dbt vs. IBM StreamSets

dbt (Data Build Tool) is best suited for doing the data transformation. dbt is just a transformation tool and it is not suitable for building a data pipeline which requires extraction of data and loading. dbt is well suited for SQL based transformation logic and it is less appropriate when transformation logic requires python.

Incentivized

Because real-world sources often change (new fields get added, formats get tweaked, etc.), StreamSets helps detect and adapt to those "schema drifts" or changes automatically, or with minimal manual intervention. That makes pipelines more resilient and significantly reduces the maintenance burden. Therefore, data sets with constantly changing sources/formats are great for StreamSets.

Incentivized

• user experience makes it easy to work with SQL and version control
• customer success team and the dbt (data build tool) community help establish best practices
• thorough and clear documentation

• it connects to many data sources and helps catch issues early with built-in alerts and monitoring tools
• it supports real-time and batch processing, handles data drift well, and makes pipeline debugging easier with the updated UI

Incentivized

• Slow load times of the dbt cloud environment (they're working on it via a new UI though)
• More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros

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

because i think that overall the solution is having a positive impact on the business, it allows multiple benefits in simplification of the tasks and is capable of doing multiple process that are usually done by a combination of man and systems, reducing the time and effort required to have the data.

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

Matillion is graphical versus dbt, which is SQL code-based (that, of course, is a matter of personal preference and not an objective advantage). The integrated testing, documentation generation, lineage, etc., were additional criteria that led us to choose dbt.

Incentivized

Before, we were using Informatica since most of our applications were running on on-prem servers. Later, when we started moving to the cloud, we tried Informatica Cloud, but it's more useful for batch-oriented than streaming. That's why one of our tech architects suggested IBM StreamSets for our real-time data streaming. During the POC stage, we were happy that the data streaming was way better with IBM StreamSets compared to the Informatica Cloud way of doing.

Incentivized

• In 3 months we re-wrote the data warehouse (15-20 sources) in dbt with 3 developers.
• We are using it continually for the past year with no issues.
• Sorry, I don't have ROI numbers but the impact was huge.

Incentivized

• Reduced manual handling, cutting down operational costs for our team.
• It also accelerated our time to Insight, which has eventually led to faster decision making.
• Data quality is improved.

Incentivized