In the current Data-Centric AI paradigm, where all businesses seek to leverage the power of their data for any competitive advantage they can get, organizations face a critical choice: to buy or build their solutions.
The landscape of synthetic data generation is no exception. The need to break data silos to accelerate development and data sharing while addressing data privacy concerns and compliance requirements has driven the demand for synthetic data solutions, with several open-source and proprietary solutions being continuously developed.
But when should your organization consider adopting a proprietary synthetic data solution over building one in-house or relying on using open-source alternatives?
In this article, we will discuss the differences between Fabric and the Synthetic Data Vault (SDV), a popular open-source solution among data practitioners, and discover how their functionalities compare across several components.
Organizations need to handle multiple sources of data with distinct characteristics, data complexity, categories cardinality, data size, and the presence of complex relationships. Finding a solution that can cope with distinct types of data and deliver optimal synthesization models is often a brain teaser:
For organizations, having the ability to set business rules and constraints, as well as ensuring data privacy, is paramount.
Comparing the synthetic data against the real data is a fundamental step in assessing the quality of the generated data.
Scalability, Hardware Requirements, and Performance
Scale and performance are perhaps the factors that propel organizations to move towards proprietary software since open-source solutions simply can’t cope with the requirements of real-world data:
Additionally, while SDV provides only a Python SDK, Fabric enables multiple interfaces (a GUI, an API, and an SDK), and provides several integrations and ecosystems, supporting the most common cloud vendors, data engineering platforms, and popular data science IDEs.
Throughout this article, we discussed the similarities and differences between Fabric and SDV, highlighting the limitations that open-source solutions face when handling real-world scenarios where scalability, optimization, usability, and business-centric perspectives are non-negotiable.
If you’re looking to fully unlock your assets for data sharing or secure development initiatives for your organization, open-source solutions will not be able to cope with your specific business needs, data flow’s complexity, and scale.
Open-source solutions are great for exploring new technology’s benefits and limitations but might be more limited for production systems where reliability is part of the requirements. Fabric’s interfaces (GUI and code) enable different profiles within an organization to leverage the benefits of synthetic data, from data stewards and quality assurance engineers all the way to data analysts and data scientists.
In case you’re still on the fence, check it for yourself. You may find a complete benchmark comparing Fabric with SDV across several datasets, read up on a point-by-point comparison between Fabric and SDV across the components mentioned above.
When you’re ready to take your AI endeavors to the next level, take a look at Fabric and sign up for the community version, or contact us for trial access to the full platform.
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