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.

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IBM StreamSets excels in real-time logistics data ingestion and transformation across hybrid systems. It’s less ideal for lightweight ETL tasks or static datasets where simpler tools can achieve similar results with less overhead and complexity.

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• 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.

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• It helps streaming huge data that we have in our Teradata database to various reporting applications that runs on cloud seamlessly.
• We also use IBM StreamSets to power few BI dashboards that our product managers use on regular basis to showcase various data with clients.
• I think the data quality is way better compared to Informatica tool.

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• 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)

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• The error messages I feel aren t always very descriptive so troubleshooting can take longer
• Maybe more customisation options for scheduling can be done, rest it works pretty well.

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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.

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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.

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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.

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First advantage is that this software is particularly new and it keeps updating according to the needs of the user. Other advantage is the it organises and produces conclusions on the basis of data without leaving any relevant information. Other softwares lack in data summarising and readability of the charts and sheets they produce.

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

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• time saving for automatic collection and integration of data
• time saving thanks to live monitoring and reaction
• time saving for standardization of data

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