etl tools, if they hike the rates you are stuck using their product or re-writing everything from scratch. dbt core offers a good alternative under those circumstances. However, the company provides good value for its services so I'm happy to stay with them.
With a prior GUI-based tool it was impossible to easily see what had changed, and merge with other developers. This is built for teamwork and accountability.
There are also out-of-the-box tools and libraries that can help you test and maintain data quality with no coding required.
The online dbt documentation is very good. In addition to regular support, they have slack channels and are very responsive.
The online training and personalized rapid onboarding training was excellent too.
Using dbt we were able to set up a Snowflake data warehouse and bring in 21 different sources, servicing hundreds of reports, in 8 months with a team of 2-3 data engineers and 3 BI engineers.
With a prior GUI-based tool it was impossible to easily see what had changed, and merge with other developers. This is built for teamwork and accountability.
There are also out-of-the-box tools and libraries that can help you test and maintain data quality with no coding required.
The online dbt documentation is very good. In addition to regular support, they have slack channels and are very responsive.
The online training and personalized rapid onboarding training was excellent too.
Using dbt we were able to set up a Snowflake data warehouse and bring in 21 different sources, servicing hundreds of reports, in 8 months with a team of 2-3 data engineers and 3 BI engineers.
dbt is THE way to manage your SQL transformations - version control and all kinds of other tools!
What do you like best about the product?
Dbt is a revelation for the analytics/BI engineering space. The seamless version control and 'don't repeat yourself' tools make your data and analytics pipelines WAY more reliable and efficient. Build data like a developer!
What do you dislike about the product?
There are still some things that dbt can handle better like managing ALL your SQL data warehouse functionality like UDFs and other non-model-building things. I have high hopes that dbt will 'fast follow' features like this though.
What problems is the product solving and how is that benefiting you?
I'm now adapting ALL my data transformations in my Fivetran -> Snowflake -> visualizations data pipelines using dbt. The productivity gains + better accuracy/less bugs introduced are HUGE. There's definitely some upfront work to learn dbt (not hard if you're already a SQL expert AND if you have a little git or coding familiarity), but man is it worth it!
Recommendations to others considering the product:
I actually don't know what the alternative to dbt is. I'm sure one must exist other than more 'roll your own' options like Apache Airflow, say, bu tin terms of super easy managed/cloud data transforms, dbt really does seem to be THE tool to use. It's $50/month per dev, BUT there's a FREE version for 1 dev seat with no read-only access for anyone else, so you can always start with that and then buy yourself a seat later.
Must-have tool for every analytics team
What do you like best about the product?
dbt formalizes software engineering best practices into our analytics workflow. Version control, data modeling, testing, documentation, etc. rolled into one toolkit that accommodates a big chunk of our data team's (and even a data engineer's) work. It's a life-changer, especially for diverse analytics teams whose members do not necessarily come from an engineering background.
What do you dislike about the product?
Nothing at all. The least favorable aspect I can think of is that the price for dbt Cloud is a bit high (per user), which may be prohibitive for larger teams who would like to test it out first without the need to measure ROI. Still, dbt-core is open-source and can be set up easily without purchasing subscriptions yet, so the price is not exactly a blocker.
What problems is the product solving and how is that benefiting you?
With dbt, we're able to clean and model our datasets flexibly. The main benefits are, our data engineers were able to focus on data integration, while our data analysts (who know our data better) were able to develop and maintain data models themselves. The flexibility in delegating the modeling work to our analysts has unlocked significant value, both in terms of data pipeline stability and the quality & usability of our downstream datasets.
Recommendations to others considering the product:
Just do it. Help your team be more productive.
Very powerful tool
What do you like best about the product?
DBT is critical to my organization and so far the tool has been very simple to use. My favorite feature is the interactive flowchart! absolutely game changing
What do you dislike about the product?
I think editing in DBT could get a little confusing with the different steps that are needed to update the code.
What problems is the product solving and how is that benefiting you?
We use DBT to clean and transform our data before it is synced into our BI tool
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