dbt vs. IBM StreamSets

The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.

Incentivized

When you are dealing with a data warehouse and want to find an easy way to integrate applications and expose data in real-time, then IBM StreamSets is the best tool to go for. I'm using it for the same purpose in my applications. This tool will be well-suited for someone with a proper technical background. Though IBM StreamSets UI is mostly drag and drop, advanced configurations require technical expertise or support to do the initial setup.

Incentivized

• dbt supports version control through GIT, this allows teams to collaborate and track the data transformation logic.
• dbt allows us to build data models which helps to break complex transformation logic into simple and smaller logic.
• dbt is completely based on SQL which allows data analyst and data engineers to build the transformation logic.
• dbt can be easily integrated with snowflake.

Incentivized

• It makes building data pipelines quite super intuitive even for non coders.
• Ir also handles real time data ingestion effortlessly so I always have up to date information for my reports.
• It's great at monitoring data quality as well.

Incentivized

• Field-level lineage (currently at table level)
• Documentation inheritance - if a field is documented the downstream field of the same name could inherit the doc info
• Adding python model support (in beta now)

Incentivized

• Where the person's skillsets in data analysis is not of an expert.
• Data monitoring and analysis.
• Customer data for better customer acquisition

Incentivized

IBM Stream sets has been a wonderful addition to our technology stack. It has helped in some of our initiatives such as data engineering, data integration for not only external customers but also for internal purposes. The tool has also helped on our use cases related to streaming data. Moving to another tool would require significant amount of work and time.

Incentivized

dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.

Incentivized

The StreamSets platform is very easy to use and the interface is extremely intuitive. The drag-and-drop, low-code design makes it accessible for teams with varying technical skills, allowing us to quickly connect sources, define transformations, and deploy pipelines without heavy coding. StreamSets allows us to get started quickly and not have to worry about our pipelines breaking once they're built.

Incentivized

Streamsets support has improved a lot in the last couple of years. We had some challenges in the beginning with support, but now the quality of the support and the responsiveness to tickets are better. We have contacted support multiple times when it came to scenarios where the system was slow or the output as not as we expected

Incentivized

I was not involved in the implementation

Incentivized

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.

Incentivized

the IBM solution can be considered a good player in the specific perimeter of application because its main functionalities are working well, are easy to use, and complete. it allows also a good degree of freedom when it comes to personalization of pipelines and streams, and customization of data ingestion methodologies.

Incentivized

• Simplified our BI layer for faster load times
• Increased the quality of data reaching our end users
• Makes complex transformations manageable

Incentivized

• it reduced the time I spent managing and updating pipelines when data formats changed
• it saved us from building everything from scratch by making data movement between systems easier
• it helps scale workflows as data volume grows, without much extra effort

Incentivized