dbt Reviews

its capabilities to put features of development pipelines into the data pipelines. The interface is fairly easy to use. Its scalability and performance, as it can handle very big workloads by pushing it into the DW. It works wonderfully integrated with Snowflake. Pricing is predictable.

It requires very high skills to use effectively. Should have much more AI capabilities.

Cleaning and preparing the data for all other processes

I like the amount of variety that dbt offers because of the Jinja code and the inbuilt functions. The incremental models are very well built-in, offering lots of capabilities at a layer beyond what's in the data warehouse, like Redshift. The initial setup of dbt is very straightforward, which I find really helpful.

I guess the development cycle of dbt is slower as a result. Writing YAML file descriptions and the actual code for every single model can lead to a slower development cycle.

dbt allows us to handle data with software engineering best practices, letting us write tests, store code in a git repository, and manage it like a software project.

I find dbt very simple to use and get started with. Anyone with some SQL and YAML files can use it. It solves many problems related to data transformations, data quality, and data lineage. I like dbt Cloud for scheduling jobs, which is crucial for me, and I appreciate that I can schedule jobs in various environments based on my use case and get alerts too. I also enjoy the new AI feature, dbt-copilot, which helps with development, understanding code better, and writing dbt macros, which can be challenging. The initial setup was easy as it mostly required SQL expertise, and the training sessions were helpful. Overall, I love this tool.

I like most use cases with dbt, but sometimes I have to write the entire code just to compile it. It can improve in terms of showing errors early before compile time. For example, if I am selecting from a model and a column is extra, dbt could flag it before compile and run actions.

I use dbt for data transformations, quality testing, and lineage on top of warehouses. It simplifies starting up as it just needs SQL and YAML files. With dbt Cloud, I can schedule jobs and get alerts. The dbt-copilot aids development, particularly with writing dbt macros.

dbt simplifies the process of building a solid data pipeline by offering a lot of features that would be difficult to implement from scratch. In particular, the SCD2 and incremental functionality helps remove a lot of overhead for developers and makes ongoing maintenance easier. There are also many other features that are great and contribute to a smoother overall workflow.

There’s nothing I dislike about it, but I do have one suggestion:adding a feature for backfilling data (historical loads) will help a lot. Right now this can be done using a macro, but having an inbuilt option similar to incremental would make it much easier and help a lot.

DBT is like a framework for data engineering. Building ETL using a traditional approach is very time-consuming and error-prone things like dependency issues, documentation, and testing all require extra precautions and a lot of manual effort.

I have been using DBT every day in my project for the last two years, and I can say it is one of the best platforms for creating data models through SQL-based data transformations. DBT’s ease of integration has helped us save effort during code deployment. We integrated DBT with Git so we can work in parallel on multiple tasks across multiple models for a single requirement.

Its ease of implementation also makes it simpler to understand DBT’s benefits and has helped improve our efficiency. The command-line interface and the IDE environment make it easier for us to manage data and execute data models. DBT also has its own test case preparation features, which help us validate both our data model changes and the data being populated through DBT models. If we run into any product-related issues, there is a customer support team that is always ready to guide us through our blockers.

In my experience, I worked with DBT for 2–3 years and noticed that, to use DBT effectively, you need prior knowledge of SQL and Python—especially if you want to take advantage of additional features like Jinja templates. This can be a major drawback for data analysts and developers who don’t already have a solid understanding of SQL and Python.

speed of building models, performance, cloud based

a steep learning curve for non-technical users, high resource consumption with large datasets, and lack of built-in scheduling.

dbt (data build tool) solves the critical problem of inefficient, siloed, and unverified data transformation by allowing data analysts and engineers to transform data within their warehouse using

SQL Transformation is the best thing which dbt have.

Nothing as of now. but pricing seems high for model run

ETL and data engineering portion.

The way it handles large amounts of data, as well as how it integrates into AWS (S3/Glue) is great. This allows me to avoid building custom pipelines which would have been very time consuming and caused additional headaches and due to its columnar database design, all of my complex query requests are processed in a timely manner which means I do not fall asleep while waiting for results.

Vacuuming Tables… seriously, I have to manually vacuum and analyze tables to keep this thing running smoothly? It looks like 2005. Managing the clusters and nodes is also a pain – it’s not true serverless. If you’re not paying close attention to the costs, they will jump up way too high.

This has allowed us to move away from a pandas-based reporting solution, which crashed consistently. We now can process billions of records from our retail business and have a working dashboard. Its architecture provides separate storage and compute, allowing us to scale our compute resources as much as needed based on the demand for reports by management. Most importantly, it has reduced the amount of yelling from our data team regarding slow query performance.

DBT is a data building tool that is very easy to setup, to use and we are using it every day for our data transformation. It is very easy to integrate and leverage the tool with lots for features.

Sometimes, it experiences server downtime.

DBT is solving where our business analyst has data spread out in many different tables in our warehouse. Using DBT, I made datamart where I gathered all that information together so our BAs can get all the information they need from one single table.

The largest benefit of dbt to me is that it provides structure to data work. I use it regularly with the BigQuery and version control tools. The integration is comfortable and teamwork is facilitated. It did not add any delay during implementation and the feature set enables one to reuse logic rather than rewriting it. It has minimized the number of errors and saved me time on the review and updates.

The negative side about dbt is that it becomes rigid when projects expand. Minor modifications in some cases need more readjustments than anticipated, and this makes me slow down. The problems of debugging failures are not always evident, particularly to more novice team members and this has an impact on the speed of delivery. Clean source data is also used in implementation and hence when inputs are messy, it only adds more workload rather than making it easy.

Before using dbt, our changes were far between and difficult to handle. At this point, all things go in the same way, which is advantageous to the entire team. The coordination between systems was eliminated through integration and implementation provided a sense of ownership. I can perceive fewer errors, more harmonious work, and a higher level of trust in products. It has made daily work less stressful and less value building oriented.