Dataiku vs. dbt

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Dataiku
Score 8.2 out of 10
N/A
The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.N/A
dbt
Score 9.0 out of 10
N/A
dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt Core is distributed under the Apache 2.0 license, and paid Teams and Enterprise editions are available.
$0
per month per seat
Pricing
Dataikudbt
Editions & Modules
Discover
Contact sales team
Business
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Enterprise
Contact sales team
No answers on this topic
Offerings
Pricing Offerings
Dataikudbt
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Dataikudbt
Features
Dataikudbt
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku
8.6
5 Ratings
3% above category average
dbt
-
Ratings
Connect to Multiple Data Sources8.05 Ratings00 Ratings
Extend Existing Data Sources10.04 Ratings00 Ratings
Automatic Data Format Detection10.05 Ratings00 Ratings
MDM Integration6.52 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku
10.0
5 Ratings
18% above category average
dbt
-
Ratings
Visualization10.05 Ratings00 Ratings
Interactive Data Analysis10.05 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku
9.5
5 Ratings
16% above category average
dbt
-
Ratings
Interactive Data Cleaning and Enrichment9.05 Ratings00 Ratings
Data Transformations9.05 Ratings00 Ratings
Data Encryption10.04 Ratings00 Ratings
Built-in Processors10.04 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku
8.5
5 Ratings
1% above category average
dbt
-
Ratings
Multiple Model Development Languages and Tools8.05 Ratings00 Ratings
Automated Machine Learning8.05 Ratings00 Ratings
Single platform for multiple model development8.05 Ratings00 Ratings
Self-Service Model Delivery10.04 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku
8.0
5 Ratings
6% below category average
dbt
-
Ratings
Flexible Model Publishing Options8.05 Ratings00 Ratings
Security, Governance, and Cost Controls8.05 Ratings00 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Dataiku
-
Ratings
dbt
9.7
8 Ratings
19% above category average
Simple transformations00 Ratings10.08 Ratings
Complex transformations00 Ratings9.48 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Dataiku
-
Ratings
dbt
9.1
8 Ratings
15% above category average
Data model creation00 Ratings9.78 Ratings
Metadata management00 Ratings8.78 Ratings
Business rules and workflow00 Ratings9.08 Ratings
Collaboration00 Ratings10.06 Ratings
Testing and debugging00 Ratings8.08 Ratings
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User Ratings
Dataikudbt
Likelihood to Recommend
10.0
(4 ratings)
10.0
(10 ratings)
Usability
10.0
(1 ratings)
9.7
(3 ratings)
Support Rating
9.4
(3 ratings)
-
(0 ratings)
User Testimonials
Dataikudbt
Likelihood to Recommend
Dataiku
Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.
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dbt Labs
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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Pros
Dataiku
  • Allows users to collaborate and monitor individual tasks
  • Caters to both types of analysts, coders and non-coders, alike
  • Integrate graphs and plots with visualization tools such as Tableau
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dbt Labs
  • 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.
Read full review
Cons
Dataiku
  • The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
  • When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
  • Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.
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dbt Labs
  • 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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Usability
Dataiku
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
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dbt Labs
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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Support Rating
Dataiku
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
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dbt Labs
No answers on this topic
Alternatives Considered
Dataiku
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.
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dbt Labs
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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Return on Investment
Dataiku
  • Customer satisfaction
  • Timely project delivery
Read full review
dbt Labs
  • 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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ScreenShots