dbt Platform
dbt LabsExternal reviews
External reviews are not included in the AWS star rating for the product.
Centralized data transformations have improved workflows but integrations still need expansion
What is our primary use case?
What is most valuable?
It is very convenient because at the end, I have the opportunity to orchestrate all my transformations in just one single place, rather than having them spread out. I can use SQL, which is very convenient for the sizes of data that I usually use in the day-to-day. Of course, some other deployments might require Spark, and in this case, it is not a good idea to use SQL plus dbt, but for most cases, it is very convenient. Because it is easy to set up, and also due to the cost, I have all the transformations in one single place with a very convenient tool.
It is very convenient; I can set up the expectations really easily, all integrated in the tool that I usually use for my data transformation, so it is very convenient.
What needs improvement?
With AI, everything is advancing so fast, so I would say that the most important thing is to try to integrate with more platforms. As of now, dbt has a strong integration with AWS and with Snowflake, but I have not seen other integrations. Having more partners and having more visibility on the things that can be done is important, because I see that competitors are doing great in that aspect. For example, Databricks and Snowflake itself are doing that, so more visibility, more partnerships, and more integrations would be helpful.
For how long have I used the solution?
I have been using dbt for five years as of now.
How are customer service and support?
I may not have enough information to respond about the technical support of dbt because I have not reached out to dbt support at any time, probably because I have never needed it. In the three or four organizations that I have been with using dbt, I have not contacted the support service directly, so it may be a good sign.
How was the initial setup?
I will say the deployment for dbt is very straightforward; once you have the experience, it is quite straightforward. For example, you can set up a Docker or Docker Compose and run it, or you can use directly the on-cloud version.
I would say that the deployment for dbt requires about a day; a day could work, and with a day, you can set up and deploy dbt.
What about the implementation team?
I find that with one person, it is enough to complete the deployment; of course, it can vary depending on the complexity of the project, but I would say that one person working one day is enough.
What's my experience with pricing, setup cost, and licensing?
I mentioned the cost as one of the advantages, specifically the license cost.
Which other solutions did I evaluate?
I think the pricing is very convenient; one of the barriers is that for example, some of the companies that I have been with, dbt is a normal solution or neutral in terms of cost. It is not cheap or more expensive, but the problem is that companies are really locked in with existing vendors, and those vendors offer alternatives that might be less expensive given that they have other products. dbt only offers data transformation services, making it hard to compete with vendors that have all the packages included, such as cloud and processing services. If you compare the cost of those packages with dbt alone, it is more expensive to use dbt alone.
What other advice do I have?
I am using a private cloud. I have used both on-prem and cloud versions of the product, but mainly the managed version, the on-cloud version. That is very convenient; of course with AI, that is being commoditized a little bit. But I like it; I used it more before. Now with AI, it is even easier to do documentation, but before AI, it was really convenient to generate documentation with that tool. My overall review rating for dbt is 7 out of 10.
Streamlined Data engineering and built-in lineages
What is our primary use case?
dbt is used for data transformation and data engineering with multiple data transformations and engineering functions. It is also used for orchestrating data engineering pipelines. An example of this is ingesting data from Azure Blob or S3 sources and then transforming it into different layers in the data platform.
What is most valuable?
The best features of dbt include lineage and Jinja templating languages that make it easy for creating pipelines.
The built-in lineage feature provides a good understanding of the several layers where data is being loaded in dbt, allowing visibility from different layers into the end product.
dbt has positively impacted version controlling as it has different version control steps involved. The specific improvements seen with version control in dbt are that it has helped trace the data lineage, enabled faster trace and rollbacks, and enabled safe collaboration at every scale, which has improved data quality.
A return on investment has been seen from using dbt as the time has reduced while utilizing dbt in the form of data pipelines and ETL scripting. There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors.
What needs improvement?
dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section as it is not stable.
The copilot in dbt is not very comfortable for users, and my team has already tried using it but opted to move off from the dbt copilot to other copilots such as GitHub.
Improvement is needed in the tool itself in terms of the copilot, in terms of covering outages, in terms of testing, and in terms of quality reasons related to governance and collaboration.
For how long have I used the solution?
dbt has been used for about a year.
What do I think about the stability of the solution?
dbt is not as stable as preferred, as it has had a few outages in the current year itself, so improvement should be made in the outages section. Overall, dbt is stable.
What do I think about the scalability of the solution?
In terms of scalability, dbt has improved the scalability of the organization depending on different dimensions for team size, data, and complexity of transformations.
How are customer service and support?
The customer support from dbt was good and was identified and resolved by the customer support team when reached out to.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
Initially, multiple solutions such as Talend Studio and Informatica were utilized for different projects before switching to dbt.
How was the initial setup?
The experience with pricing, setup cost, and licensing was that it was straightforward for the pricing setup and also on the licensing part for dbt.
What was our ROI?
A return on investment has been seen from using dbt as the time has reduced while utilizing dbt in the form of data pipelines and ETL scripting. There is operational efficiency achieved, and data quality and governance have also been achieved with modular SQL and version controlling, which reduced duplication of data and data errors.
What's my experience with pricing, setup cost, and licensing?
dbt was purchased through the AWS Marketplace.
Which other solutions did I evaluate?
Before choosing dbt, other options were evaluated, but dbt was the preferred choice as it was an open-source solution that was already on the track.
What other advice do I have?
My advice to others looking into using dbt is that it is a good tool for having ETL or ELT transformations done. To begin with, a pilot project can be added with modular SQL or modeling, Git workflows, and a standardized project structure from source, staging, intermediate, to the mart layers, which will optimize performance. I would rate this solution a seven out of ten.
Which deployment model are you using for this solution?
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Efficient Data Management with Room for Documentation Improvement
DBT review
ELT made easy with just SQL
Support tickets are resolved quickly from support team.
I can create any model with just SQL and orchestration made easy with creating job. Tags feature with lineage made my job easy by running sequence of models.
Lineage is helpful in visualising data flow. Lint feature helps with code formatting
Developer-friendly and easy to use, but doesn't have many optimization options
What is our primary use case?
I use the solution for transformation. When we perform the ELT process, we need to transform the data according to the business requirements. We can also use the tool for testing.
What is most valuable?
The product is developer-friendly. A person who understands SQL can develop the transformation. We do not have to learn a lot of things like we do for new tools. The tool has good testing and data quality features. Implementing Slowly Changing Dimensions through dbt has been easy. It is very easy for a beginner to use the product.
The tool provides multiple technical advantages if we use Snowflake. It is a good transformation tool because it is SQL-oriented. It has data lineage, data quality, and workflow scheduler.
What needs improvement?
The solution must add more Python-based implementations. Transformation tools require Python-based implementations. It would give developers more freedom to use SQL or Python. We can use Python, but it is not that user-friendly. The product doesn't have a lot of optimization options.
For how long have I used the solution?
I have been using the solution for almost three years.
What do I think about the stability of the solution?
There are no problems with the product’s stability.
What do I think about the scalability of the solution?
We have at least 25 to 50 users in our organization.
How was the initial setup?
The solution is deployed on the cloud. It can be deployed on AWS, Azure, or GCP. The initial setup is easy.
What's my experience with pricing, setup cost, and licensing?
The solution’s pricing is affordable.
What other advice do I have?
We also use stored procedures and Talend. They are not replaced by dbt completely. We use dbt only for the transformation process. My recommendations would depend on an organization’s requirements and problems. I will recommend the tool to others. The product is developer-friendly. However, it is still dependent on the data warehouse for big data and optimization.
It's just a SQL transformation tool. It doesn't have a lot of optimization options like Spark. It's a good tool for Snowflake. If it were only for Snowflake, I would have rated it an eight out of ten. However, there are other data platforms.
Overall, I rate the tool a six and a half out of ten.